{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# [cknowledge.org/ai](http://cknowledge.org/ai): Crowdsourcing benchmarking and optimisation of AI"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# [PUBLIC] Benchmarking Caffe and TensorRT on NVIDIA Jetson TX1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**NB:** The Caffe results are released with approval from General Motors. The TensorRT 1.0 EA results are released with approval from NVIDIA."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Table of Contents"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. [Overview](#overview)\n",
    "1. [See the code](#code) [for developers]\n",
    "1. [Get the data](#data) [for developers]\n",
    "1. [See the tables](#tables)\n",
    "  1. [All data](#df_all)\n",
    "  1. [All execution time data](#df_time)\n",
    "  1. [Mean execution time per batch](#df_mean_time_per_batch)\n",
    "  1. [Mean execution time per image](#df_mean_time_per_image)\n",
    "  1. [Best mean execution time per image](#df_best_mean_time_per_image)\n",
    "1. [See the graphs - grouped by models](#plot_models)\n",
    "  1. [All libs](#plot_models_all)\n",
    "  1. [GPU libs](#plot_models_gpu)\n",
    "  1. [CUDA-level performance libs](#plot_models_cuda)\n",
    "  1. [cuBLAS libs](#plot_models_cublas)\n",
    "  1. [cuDNN libs](#plot_models_cudnn)\n",
    "  1. [TensorRT engine](#plot_models_tensorrt)\n",
    "1. [See the graphs - grouped by libs](#plot_libs)\n",
    "  1. [All models](#plot_libs_all)\n",
    "  1. [All models, GPU libs](#plot_libs_gpu)\n",
    "  1. [Models with AlexNet-level accuracy](#plot_libs_alexnet)\n",
    "  1. [Models with AlexNet-level accuracy, CPU lib](#plot_libs_alexnet_cpu) \n",
    "  1. [Models with AlexNet-level accuracy, CUDA-level perfomance libs](#plot_libs_alexnet_cuda)\n",
    "  1. [Models with AlexNet-level accuracy, fp16 libs](#plot_libs_alexnet_fp16)\n",
    "  1. [GoogleNet, fp16 libs](#plot_libs_googlenet_fp16)\n",
    "1. [See the graphs - per layer execution time profiling](#plot_per_layer)\n",
    "1. [See the graphs - the ideal adaptive solution](#plot_ideal)\n",
    "  1. [Using all libs for adaptation](#plot_ideal_all)\n",
    "  1. [Using CUDA-level performance libs for adaptation](#plot_ideal_cuda)\n",
    "  1. [Using cuDNN and cuBLAS for adaptation](#plot_ideal_cudnn_cublas)\n",
    "  1. [Using cuDNN and libDNN for adaptation](#plot_ideal_cudnn_libdnn)\n",
    "1. [See the memory consumption graph](#plot_memory)\n",
    "  1. [Balance memory consumption and execution time per image](#balance_memory_time)\n",
    "1. [Compare AlexNet and SqueezeNet 1.1](#alexnet_vs_squeezenet)\n",
    "  1. [Compare memory consumption](#alexnet_vs_squeezenet_memory)\n",
    "  1. [Compare execution time](#alexnet_vs_squeezenet_time)\n",
    "1. [Conclusion](#conclusion)\n",
    "  1. [What are the improvements brought on by each approach?](#improvements_of_each_approach)\n",
    "  1. [How do dividiti's results compare with NVIDIA's results in the whitepaper?](#compare_with_whitepaper)\n",
    "  1. [How does TX1 compare to Myriad2?](#tx1_vs_myriad2)\n",
    "1. [Crowdsourcing benchmarking and optimisation of AI](#cknowledge_ai)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"overview\"></a>\n",
    "## Overview"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This Jupyter Notebook compares the performance (execution time, memory consumption):\n",
    "- on **[dividiti](http://dividiti.com)**'s Jetson TX1 board ([official page](http://www.nvidia.com/object/jetson-tx1-dev-kit.html), [Phoronix review](http://www.phoronix.com/scan.php?page=article&item=nvidia-jtx1-perf)):\n",
    "  - CPU:\n",
    "    - ARM&reg; Cortex&reg;-A57 architecture (\"big\");\n",
    "    - 4 cores;\n",
    "    - Max clock 1743 MHz;\n",
    "  - GPU:\n",
    "    - Maxwell&trade; architecture;\n",
    "    - 256 CUDA cores;\n",
    "    - Max clock 998 MHz;\n",
    "  - RAM:\n",
    "    - LPDDR4;\n",
    "    - 4 GB (shared between the CPU and the GPU);\n",
    "    - Max bandwidth 25.6 GB/s;\n",
    "  - [Linux for Tegra 24.2.1](http://developer.download.nvidia.com/embedded/L4T/r24_Release_v2.1/Docs/Tegra_Linux_Driver_Package_Release_Notes_R24.2.1.pdf);\n",
    "  - [JetPack 2.3.1](http://docs.nvidia.com/jetpack-l4t/index.html#developertools/desktop/jetpack/l4t/2.3.1);\n",
    "  - CUDA Toolkit 8.0.33.\n",
    "```\n",
    "$ uname -a\n",
    "Linux tegra-ubuntu 3.10.96-tegra #1 SMP PREEMPT Wed Nov 9 19:42:57 PST 2016 aarch64 aarch64 aarch64 GNU/Linux\n",
    "$ cat /etc/lsb-release\n",
    "DISTRIB_ID=Ubuntu\n",
    "DISTRIB_RELEASE=16.04\n",
    "DISTRIB_CODENAME=xenial\n",
    "DISTRIB_DESCRIPTION=\"Ubuntu 16.04.1 LTS\"\n",
    "```\n",
    "\n",
    "- using 6 Caffe **libraries**:\n",
    "  - [`tag`] **Branch** (**revision hash, date**): **math libraries**.\n",
    "  - [`cpu`] Master ([24d2f67](https://github.com/BVLC/caffe/commit/24d2f67173db3344141dce24b1008efffbfe1c7d), 28/Nov/2016): with [OpenBLAS 0.2.19](https://github.com/xianyi/OpenBLAS/releases/tag/v0.2.19);\n",
    "  - [`nvidia-cuda`] NVIDIA 0.15 ([1024d34](https://github.com/NVIDIA/caffe/commit/1024d34d93cd34a9013d6fac4e56e45162073d38), 17/Nov/2016): with [cuBLAS](https://developer.nvidia.com/cublas) (part of CUDA Toolkit 8.0.33);\n",
    "  - [`nvidia-cudnn`] NVIDIA 0.15 ([1024d34](https://github.com/NVIDIA/caffe/commit/1024d34d93cd34a9013d6fac4e56e45162073d38), 17/Nov/2016): with [cuDNN 5.1](https://developer.nvidia.com/cudnn);\n",
    "  - [`nvidia-fp16-cuda`] NVIDIA experimental/fp16 ([fca1cf4](https://github.com/NVIDIA/caffe/commit/fca1cf475d1d0a6d355f8b9877abcc4e13951c9c), 11/Jul/2016): with [cuBLAS](https://developer.nvidia.com/cublas) (part of CUDA Toolkit 8.0.33);\n",
    "  - [`nvidia-fp16-cudnn`] NVIDIA experimental/fp16 ([fca1cf4](https://github.com/NVIDIA/caffe/commit/fca1cf475d1d0a6d355f8b9877abcc4e13951c9c), 11/Jul/2016): with [cuDNN 5.1](https://developer.nvidia.com/cudnn);\n",
    "  - [`libdnn-cuda`] OpenCL ([b735c2d](https://github.com/BVLC/caffe/commit/b735c2dd4103ac963332b400168507dd7cefd204), 23/Nov/2016): with [libDNN](https://github.com/BVLC/caffe/issues/4155) and [cuBLAS](https://developer.nvidia.com/cublas) (part of CUDA Toolkit 8.0.33) for fully connected layers;\n",
    "  <br>\n",
    "  **NB:** libDNN is not yet tuned for TX1 - it uses parameters that are optimal for GTX 1080.\n",
    "\n",
    "- using 2 configurations of the [NVIDIA TensorRT 1.0.0 EA engine](https://developer.nvidia.com/tensorrt):\n",
    "  - [`tensorrt-fp16`] NVIDIA TensorRT 1.0.0 EA with fp16 enabled;\n",
    "  - [`tensorrt-fp32`] NVIDIA TensorRT 1.0.0 EA with fp16 disabled;\n",
    "  <br>\n",
    "  **NB:** This EA (\"early access\") version is used in accordance with special licensing terms: the results are released with explicit written approval from NVIDIA. The results may not be representative of the GA (\"general availability\") version.\n",
    "\n",
    "- using 4 CNN **models**:\n",
    "  - [GoogleNet](https://github.com/BVLC/caffe/tree/master/models/bvlc_googlenet);\n",
    "  - [AlexNet](https://github.com/BVLC/caffe/tree/master/models/bvlc_alexnet);\n",
    "  - [SqueezeNet 1.0](https://github.com/DeepScale/SqueezeNet/tree/master/SqueezeNet_v1.0);\n",
    "  - [SqueezeNet 1.1](https://github.com/DeepScale/SqueezeNet/tree/master/SqueezeNet_v1.1);\n",
    "\n",
    "- with the **batch size** varying from 2 to 16 with step 2."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Configure the execution time metric to use"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "fw = [ 'forward' ]\n",
    "fwbw = [ 'forward', 'backward' ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['forward']"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Set to fw for inference; to fwbw for training.\n",
    "direction = fw\n",
    "direction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'time_fw_ms'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "if direction==fw:\n",
    "    time_ms = 'time_fw_ms'\n",
    "else: # direction==fwbw\n",
    "    time_ms = 'time_fwbw_ms'\n",
    "time_ms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def images_per_second(time_in_milliseconds):\n",
    "    return 1000.0 / time_in_milliseconds"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"code\"></a>\n",
    "## Data wrangling code"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**NB:** Please ignore this section if you are not interested in re-running or modifying this notebook."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Includes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Standard"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import sys\n",
    "import json\n",
    "import re"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Scientific"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If some of the scientific packages are missing, please install them using:\n",
    "```\n",
    "# pip install jupyter pandas numpy matplotlib\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import IPython as ip\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib as mp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "IPython version: 4.1.1\n",
      "Pandas version: 0.19.1\n",
      "NumPy version: 1.11.2\n",
      "Matplotlib version: 1.5.3\n"
     ]
    }
   ],
   "source": [
    "print ('IPython version: %s' % ip.__version__)\n",
    "print ('Pandas version: %s' % pd.__version__)\n",
    "print ('NumPy version: %s' % np.__version__)\n",
    "print ('Matplotlib version: %s' % mp.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from IPython.display import Image\n",
    "from IPython.core.display import HTML"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import cm\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "default_title = 'NVIDIA Jetson TX1'\n",
    "default_ylabel = 'Execution time (ms)'\n",
    "default_colormap = cm.autumn\n",
    "default_fontsize = 16\n",
    "default_figsize = [16, 8]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Collective Knowledge"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If CK is not installed, please install it using:\n",
    "```\n",
    "# pip install ck\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CK version: 1.8.7.1\n"
     ]
    }
   ],
   "source": [
    "import ck.kernel as ck\n",
    "print ('CK version: %s' % ck.__version__)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Access the experimental data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "repo_uoa = 'ck-caffe-nvidia-tx1'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def get_experimental_results(repo_uoa, tags):\n",
    "    module_uoa = 'experiment'\n",
    "    r = ck.access({'action':'search', 'repo_uoa':repo_uoa, 'module_uoa':module_uoa, 'tags':tags})\n",
    "    if r['return']>0:\n",
    "        print (\"Error: %s\" % r['error'])\n",
    "        exit(1)\n",
    "    experiments = r['lst']\n",
    "    \n",
    "    dfs = []\n",
    "    for experiment in experiments:\n",
    "        data_uoa = experiment['data_uoa']\n",
    "        r = ck.access({'action':'list_points', 'repo_uoa':repo_uoa, 'module_uoa':module_uoa, 'data_uoa':data_uoa})\n",
    "        if r['return']>0:\n",
    "            print (\"Error: %s\" % r['error'])\n",
    "            exit(1)\n",
    "\n",
    "        # Get (lib_tag, model_tag) from a list of tags that should be available in r['dict']['tags'].\n",
    "        # Tags include 2 of the 3 irrelevant tags, a model tag and a lib tag.\n",
    "        # NB: Since it's easier to list all model tags than all lib tags, the latter list is not expicitly specified.\n",
    "        tags = r['dict']['tags']\n",
    "        irrelevant_tags = [ 'explore-batch-size-libs-models','time_gpu','time_cpu','time_gpu_fp16' ]\n",
    "        model_tags = [ 'bvlc-alexnet','bvlc-googlenet','deepscale-squeezenet-1.0','deepscale-squeezenet-1.1' ]\n",
    "        lib_model_tags = [ tag for tag in tags if tag not in irrelevant_tags ]\n",
    "        model_tags = [ tag for tag in lib_model_tags if tag in model_tags ]\n",
    "        lib_tags = [ tag for tag in lib_model_tags if tag not in model_tags ]\n",
    "        if len(lib_tags)==1 and len(model_tags)==1:\n",
    "            (lib, model) = (lib_tags[0], model_tags[0])\n",
    "        else:\n",
    "            continue\n",
    "        \n",
    "        for point in r['points']:\n",
    "            with open(os.path.join(r['path'], 'ckp-%s.0001.json' % point)) as point_file:\n",
    "                point_data_raw = json.load(point_file)\n",
    "                # Obtain column data.\n",
    "                characteristics = [\n",
    "                    {\n",
    "                        'time (ms)'   : characteristics['run'].get(time_ms,+1e9), # \"positive infinity\"\n",
    "                        'memory (MB)' : characteristics['run'].get('memory_mbytes',-1),\n",
    "                        'success?'    : characteristics['run'].get('run_success','n/a'),\n",
    "                        'per layer info' : characteristics['run'].get('per_layer_info',[])\n",
    "                    }\n",
    "                    for characteristics in point_data_raw['characteristics_list'] \n",
    "                ]\n",
    "                # Deal with missing column data (resulting from failed runs).\n",
    "                if len(characteristics)==1:\n",
    "                    repetitions = point_data_raw['features'].get('statistical_repetitions',1)\n",
    "                    characteristics = characteristics * repetitions\n",
    "                # Construct a DataFrame.\n",
    "                df = pd.DataFrame(characteristics)\n",
    "                # Set columns and index names.\n",
    "                df.columns.name = 'run characteristic'\n",
    "                df.index.name = 'repetition'\n",
    "                # Set indices.\n",
    "                if lib=='tensorrt-1.0.0':\n",
    "                    enable_fp16 = (point_data_raw['choices']['env']['CK_TENSORRT_ENABLE_FP16'] != 0)\n",
    "                    df['lib'] = 'tensorrt-fp%d' % (16 if enable_fp16 else 32)\n",
    "                else:\n",
    "                    df['lib'] = lib\n",
    "                df['model'] = model\n",
    "                df['batch size'] = point_data_raw['choices']['env']['CK_CAFFE_BATCH_SIZE']\n",
    "                df = df.set_index(['lib', 'model', 'batch size'], append=True)\n",
    "                df = df.reorder_levels(('model', 'lib', 'batch size', 'repetition'))\n",
    "                # Append to the list of similarly constructed DataFrames.\n",
    "                dfs.append(df)\n",
    "    # Concatenate all constructed DataFrames (i.e. stack on top of each other).\n",
    "    result = pd.concat(dfs)\n",
    "    return result.sortlevel(result.index.names)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot execution time per image or memory consumption"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def plot(mean, std, title=default_title, ylabel=default_ylabel, rot=0, ymax=0):\n",
    "    ymax = mean.max().max() if ymax==0 else ymax\n",
    "    ax = mean.plot(kind='bar', yerr=std, grid=True, legend=True, rot=rot, ylim=[0,ymax*1.05],\n",
    "                   fontsize=default_fontsize, figsize=default_figsize, colormap=default_colormap)\n",
    "    ax.set_title(title, fontsize=default_fontsize)\n",
    "    ax.set_xlabel(mean.index.name, fontsize=default_fontsize)\n",
    "    ax.set_ylabel(ylabel, fontsize=default_fontsize)\n",
    "    return ax"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot maximum number of images per second"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pretty_print_libs = {\n",
    "    'cpu': 'OpenBLAS (CPU)',\n",
    "    'libdnn-cuda':'libDNN-fp32',\n",
    "    'nvidia-cuda':'cuBLAS-fp32',\n",
    "    'nvidia-fp16-cuda':'cuBLAS-fp16',\n",
    "    'nvidia-cudnn':'cuDNN-fp32',\n",
    "    'nvidia-fp16-cudnn':'cuDNN-fp16',\n",
    "    'tensorrt-fp32':'TensorRT-fp32',\n",
    "    'tensorrt-fp16':'TensorRT-fp16'\n",
    "}\n",
    "\n",
    "pretty_print_models = {\n",
    "    'bvlc-alexnet':'AlexNet',\n",
    "    'bvlc-googlenet':'GoogleNet',\n",
    "    'deepscale-squeezenet-1.0':'SqueezeNet 1.0',\n",
    "    'deepscale-squeezenet-1.1':'SqueezeNet 1.1'\n",
    "}\n",
    "\n",
    "speedup_sort_models = [\n",
    "    'OpenBLAS (CPU)',\n",
    "    'libDNN-fp32',\n",
    "    'cuBLAS-fp32',\n",
    "    'cuBLAS-fp16',\n",
    "    'cuDNN-fp32',\n",
    "    'cuDNN-fp16',\n",
    "    'TensorRT-fp32',\n",
    "    'TensorRT-fp16'\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# ['cuda', 'cudnn'] are roughly equivalent to ['nvidia-cuda', 'nvidia-cudnn'], so can be dropped.\n",
    "def plot_max_num_images_per_second(df_mean_time_per_image, libs_to_drop=['cuda', 'cudnn'], rot=0, fontsize=None):\n",
    "    min_time_per_image = df_mean_time_per_image.min(axis=1).unstack('lib')\n",
    "    max_num_images_per_second = images_per_second(min_time_per_image) \\\n",
    "        .drop(libs_to_drop, axis=1) \\\n",
    "        .rename(columns=pretty_print_libs, index=pretty_print_models) \\\n",
    "        .reindex(columns=speedup_sort_models)\n",
    "    ax = max_num_images_per_second \\\n",
    "        .plot(kind='bar', rot=rot, width=0.95, grid=True, legend=True,\n",
    "              fontsize=default_fontsize, figsize=default_figsize, colormap=default_colormap)\n",
    "    ax.set_title(default_title, fontsize=default_fontsize)\n",
    "    ax.set_xlabel(max_num_images_per_second.index.name, fontsize=default_fontsize)\n",
    "    ax.set_ylabel('Images/s (with the best even batch size between 2 and 16)', fontsize=default_fontsize)\n",
    "    for patch in ax.patches:\n",
    "         ax.annotate(str(int(patch.get_height()+0.5)), (patch.get_x()*1.00, patch.get_height()*1.01), fontsize=fontsize)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot the speedup over a given baseline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# ['cuda', 'cudnn'] are roughly equivalent to ['nvidia-cuda', 'nvidia-cudnn'], so can be dropped.\n",
    "def plot_speedup_over_baseline(df_mean_time_per_image, baseline='cpu', libs_to_drop=['cuda', 'cudnn'], rot=0, fontsize=None):\n",
    "    speedup_over_baseline = df_mean_time_per_image.min(axis=1).unstack('model').ix[baseline] / \\\n",
    "                            df_mean_time_per_image.min(axis=1).unstack('model')\n",
    "    speedup_over_baseline = speedup_over_baseline.T \\\n",
    "        .drop(libs_to_drop, axis=1) \\\n",
    "        .rename(index=pretty_print_models, columns=pretty_print_libs) \\\n",
    "        .reindex(columns=speedup_sort_models)\n",
    "    ax = speedup_over_baseline \\\n",
    "        .plot(kind='bar', rot=rot, width=0.95, grid=True, legend=True,\n",
    "              fontsize=default_fontsize, figsize=default_figsize, colormap=default_colormap)\n",
    "    ax.set_title(default_title, fontsize=default_fontsize)\n",
    "    ax.set_xlabel(speedup_over_baseline.index.name, fontsize=default_fontsize)\n",
    "    ax.set_ylabel('Speedup over the given baseline (%s)' % pretty_print_libs[baseline], fontsize=default_fontsize)\n",
    "    for patch in ax.patches:\n",
    "        ax.annotate('{0:.2f}'.format(patch.get_height())[0:4], (patch.get_x()*1.00, patch.get_height()*1.01),\n",
    "                        fontsize=fontsize)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot execution time per image per layer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# This transformation is time consuming, hence only call it once for multiple plots.\n",
    "def get_per_layer_info(df_all):\n",
    "    df_per_layer_info = df_all['per layer info']\n",
    "    row_dfs = []\n",
    "    for (row_info, row_id) in zip(df_per_layer_info, range(len(df_per_layer_info))):\n",
    "        # Skip constructing a DataFrame when no layer info is available.\n",
    "        if not row_info: continue\n",
    "        # Augment each layer info with the row index: (model, lib, batch size, repetition).\n",
    "        for layer_info in row_info:\n",
    "            layer_info.update({ k : v for k, v in zip(df_per_layer_info.index.names, df_per_layer_info.index[row_id]) })\n",
    "        # Construct a DataFrame and move the row index to where it belongs.\n",
    "        row_df = pd.DataFrame(data=row_info).set_index(df_per_layer_info.index.names)\n",
    "        row_dfs.append(row_df)\n",
    "    return pd.concat(row_dfs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def plot_time_per_image_per_layer(df_per_layer_info, model, libs, batch_sizes,\n",
    "                                  direction=['forward'], lower=0.0, upper=1.0, ymax=0, rot=90):\n",
    "    df_time_per_batch = df_per_layer_info.loc[model, libs, batch_sizes] \\\n",
    "        .set_index(['direction', 'label'], append=True) \\\n",
    "        .reorder_levels(['direction', 'label', 'model', 'lib', 'batch size', 'repetition' ]) \\\n",
    "        .ix[direction] \\\n",
    "        .reorder_levels(['label', 'model', 'lib', 'batch size', 'repetition', 'direction' ]) \\\n",
    "        .groupby(level=['label', 'model', 'lib', 'batch size', 'repetition']).sum() \\\n",
    "        ['time_ms']\n",
    "    df_time_per_image = df_time_per_batch.unstack('batch size') / batch_sizes\n",
    "    df = df_time_per_image.unstack(['lib', 'model'])\n",
    "    df = df.reorder_levels(['model', 'lib', 'batch size'], axis=1)\n",
    "    mean = df.groupby(level='label').mean()\n",
    "    std = df.groupby(level='label').std()\n",
    "    select = (lower*mean.sum() <= mean).any(axis=1) & (mean <= upper*mean.sum()).any(axis=1)\n",
    "    ymax = mean[select].max().max() if ymax==0 else ymax\n",
    "    ax = plot(mean=mean[select], std=std[select], ylabel='Execution per image time per layer (ms)', ymax=ymax, rot=rot)\n",
    "    ax.set_xlabel('Layer', fontsize=default_fontsize)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot the ideal adaptive solution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# The ideal adaptive solution for each layer selects the best performing library from the 'libs_for_adaptation' list.\n",
    "# FIXME: add batch_sizes as explicit parameter.\n",
    "def get_ideal_adaptive_solution(df_per_layer_info, libs_for_adaptation, direction):\n",
    "    df_for_adaptation = df_per_layer_info \\\n",
    "        .set_index(['direction', 'label'], append=True) \\\n",
    "        .reorder_levels(['direction', 'lib', 'model', 'label', 'batch size', 'repetition']) \\\n",
    "        .ix[direction] \\\n",
    "        .reorder_levels(['lib', 'model', 'label', 'batch size', 'repetition', 'direction']) \\\n",
    "        .ix[libs_for_adaptation] \\\n",
    "        .reorder_levels(['model', 'label', 'lib', 'batch size', 'repetition', 'direction']) \\\n",
    "        ['time_ms']\n",
    "    # With every step, reduce the rightmost dimension until the min time per model is reached.\n",
    "    df_cum_time_per_repetition = df_for_adaptation.groupby(level=df_for_adaptation.index.names[:-1]).sum()\n",
    "    df_min_time_per_repetition = df_cum_time_per_repetition.groupby(level=df_cum_time_per_repetition.index.names[:-1]).min()\n",
    "    df_min_time_per_batch = df_min_time_per_repetition.unstack('batch size') / batch_sizes\n",
    "    df_min_time_per_image = df_min_time_per_batch.min(axis=1)\n",
    "    df_min_time_per_layer = df_min_time_per_image.groupby(level=df_min_time_per_image.index.names[:-1]).min()\n",
    "    #df_min_time_per_model = df_min_time_per_layer.groupby(level=df_min_time_per_layer.index.names[:-1]).sum()\n",
    "    # Transform to get the models in the index and the libs in the columns.\n",
    "    df_min_time_per_layer_idx = df_min_time_per_image.groupby(level=df_min_time_per_image.index.names[:-1]).idxmin()\n",
    "    df_ideal = df_min_time_per_image[df_min_time_per_layer_idx] \\\n",
    "        .reorder_levels(['model', 'lib', 'label']) \\\n",
    "        .groupby(level=['model', 'lib']).sum() \\\n",
    "        .unstack('lib')\n",
    "    # Sort in the order of increasing time per model.\n",
    "    df_ideal_sorted = df_ideal.ix[df_ideal.sum(axis=1).sort_values(ascending=True).index]\n",
    "    return df_ideal_sorted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def plot_ideal_adaptive_solution(df_ideal, df_real, tag=\"\"):\n",
    "    figsize=[15, 3]\n",
    "    if not tag==\"\": figsize=[10, 2] # good for dumping png (e.g. 3 graphs fit well onto a slide).\n",
    "    for model in df_ideal.index:\n",
    "        df_data = {}; df_data['adaptive'] = df_ideal.ix[model]\n",
    "        for lib in df_ideal.columns:\n",
    "            df_data[lib] = pd.Series(index=df_ideal.columns)\n",
    "            df_data[lib][lib] = df_real.ix[model, lib]\n",
    "        df = pd.DataFrame(df_data).T \\\n",
    "            .rename(index={'cpu': 'OpenBLAS only', 'nvidia-cuda':'cuBLAS only', 'nvidia-cudnn':'cuDNN only',\n",
    "                           'libdnn-cuda': 'libDNN only'},\n",
    "                    columns={'cpu': 'OpenBLAS', 'nvidia-cuda':'cuBLAS', 'nvidia-cudnn':'cuDNN', 'libdnn-cuda': 'libDNN'})\n",
    "        ax = df.ix[df.sum(axis=1).sort_values(ascending=True).index] \\\n",
    "            .plot(kind='barh', stacked=True, width=0.9, grid=True, legend=True,\n",
    "                  fontsize=default_fontsize, figsize=figsize, colormap=cm.summer_r)\n",
    "            #.legend(loc='lower right')\n",
    "        ax.set_title('%s - execution time per image (ms)' % model, fontsize=default_fontsize)\n",
    "        if not tag==\"\": ax.get_figure().savefig('%s.%s.png' % (tag, model))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot execution time per image and memory consumption"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def plot_time_per_image_and_memory_consumption(df_all, model, lib):\n",
    "    df = df_all[['time (ms)', 'memory (MB)']] \\\n",
    "        .groupby(level=df_all.index.names[:-1]).mean() \\\n",
    "        .loc[model, lib]\n",
    "    df['time per image (ms)'] = df['time (ms)'].divide(df.index, axis=0)\n",
    "    df['memory per image (MB)'] = df['memory (MB)'].divide(df.index, axis=0)\n",
    "    df = df.drop('time (ms)', axis=1).sortlevel(axis=1)\n",
    "    ax = df.plot(secondary_y=['memory (MB)', 'memory per image (MB)'], mark_right=False, grid=True,\n",
    "                 figsize=[12, 8], fontsize=default_fontsize, colormap=cm.winter)\n",
    "    ax.set_title('%s w/ %s' % (model, lib), fontsize=default_fontsize)\n",
    "    ax.set_xlabel(df.index.name, fontsize=default_fontsize)\n",
    "    ax.set_ylabel('execution time (ms)', fontsize=default_fontsize); ax.legend(loc='center left'); ax.set_ylim(0)\n",
    "    ax.right_ax.set_ylabel('memory consumption (MB)', fontsize=default_fontsize); ax.right_ax.legend(loc='center right')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"data\"></a>\n",
    "## Get the experimental data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**NB:** Please ignore this section if you are not interested in re-running or modifying this notebook. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Caffe experimental data was collected on the experimental platform (after installing all Caffe libraries and models of interest) as follows:\n",
    "```\n",
    "$ cd `ck find ck-caffe:script:explore-batch-size-libs-models`\n",
    "$ python explore-batch-size-libs-models-benchmark.py\n",
    "```\n",
    "It can be downloaded from GitHub via CK as follows:\n",
    "```\n",
    "$ ck pull repo:ck-caffe-nvidia-tx1 --url=https://github.com/dividiti/ck-caffe-nvidia-tx1\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"tables\"></a>\n",
    "## Tables"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"df_all\"></a>\n",
    "### All data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df_all = get_experimental_results(repo_uoa=repo_uoa, tags='explore-batch-size-libs-models')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>run characteristic</th>\n",
       "      <th>memory (MB)</th>\n",
       "      <th>per layer info</th>\n",
       "      <th>success?</th>\n",
       "      <th>time (ms)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model</th>\n",
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       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>116.525416</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>1750.410000</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>116.525416</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>1767.410000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">16</th>\n",
       "      <th>0</th>\n",
       "      <td>133.171904</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>1983.670000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>133.171904</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>2001.910000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>133.171904</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>1982.130000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"24\" valign=\"top\">libdnn-cuda</th>\n",
       "      <th rowspan=\"3\" valign=\"top\">2</th>\n",
       "      <th>0</th>\n",
       "      <td>16.646488</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>43.455800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>16.646488</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>45.209400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>16.646488</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>45.086200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">4</th>\n",
       "      <th>0</th>\n",
       "      <td>33.292976</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>65.658900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>33.292976</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>65.170500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>33.292976</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>65.972100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">6</th>\n",
       "      <th>0</th>\n",
       "      <td>49.939464</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>93.980300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>49.939464</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>93.099900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>49.939464</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>91.359400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">8</th>\n",
       "      <th>0</th>\n",
       "      <td>66.585952</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>120.833000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>66.585952</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>118.322000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>66.585952</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>118.151000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">10</th>\n",
       "      <th>0</th>\n",
       "      <td>83.232440</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>147.654000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>83.232440</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>147.449000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>83.232440</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>149.036000</td>\n",
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       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">12</th>\n",
       "      <th>0</th>\n",
       "      <td>99.878928</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>179.296000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>99.878928</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>177.986000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>99.878928</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>177.760000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">14</th>\n",
       "      <th>0</th>\n",
       "      <td>116.525416</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>208.372000</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>116.525416</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>208.853000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>116.525416</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>208.871000</td>\n",
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       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">16</th>\n",
       "      <th>0</th>\n",
       "      <td>133.171904</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>233.930000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>133.171904</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>232.266000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>133.171904</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>235.624000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"24\" valign=\"top\">nvidia-cuda</th>\n",
       "      <th rowspan=\"3\" valign=\"top\">2</th>\n",
       "      <th>0</th>\n",
       "      <td>16.646488</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>32.655300</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>16.646488</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>34.419300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>16.646488</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>32.973400</td>\n",
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       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">4</th>\n",
       "      <th>0</th>\n",
       "      <td>33.292976</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>44.510100</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>33.292976</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>44.684900</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>33.292976</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>47.259100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">6</th>\n",
       "      <th>0</th>\n",
       "      <td>49.939464</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>63.047100</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>49.939464</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>62.730400</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>49.939464</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>62.857800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">8</th>\n",
       "      <th>0</th>\n",
       "      <td>66.585952</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>82.230200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>66.585952</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>81.707200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>66.585952</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>81.749900</td>\n",
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       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">10</th>\n",
       "      <th>0</th>\n",
       "      <td>83.232440</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>104.912000</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>83.232440</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>102.048000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>83.232440</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>102.393000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">12</th>\n",
       "      <th>0</th>\n",
       "      <td>99.878928</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>123.494000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>99.878928</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>122.632000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>99.878928</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>123.046000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">14</th>\n",
       "      <th>0</th>\n",
       "      <td>116.525416</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>146.433000</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>116.525416</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>145.012000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>116.525416</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>144.910000</td>\n",
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       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">16</th>\n",
       "      <th>0</th>\n",
       "      <td>133.171904</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>161.290000</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>133.171904</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>159.418000</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>133.171904</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>163.115000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"24\" valign=\"top\">nvidia-cudnn</th>\n",
       "      <th rowspan=\"3\" valign=\"top\">2</th>\n",
       "      <th>0</th>\n",
       "      <td>16.646488</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>30.962000</td>\n",
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       "    <tr>\n",
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       "      <td>16.646488</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>31.267200</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>16.646488</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>29.678100</td>\n",
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       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">4</th>\n",
       "      <th>0</th>\n",
       "      <td>33.292976</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>36.172400</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>33.292976</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>35.930800</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>33.292976</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>35.934400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">6</th>\n",
       "      <th>0</th>\n",
       "      <td>49.939464</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>49.516800</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>49.939464</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>47.077900</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>49.939464</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>48.064000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">8</th>\n",
       "      <th>0</th>\n",
       "      <td>66.585952</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>61.372700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>66.585952</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>61.781200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>66.585952</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>62.266000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">10</th>\n",
       "      <th>0</th>\n",
       "      <td>83.232440</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>77.890600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>83.232440</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>76.912900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>83.232440</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>77.028600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">12</th>\n",
       "      <th>0</th>\n",
       "      <td>99.878928</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>90.423300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>99.878928</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>91.181600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>99.878928</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>90.351700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">14</th>\n",
       "      <th>0</th>\n",
       "      <td>116.525416</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>108.288000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>116.525416</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>108.049000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>116.525416</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>110.727000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">16</th>\n",
       "      <th>0</th>\n",
       "      <td>133.171904</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>118.699000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>133.171904</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>118.891000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>133.171904</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>118.251000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"24\" valign=\"top\">nvidia-fp16-cuda</th>\n",
       "      <th rowspan=\"3\" valign=\"top\">2</th>\n",
       "      <th>0</th>\n",
       "      <td>7.704896</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>24.763800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7.704896</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>31.361200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.704896</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>24.386800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">4</th>\n",
       "      <th>0</th>\n",
       "      <td>15.409792</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>35.685400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>15.409792</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>35.958800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>15.409792</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>35.573300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">6</th>\n",
       "      <th>0</th>\n",
       "      <td>23.114688</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>54.463000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>23.114688</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>52.272600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>23.114688</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>52.196600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">8</th>\n",
       "      <th>0</th>\n",
       "      <td>30.819584</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>69.339700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>30.819584</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>70.808600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>30.819584</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>69.177900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">10</th>\n",
       "      <th>0</th>\n",
       "      <td>38.524480</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>87.918800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>38.524480</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>87.469800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>38.524480</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>87.699700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">12</th>\n",
       "      <th>0</th>\n",
       "      <td>46.229376</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>103.192000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>46.229376</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>102.738000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>46.229376</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>103.069000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">14</th>\n",
       "      <th>0</th>\n",
       "      <td>53.934272</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>128.973000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>53.934272</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>129.737000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>53.934272</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>128.318000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">16</th>\n",
       "      <th>0</th>\n",
       "      <td>61.639168</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>138.174000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>61.639168</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>136.887000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>61.639168</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>138.502000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"24\" valign=\"top\">nvidia-fp16-cudnn</th>\n",
       "      <th rowspan=\"3\" valign=\"top\">2</th>\n",
       "      <th>0</th>\n",
       "      <td>7.704896</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>18.756000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7.704896</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>18.588600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.704896</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>18.794500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">4</th>\n",
       "      <th>0</th>\n",
       "      <td>15.409792</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>23.403500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>15.409792</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>23.671800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>15.409792</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>23.406400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">6</th>\n",
       "      <th>0</th>\n",
       "      <td>23.114688</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>32.481200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>23.114688</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>32.601000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>23.114688</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>32.558900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">8</th>\n",
       "      <th>0</th>\n",
       "      <td>30.819584</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>41.800500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>30.819584</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>41.906000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>30.819584</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>41.736200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">10</th>\n",
       "      <th>0</th>\n",
       "      <td>38.524480</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>56.119700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>38.524480</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>55.608400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>38.524480</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>56.036900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">12</th>\n",
       "      <th>0</th>\n",
       "      <td>46.229376</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>62.911300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>46.229376</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>62.975500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>46.229376</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>63.190200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">14</th>\n",
       "      <th>0</th>\n",
       "      <td>53.934272</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>83.796800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>53.934272</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>83.486300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>53.934272</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>84.114500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">16</th>\n",
       "      <th>0</th>\n",
       "      <td>61.639168</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>81.327700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>61.639168</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>80.877900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>61.639168</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>80.907600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"24\" valign=\"top\">tensorrt-fp16</th>\n",
       "      <th rowspan=\"3\" valign=\"top\">2</th>\n",
       "      <th>0</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'nam...</td>\n",
       "      <td>yes</td>\n",
       "      <td>12.620625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'nam...</td>\n",
       "      <td>yes</td>\n",
       "      <td>12.564573</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'nam...</td>\n",
       "      <td>yes</td>\n",
       "      <td>12.914520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">4</th>\n",
       "      <th>0</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'nam...</td>\n",
       "      <td>yes</td>\n",
       "      <td>16.090150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'nam...</td>\n",
       "      <td>yes</td>\n",
       "      <td>15.953150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'nam...</td>\n",
       "      <td>yes</td>\n",
       "      <td>15.990307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">6</th>\n",
       "      <th>0</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'nam...</td>\n",
       "      <td>yes</td>\n",
       "      <td>27.783512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'nam...</td>\n",
       "      <td>yes</td>\n",
       "      <td>26.235513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'nam...</td>\n",
       "      <td>yes</td>\n",
       "      <td>26.120513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">8</th>\n",
       "      <th>0</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'nam...</td>\n",
       "      <td>yes</td>\n",
       "      <td>30.959194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'nam...</td>\n",
       "      <td>yes</td>\n",
       "      <td>30.995985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'nam...</td>\n",
       "      <td>yes</td>\n",
       "      <td>30.967563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">10</th>\n",
       "      <th>0</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'nam...</td>\n",
       "      <td>yes</td>\n",
       "      <td>34.830140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'nam...</td>\n",
       "      <td>yes</td>\n",
       "      <td>34.651298</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'nam...</td>\n",
       "      <td>yes</td>\n",
       "      <td>34.610877</td>\n",
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       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">12</th>\n",
       "      <th>0</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'nam...</td>\n",
       "      <td>yes</td>\n",
       "      <td>39.653559</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'nam...</td>\n",
       "      <td>yes</td>\n",
       "      <td>39.588243</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'nam...</td>\n",
       "      <td>yes</td>\n",
       "      <td>39.469033</td>\n",
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       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">14</th>\n",
       "      <th>0</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'nam...</td>\n",
       "      <td>yes</td>\n",
       "      <td>43.443609</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'nam...</td>\n",
       "      <td>yes</td>\n",
       "      <td>43.248030</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'nam...</td>\n",
       "      <td>yes</td>\n",
       "      <td>43.646662</td>\n",
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       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">16</th>\n",
       "      <th>0</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'nam...</td>\n",
       "      <td>yes</td>\n",
       "      <td>47.710660</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'nam...</td>\n",
       "      <td>yes</td>\n",
       "      <td>47.555765</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'nam...</td>\n",
       "      <td>yes</td>\n",
       "      <td>47.390291</td>\n",
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       "    <tr>\n",
       "      <th rowspan=\"24\" valign=\"top\">tensorrt-fp32</th>\n",
       "      <th rowspan=\"3\" valign=\"top\">2</th>\n",
       "      <th>0</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'nam...</td>\n",
       "      <td>yes</td>\n",
       "      <td>29.353353</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'nam...</td>\n",
       "      <td>yes</td>\n",
       "      <td>20.505832</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'nam...</td>\n",
       "      <td>yes</td>\n",
       "      <td>20.473990</td>\n",
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       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">4</th>\n",
       "      <th>0</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'nam...</td>\n",
       "      <td>yes</td>\n",
       "      <td>28.293512</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'nam...</td>\n",
       "      <td>yes</td>\n",
       "      <td>28.337670</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'nam...</td>\n",
       "      <td>yes</td>\n",
       "      <td>28.287670</td>\n",
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       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">6</th>\n",
       "      <th>0</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'nam...</td>\n",
       "      <td>yes</td>\n",
       "      <td>40.110348</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'nam...</td>\n",
       "      <td>yes</td>\n",
       "      <td>40.261453</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'nam...</td>\n",
       "      <td>yes</td>\n",
       "      <td>40.066032</td>\n",
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       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">8</th>\n",
       "      <th>0</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'nam...</td>\n",
       "      <td>yes</td>\n",
       "      <td>52.178815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'nam...</td>\n",
       "      <td>yes</td>\n",
       "      <td>52.142868</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'nam...</td>\n",
       "      <td>yes</td>\n",
       "      <td>52.086184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">10</th>\n",
       "      <th>0</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'nam...</td>\n",
       "      <td>yes</td>\n",
       "      <td>63.779229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'nam...</td>\n",
       "      <td>yes</td>\n",
       "      <td>63.143178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'nam...</td>\n",
       "      <td>yes</td>\n",
       "      <td>63.218810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">12</th>\n",
       "      <th>0</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'nam...</td>\n",
       "      <td>yes</td>\n",
       "      <td>71.943542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'nam...</td>\n",
       "      <td>yes</td>\n",
       "      <td>72.634436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'nam...</td>\n",
       "      <td>yes</td>\n",
       "      <td>71.585332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">14</th>\n",
       "      <th>0</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'nam...</td>\n",
       "      <td>yes</td>\n",
       "      <td>79.590329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'nam...</td>\n",
       "      <td>yes</td>\n",
       "      <td>79.380643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'nam...</td>\n",
       "      <td>yes</td>\n",
       "      <td>78.854327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">16</th>\n",
       "      <th>0</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'nam...</td>\n",
       "      <td>yes</td>\n",
       "      <td>104.076155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'nam...</td>\n",
       "      <td>yes</td>\n",
       "      <td>103.862054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'nam...</td>\n",
       "      <td>yes</td>\n",
       "      <td>103.895998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"192\" valign=\"top\">bvlc-googlenet</th>\n",
       "      <th rowspan=\"24\" valign=\"top\">cpu</th>\n",
       "      <th rowspan=\"3\" valign=\"top\">2</th>\n",
       "      <th>0</th>\n",
       "      <td>110.306688</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>726.576000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>110.306688</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>721.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>110.306688</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>721.741000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">4</th>\n",
       "      <th>0</th>\n",
       "      <td>220.613376</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>1433.930000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>220.613376</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>1435.080000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>220.613376</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>1452.440000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">6</th>\n",
       "      <th>0</th>\n",
       "      <td>330.920064</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>2161.430000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>330.920064</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>2169.870000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>330.920064</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>2165.080000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">8</th>\n",
       "      <th>0</th>\n",
       "      <td>441.226752</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>2872.770000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>441.226752</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>2895.270000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>441.226752</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>2882.010000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">10</th>\n",
       "      <th>0</th>\n",
       "      <td>551.533440</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>3577.710000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>551.533440</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>3592.950000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>551.533440</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>3600.860000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">12</th>\n",
       "      <th>0</th>\n",
       "      <td>661.840128</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>4307.930000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>661.840128</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>4308.880000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>661.840128</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>4362.740000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">14</th>\n",
       "      <th>0</th>\n",
       "      <td>772.146816</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>5008.490000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>772.146816</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>5067.070000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>772.146816</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>5023.240000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">16</th>\n",
       "      <th>0</th>\n",
       "      <td>882.453504</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>5737.780000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>882.453504</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>5730.080000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>882.453504</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>5731.630000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"24\" valign=\"top\">libdnn-cuda</th>\n",
       "      <th rowspan=\"3\" valign=\"top\">2</th>\n",
       "      <th>0</th>\n",
       "      <td>110.306688</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>95.832100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>110.306688</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>98.095700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>110.306688</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>107.846000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">4</th>\n",
       "      <th>0</th>\n",
       "      <td>220.613376</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>150.999000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>220.613376</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>152.185000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>220.613376</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>153.025000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">6</th>\n",
       "      <th>0</th>\n",
       "      <td>330.920064</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>215.748000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>330.920064</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
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       "      <th>2</th>\n",
       "      <td>330.920064</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>216.775000</td>\n",
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       "      <th rowspan=\"3\" valign=\"top\">8</th>\n",
       "      <th>0</th>\n",
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       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
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       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>441.226752</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>276.615000</td>\n",
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       "      <th rowspan=\"3\" valign=\"top\">10</th>\n",
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       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>338.860000</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>551.533440</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>349.021000</td>\n",
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       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>349.793000</td>\n",
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       "      <th rowspan=\"3\" valign=\"top\">12</th>\n",
       "      <th>0</th>\n",
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       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>414.334000</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>661.840128</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>411.590000</td>\n",
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       "      <th>2</th>\n",
       "      <td>661.840128</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>401.385000</td>\n",
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       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">14</th>\n",
       "      <th>0</th>\n",
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       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>462.678000</td>\n",
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       "      <td>772.146816</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>468.287000</td>\n",
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       "      <td>772.146816</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>472.324000</td>\n",
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       "      <th rowspan=\"3\" valign=\"top\">16</th>\n",
       "      <th>0</th>\n",
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       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>523.130000</td>\n",
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       "      <td>882.453504</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>522.837000</td>\n",
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       "      <td>882.453504</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>522.547000</td>\n",
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       "    <tr>\n",
       "      <th rowspan=\"24\" valign=\"top\">nvidia-cuda</th>\n",
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       "      <th>0</th>\n",
       "      <td>110.306688</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
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       "    <tr>\n",
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       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>67.808100</td>\n",
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       "      <th>2</th>\n",
       "      <td>110.306688</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>67.142200</td>\n",
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       "      <th rowspan=\"3\" valign=\"top\">4</th>\n",
       "      <th>0</th>\n",
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       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>114.992000</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>220.613376</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>113.434000</td>\n",
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       "      <th>2</th>\n",
       "      <td>220.613376</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>113.910000</td>\n",
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       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">6</th>\n",
       "      <th>0</th>\n",
       "      <td>330.920064</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>158.350000</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>330.920064</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>158.556000</td>\n",
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       "      <th>2</th>\n",
       "      <td>330.920064</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>159.451000</td>\n",
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       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">8</th>\n",
       "      <th>0</th>\n",
       "      <td>441.226752</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>203.071000</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>441.226752</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>204.409000</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>441.226752</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>203.029000</td>\n",
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       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">10</th>\n",
       "      <th>0</th>\n",
       "      <td>551.533440</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>248.787000</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>551.533440</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>248.164000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>551.533440</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>248.568000</td>\n",
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       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">12</th>\n",
       "      <th>0</th>\n",
       "      <td>661.840128</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>298.789000</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>661.840128</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>294.533000</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>661.840128</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>300.100000</td>\n",
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       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">14</th>\n",
       "      <th>0</th>\n",
       "      <td>772.146816</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>342.822000</td>\n",
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       "      <th>1</th>\n",
       "      <td>772.146816</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>339.103000</td>\n",
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       "      <td>772.146816</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>347.853000</td>\n",
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       "      <th rowspan=\"3\" valign=\"top\">16</th>\n",
       "      <th>0</th>\n",
       "      <td>882.453504</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>383.233000</td>\n",
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       "      <td>882.453504</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>386.597000</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>882.453504</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>385.031000</td>\n",
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       "    <tr>\n",
       "      <th rowspan=\"24\" valign=\"top\">nvidia-cudnn</th>\n",
       "      <th rowspan=\"3\" valign=\"top\">2</th>\n",
       "      <th>0</th>\n",
       "      <td>110.306688</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>55.022400</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>110.306688</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>58.155200</td>\n",
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       "      <td>110.306688</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>55.736300</td>\n",
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       "      <th rowspan=\"3\" valign=\"top\">4</th>\n",
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       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
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       "      <th>1</th>\n",
       "      <td>220.613376</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>85.662900</td>\n",
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       "      <th>2</th>\n",
       "      <td>220.613376</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>86.069500</td>\n",
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       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">6</th>\n",
       "      <th>0</th>\n",
       "      <td>330.920064</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>117.502000</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>330.920064</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>123.356000</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>330.920064</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>117.326000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">8</th>\n",
       "      <th>0</th>\n",
       "      <td>441.226752</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>148.409000</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>441.226752</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>151.150000</td>\n",
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       "      <td>441.226752</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>149.771000</td>\n",
       "    </tr>\n",
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       "      <th rowspan=\"3\" valign=\"top\">10</th>\n",
       "      <th>0</th>\n",
       "      <td>551.533440</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>182.625000</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>551.533440</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>178.184000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>551.533440</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>177.301000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">12</th>\n",
       "      <th>0</th>\n",
       "      <td>661.840128</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>211.654000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>661.840128</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>209.654000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>661.840128</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>211.429000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">14</th>\n",
       "      <th>0</th>\n",
       "      <td>772.146816</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>243.337000</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>772.146816</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>244.806000</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>772.146816</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>242.718000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">16</th>\n",
       "      <th>0</th>\n",
       "      <td>882.453504</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>272.625000</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>882.453504</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>274.295000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>882.453504</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>274.347000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"24\" valign=\"top\">nvidia-fp16-cuda</th>\n",
       "      <th rowspan=\"3\" valign=\"top\">2</th>\n",
       "      <th>0</th>\n",
       "      <td>54.551232</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>49.495300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>54.551232</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>49.407600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>54.551232</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>51.201900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">4</th>\n",
       "      <th>0</th>\n",
       "      <td>109.102464</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>91.893600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>109.102464</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>93.751800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>109.102464</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>91.854400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">6</th>\n",
       "      <th>0</th>\n",
       "      <td>163.653696</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>134.170000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>163.653696</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>134.373000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>163.653696</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>133.610000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">8</th>\n",
       "      <th>0</th>\n",
       "      <td>218.204928</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>176.348000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>218.204928</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>176.455000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>218.204928</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>176.885000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">10</th>\n",
       "      <th>0</th>\n",
       "      <td>272.756160</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>219.665000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>272.756160</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>219.154000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>272.756160</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>221.270000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">12</th>\n",
       "      <th>0</th>\n",
       "      <td>327.307392</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>263.124000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>327.307392</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>262.079000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>327.307392</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>263.535000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">14</th>\n",
       "      <th>0</th>\n",
       "      <td>381.858624</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>306.364000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>381.858624</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>305.265000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>381.858624</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>306.503000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">16</th>\n",
       "      <th>0</th>\n",
       "      <td>436.409856</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>348.909000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>436.409856</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>349.635000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>436.409856</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>348.834000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"24\" valign=\"top\">nvidia-fp16-cudnn</th>\n",
       "      <th rowspan=\"3\" valign=\"top\">2</th>\n",
       "      <th>0</th>\n",
       "      <td>54.551232</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>29.643400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>54.551232</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>29.601300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>54.551232</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>29.663000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">4</th>\n",
       "      <th>0</th>\n",
       "      <td>109.102464</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>54.761200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>109.102464</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>54.610200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>109.102464</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>54.659300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">6</th>\n",
       "      <th>0</th>\n",
       "      <td>163.653696</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>80.573500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>163.653696</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>80.484700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>163.653696</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>80.399300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">8</th>\n",
       "      <th>0</th>\n",
       "      <td>218.204928</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>90.605100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>218.204928</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>90.459300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>218.204928</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>90.586500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">10</th>\n",
       "      <th>0</th>\n",
       "      <td>272.756160</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>128.923000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>272.756160</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>129.616000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>272.756160</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>131.376000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">12</th>\n",
       "      <th>0</th>\n",
       "      <td>327.307392</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>153.502000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>327.307392</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>153.667000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>327.307392</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>153.424000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">14</th>\n",
       "      <th>0</th>\n",
       "      <td>381.858624</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>183.439000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>381.858624</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>181.383000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>381.858624</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>183.069000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">16</th>\n",
       "      <th>0</th>\n",
       "      <td>436.409856</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>178.709000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>436.409856</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>177.023000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>436.409856</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>178.549000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"24\" valign=\"top\">tensorrt-fp16</th>\n",
       "      <th rowspan=\"3\" valign=\"top\">2</th>\n",
       "      <th>0</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'nam...</td>\n",
       "      <td>yes</td>\n",
       "      <td>14.485308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'nam...</td>\n",
       "      <td>yes</td>\n",
       "      <td>14.576308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'nam...</td>\n",
       "      <td>yes</td>\n",
       "      <td>14.585309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">4</th>\n",
       "      <th>0</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'nam...</td>\n",
       "      <td>yes</td>\n",
       "      <td>24.238514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'nam...</td>\n",
       "      <td>yes</td>\n",
       "      <td>24.137461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'nam...</td>\n",
       "      <td>yes</td>\n",
       "      <td>24.254303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">6</th>\n",
       "      <th>0</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'nam...</td>\n",
       "      <td>yes</td>\n",
       "      <td>34.025298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'nam...</td>\n",
       "      <td>yes</td>\n",
       "      <td>34.076772</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'nam...</td>\n",
       "      <td>yes</td>\n",
       "      <td>34.172509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">8</th>\n",
       "      <th>0</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'nam...</td>\n",
       "      <td>yes</td>\n",
       "      <td>44.013294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'nam...</td>\n",
       "      <td>yes</td>\n",
       "      <td>43.854451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'nam...</td>\n",
       "      <td>yes</td>\n",
       "      <td>43.804714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">10</th>\n",
       "      <th>0</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'nam...</td>\n",
       "      <td>yes</td>\n",
       "      <td>52.217868</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'nam...</td>\n",
       "      <td>yes</td>\n",
       "      <td>52.229500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'nam...</td>\n",
       "      <td>yes</td>\n",
       "      <td>52.229815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">12</th>\n",
       "      <th>0</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'nam...</td>\n",
       "      <td>yes</td>\n",
       "      <td>64.078599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'nam...</td>\n",
       "      <td>yes</td>\n",
       "      <td>64.333019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'nam...</td>\n",
       "      <td>yes</td>\n",
       "      <td>64.391072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">14</th>\n",
       "      <th>0</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'nam...</td>\n",
       "      <td>yes</td>\n",
       "      <td>74.021542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'nam...</td>\n",
       "      <td>yes</td>\n",
       "      <td>73.949804</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'nam...</td>\n",
       "      <td>yes</td>\n",
       "      <td>74.062541</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">16</th>\n",
       "      <th>0</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'nam...</td>\n",
       "      <td>yes</td>\n",
       "      <td>83.507063</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'nam...</td>\n",
       "      <td>yes</td>\n",
       "      <td>83.629536</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'nam...</td>\n",
       "      <td>yes</td>\n",
       "      <td>83.874746</td>\n",
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       "    <tr>\n",
       "      <th rowspan=\"24\" valign=\"top\">tensorrt-fp32</th>\n",
       "      <th rowspan=\"3\" valign=\"top\">2</th>\n",
       "      <th>0</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'nam...</td>\n",
       "      <td>yes</td>\n",
       "      <td>24.654566</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'nam...</td>\n",
       "      <td>yes</td>\n",
       "      <td>24.706514</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'nam...</td>\n",
       "      <td>yes</td>\n",
       "      <td>24.812513</td>\n",
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       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">4</th>\n",
       "      <th>0</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'nam...</td>\n",
       "      <td>yes</td>\n",
       "      <td>46.024871</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'nam...</td>\n",
       "      <td>yes</td>\n",
       "      <td>46.235344</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'nam...</td>\n",
       "      <td>yes</td>\n",
       "      <td>46.118398</td>\n",
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       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">6</th>\n",
       "      <th>0</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'nam...</td>\n",
       "      <td>yes</td>\n",
       "      <td>68.209123</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'nam...</td>\n",
       "      <td>yes</td>\n",
       "      <td>68.337544</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'nam...</td>\n",
       "      <td>yes</td>\n",
       "      <td>68.308860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">8</th>\n",
       "      <th>0</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'nam...</td>\n",
       "      <td>yes</td>\n",
       "      <td>89.629849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'nam...</td>\n",
       "      <td>yes</td>\n",
       "      <td>88.754744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'nam...</td>\n",
       "      <td>yes</td>\n",
       "      <td>89.036270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">10</th>\n",
       "      <th>0</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'nam...</td>\n",
       "      <td>yes</td>\n",
       "      <td>108.358576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'nam...</td>\n",
       "      <td>yes</td>\n",
       "      <td>109.368577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'nam...</td>\n",
       "      <td>yes</td>\n",
       "      <td>108.333629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">12</th>\n",
       "      <th>0</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'nam...</td>\n",
       "      <td>yes</td>\n",
       "      <td>132.795089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'nam...</td>\n",
       "      <td>yes</td>\n",
       "      <td>132.970562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'nam...</td>\n",
       "      <td>yes</td>\n",
       "      <td>132.811038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">14</th>\n",
       "      <th>0</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'nam...</td>\n",
       "      <td>yes</td>\n",
       "      <td>153.733922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'nam...</td>\n",
       "      <td>yes</td>\n",
       "      <td>153.970342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'nam...</td>\n",
       "      <td>yes</td>\n",
       "      <td>153.733921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">16</th>\n",
       "      <th>0</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'nam...</td>\n",
       "      <td>yes</td>\n",
       "      <td>176.017908</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'nam...</td>\n",
       "      <td>yes</td>\n",
       "      <td>177.000647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'nam...</td>\n",
       "      <td>yes</td>\n",
       "      <td>176.663119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"192\" valign=\"top\">deepscale-squeezenet-1.0</th>\n",
       "      <th rowspan=\"24\" valign=\"top\">cpu</th>\n",
       "      <th rowspan=\"3\" valign=\"top\">2</th>\n",
       "      <th>0</th>\n",
       "      <td>106.120408</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>253.702000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>106.120408</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>255.276000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>106.120408</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>252.807000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">4</th>\n",
       "      <th>0</th>\n",
       "      <td>212.240816</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>502.686000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>212.240816</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>504.233000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>212.240816</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>504.196000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">6</th>\n",
       "      <th>0</th>\n",
       "      <td>318.361224</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>756.378000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>318.361224</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>752.891000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>318.361224</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>755.365000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">8</th>\n",
       "      <th>0</th>\n",
       "      <td>424.481632</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>1002.240000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>424.481632</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>1003.510000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>424.481632</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>1011.790000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">10</th>\n",
       "      <th>0</th>\n",
       "      <td>530.602040</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>1253.520000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>530.602040</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>1260.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>530.602040</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>1256.570000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">12</th>\n",
       "      <th>0</th>\n",
       "      <td>636.722448</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>1504.420000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>636.722448</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>1505.160000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>636.722448</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>1505.980000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">14</th>\n",
       "      <th>0</th>\n",
       "      <td>742.842856</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>1754.850000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>742.842856</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>1763.890000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>742.842856</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>1752.510000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">16</th>\n",
       "      <th>0</th>\n",
       "      <td>848.963264</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>2017.420000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>848.963264</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>2005.910000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>848.963264</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>2027.760000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"24\" valign=\"top\">libdnn-cuda</th>\n",
       "      <th rowspan=\"3\" valign=\"top\">2</th>\n",
       "      <th>0</th>\n",
       "      <td>106.120408</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>48.651200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>106.120408</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>50.457500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>106.120408</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>55.726400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">4</th>\n",
       "      <th>0</th>\n",
       "      <td>212.240816</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>88.424500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>212.240816</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>86.170400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>212.240816</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>89.805400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">6</th>\n",
       "      <th>0</th>\n",
       "      <td>318.361224</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>126.186000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>318.361224</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>133.083000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>318.361224</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>123.676000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">8</th>\n",
       "      <th>0</th>\n",
       "      <td>424.481632</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>166.935000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>424.481632</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>162.627000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>424.481632</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>163.530000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">10</th>\n",
       "      <th>0</th>\n",
       "      <td>530.602040</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>196.310000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>530.602040</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>223.921000</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>530.602040</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>197.104000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">12</th>\n",
       "      <th>0</th>\n",
       "      <td>636.722448</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>233.976000</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>636.722448</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>234.237000</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>636.722448</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>235.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">14</th>\n",
       "      <th>0</th>\n",
       "      <td>742.842856</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>270.913000</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>742.842856</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>276.680000</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>742.842856</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>271.152000</td>\n",
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       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">16</th>\n",
       "      <th>0</th>\n",
       "      <td>848.963264</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>314.305000</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>848.963264</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>307.690000</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>848.963264</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>314.576000</td>\n",
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       "    <tr>\n",
       "      <th rowspan=\"24\" valign=\"top\">nvidia-cuda</th>\n",
       "      <th rowspan=\"3\" valign=\"top\">2</th>\n",
       "      <th>0</th>\n",
       "      <td>106.120408</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>40.542600</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>106.120408</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>41.271000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>106.120408</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>41.370500</td>\n",
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       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">4</th>\n",
       "      <th>0</th>\n",
       "      <td>212.240816</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>77.570300</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>212.240816</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>72.845500</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>212.240816</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>71.853300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">6</th>\n",
       "      <th>0</th>\n",
       "      <td>318.361224</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>103.481000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>318.361224</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>104.214000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>318.361224</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>107.186000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">8</th>\n",
       "      <th>0</th>\n",
       "      <td>424.481632</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>133.831000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>424.481632</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>133.928000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>424.481632</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>136.559000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">10</th>\n",
       "      <th>0</th>\n",
       "      <td>530.602040</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>166.995000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>530.602040</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>166.171000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>530.602040</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>163.876000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">12</th>\n",
       "      <th>0</th>\n",
       "      <td>636.722448</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>195.475000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>636.722448</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>194.790000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>636.722448</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>197.505000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">14</th>\n",
       "      <th>0</th>\n",
       "      <td>742.842856</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>225.449000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>742.842856</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>224.973000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>742.842856</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>227.388000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">16</th>\n",
       "      <th>0</th>\n",
       "      <td>848.963264</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>255.899000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>848.963264</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>256.939000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>848.963264</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>257.944000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"24\" valign=\"top\">nvidia-cudnn</th>\n",
       "      <th rowspan=\"3\" valign=\"top\">2</th>\n",
       "      <th>0</th>\n",
       "      <td>106.120408</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>34.387000</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>106.120408</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>36.602200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>106.120408</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>35.238500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">4</th>\n",
       "      <th>0</th>\n",
       "      <td>212.240816</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>59.645700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>212.240816</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>60.952400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>212.240816</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>58.919300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">6</th>\n",
       "      <th>0</th>\n",
       "      <td>318.361224</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>83.164300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>318.361224</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>83.634100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>318.361224</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>83.703200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">8</th>\n",
       "      <th>0</th>\n",
       "      <td>424.481632</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>108.407000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>424.481632</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>110.041000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>424.481632</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>112.536000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">10</th>\n",
       "      <th>0</th>\n",
       "      <td>530.602040</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>134.133000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>530.602040</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>132.381000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>530.602040</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>134.470000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">12</th>\n",
       "      <th>0</th>\n",
       "      <td>636.722448</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>156.495000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>636.722448</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>156.894000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>636.722448</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>158.866000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">14</th>\n",
       "      <th>0</th>\n",
       "      <td>742.842856</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>185.548000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>742.842856</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>183.169000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>742.842856</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>185.909000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">16</th>\n",
       "      <th>0</th>\n",
       "      <td>848.963264</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>211.621000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>848.963264</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>206.880000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>848.963264</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>205.637000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"24\" valign=\"top\">nvidia-fp16-cuda</th>\n",
       "      <th rowspan=\"3\" valign=\"top\">2</th>\n",
       "      <th>0</th>\n",
       "      <td>52.441856</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>27.853600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>52.441856</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>27.492600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>52.441856</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>27.647800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">4</th>\n",
       "      <th>0</th>\n",
       "      <td>104.883712</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>53.218000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>104.883712</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>53.337200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>104.883712</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>54.811700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">6</th>\n",
       "      <th>0</th>\n",
       "      <td>157.325568</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>78.116900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>157.325568</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>78.267300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>157.325568</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>78.620300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">8</th>\n",
       "      <th>0</th>\n",
       "      <td>209.767424</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>104.016000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>209.767424</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>103.641000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>209.767424</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>103.442000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">10</th>\n",
       "      <th>0</th>\n",
       "      <td>262.209280</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>128.267000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>262.209280</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>128.886000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>262.209280</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>128.613000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">12</th>\n",
       "      <th>0</th>\n",
       "      <td>314.651136</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>155.909000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>314.651136</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>156.209000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>314.651136</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>154.058000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">14</th>\n",
       "      <th>0</th>\n",
       "      <td>367.092992</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>179.802000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>367.092992</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>179.570000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>367.092992</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>181.353000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">16</th>\n",
       "      <th>0</th>\n",
       "      <td>419.534848</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>206.533000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>419.534848</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>204.565000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>419.534848</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>204.765000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"24\" valign=\"top\">nvidia-fp16-cudnn</th>\n",
       "      <th rowspan=\"3\" valign=\"top\">2</th>\n",
       "      <th>0</th>\n",
       "      <td>52.441856</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>20.285600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>52.441856</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>20.263700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>52.441856</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>20.176900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">4</th>\n",
       "      <th>0</th>\n",
       "      <td>104.883712</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>39.222300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>104.883712</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>39.080000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>104.883712</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>39.138000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">6</th>\n",
       "      <th>0</th>\n",
       "      <td>157.325568</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>58.987900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>157.325568</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>59.029000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>157.325568</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>59.102300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">8</th>\n",
       "      <th>0</th>\n",
       "      <td>209.767424</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>75.810200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>209.767424</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>72.273200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>209.767424</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>71.923500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">10</th>\n",
       "      <th>0</th>\n",
       "      <td>262.209280</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>98.192500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>262.209280</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>97.878600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>262.209280</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>97.853600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">12</th>\n",
       "      <th>0</th>\n",
       "      <td>314.651136</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>116.559000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>314.651136</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>114.888000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>314.651136</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>117.108000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">14</th>\n",
       "      <th>0</th>\n",
       "      <td>367.092992</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>136.638000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>367.092992</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>136.398000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>367.092992</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>136.875000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">16</th>\n",
       "      <th>0</th>\n",
       "      <td>419.534848</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>142.985000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>419.534848</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>143.718000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>419.534848</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>143.840000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"24\" valign=\"top\">tensorrt-fp16</th>\n",
       "      <th rowspan=\"3\" valign=\"top\">2</th>\n",
       "      <th>0</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'nam...</td>\n",
       "      <td>yes</td>\n",
       "      <td>8.757838</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'nam...</td>\n",
       "      <td>yes</td>\n",
       "      <td>8.738153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'nam...</td>\n",
       "      <td>yes</td>\n",
       "      <td>8.700311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">4</th>\n",
       "      <th>0</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'nam...</td>\n",
       "      <td>yes</td>\n",
       "      <td>15.879676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'nam...</td>\n",
       "      <td>yes</td>\n",
       "      <td>15.922834</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'nam...</td>\n",
       "      <td>yes</td>\n",
       "      <td>15.959466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">6</th>\n",
       "      <th>0</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'nam...</td>\n",
       "      <td>yes</td>\n",
       "      <td>23.154199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'nam...</td>\n",
       "      <td>yes</td>\n",
       "      <td>23.167041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'nam...</td>\n",
       "      <td>yes</td>\n",
       "      <td>23.222724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">8</th>\n",
       "      <th>0</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'nam...</td>\n",
       "      <td>yes</td>\n",
       "      <td>30.145195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'nam...</td>\n",
       "      <td>yes</td>\n",
       "      <td>30.219143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'nam...</td>\n",
       "      <td>yes</td>\n",
       "      <td>30.188668</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">10</th>\n",
       "      <th>0</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'nam...</td>\n",
       "      <td>yes</td>\n",
       "      <td>37.440718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'nam...</td>\n",
       "      <td>yes</td>\n",
       "      <td>37.072928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'nam...</td>\n",
       "      <td>yes</td>\n",
       "      <td>37.368454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">12</th>\n",
       "      <th>0</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'nam...</td>\n",
       "      <td>yes</td>\n",
       "      <td>45.281240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'nam...</td>\n",
       "      <td>yes</td>\n",
       "      <td>44.407293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'nam...</td>\n",
       "      <td>yes</td>\n",
       "      <td>44.368872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">14</th>\n",
       "      <th>0</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'nam...</td>\n",
       "      <td>yes</td>\n",
       "      <td>51.842710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'nam...</td>\n",
       "      <td>yes</td>\n",
       "      <td>51.829131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'nam...</td>\n",
       "      <td>yes</td>\n",
       "      <td>52.408868</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">16</th>\n",
       "      <th>0</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'nam...</td>\n",
       "      <td>yes</td>\n",
       "      <td>58.747601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'nam...</td>\n",
       "      <td>yes</td>\n",
       "      <td>58.934180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'nam...</td>\n",
       "      <td>yes</td>\n",
       "      <td>58.617443</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"24\" valign=\"top\">tensorrt-fp32</th>\n",
       "      <th rowspan=\"3\" valign=\"top\">2</th>\n",
       "      <th>0</th>\n",
       "      <td>0.000000</td>\n",
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       "      <td>yes</td>\n",
       "      <td>15.774518</td>\n",
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       "      <td>[{u'index': 0, u'direction': u'forward', u'nam...</td>\n",
       "      <td>yes</td>\n",
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       "      <td>[{u'index': 0, u'direction': u'forward', u'nam...</td>\n",
       "      <td>yes</td>\n",
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       "      <td>[{u'index': 0, u'direction': u'forward', u'nam...</td>\n",
       "      <td>yes</td>\n",
       "      <td>30.450721</td>\n",
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       "      <td>[{u'index': 0, u'direction': u'forward', u'nam...</td>\n",
       "      <td>yes</td>\n",
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       "      <td>[{u'index': 0, u'direction': u'forward', u'nam...</td>\n",
       "      <td>yes</td>\n",
       "      <td>30.546563</td>\n",
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       "      <td>yes</td>\n",
       "      <td>45.192555</td>\n",
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       "      <td>[{u'index': 0, u'direction': u'forward', u'nam...</td>\n",
       "      <td>yes</td>\n",
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       "      <td>[{u'index': 0, u'direction': u'forward', u'nam...</td>\n",
       "      <td>yes</td>\n",
       "      <td>45.055346</td>\n",
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       "      <th rowspan=\"3\" valign=\"top\">8</th>\n",
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       "      <td>[{u'index': 0, u'direction': u'forward', u'nam...</td>\n",
       "      <td>yes</td>\n",
       "      <td>59.673022</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'nam...</td>\n",
       "      <td>yes</td>\n",
       "      <td>60.149180</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'nam...</td>\n",
       "      <td>yes</td>\n",
       "      <td>59.989180</td>\n",
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       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">10</th>\n",
       "      <th>0</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'nam...</td>\n",
       "      <td>yes</td>\n",
       "      <td>75.474067</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'nam...</td>\n",
       "      <td>yes</td>\n",
       "      <td>74.971699</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'nam...</td>\n",
       "      <td>yes</td>\n",
       "      <td>74.683594</td>\n",
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       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">12</th>\n",
       "      <th>0</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'nam...</td>\n",
       "      <td>yes</td>\n",
       "      <td>90.008849</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'nam...</td>\n",
       "      <td>yes</td>\n",
       "      <td>89.615848</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'nam...</td>\n",
       "      <td>yes</td>\n",
       "      <td>89.681428</td>\n",
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       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">14</th>\n",
       "      <th>0</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'nam...</td>\n",
       "      <td>yes</td>\n",
       "      <td>104.018262</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'nam...</td>\n",
       "      <td>yes</td>\n",
       "      <td>103.730684</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'nam...</td>\n",
       "      <td>yes</td>\n",
       "      <td>104.923155</td>\n",
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       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">16</th>\n",
       "      <th>0</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'nam...</td>\n",
       "      <td>yes</td>\n",
       "      <td>119.386255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'nam...</td>\n",
       "      <td>yes</td>\n",
       "      <td>118.910782</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'nam...</td>\n",
       "      <td>yes</td>\n",
       "      <td>119.409730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"192\" valign=\"top\">deepscale-squeezenet-1.1</th>\n",
       "      <th rowspan=\"24\" valign=\"top\">cpu</th>\n",
       "      <th rowspan=\"3\" valign=\"top\">2</th>\n",
       "      <th>0</th>\n",
       "      <td>64.629592</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>136.270000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>64.629592</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>136.626000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>64.629592</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>137.120000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">4</th>\n",
       "      <th>0</th>\n",
       "      <td>129.259184</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>270.304000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>129.259184</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>273.057000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>129.259184</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>270.515000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">6</th>\n",
       "      <th>0</th>\n",
       "      <td>193.888776</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>408.191000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>193.888776</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>404.040000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>193.888776</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>406.419000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">8</th>\n",
       "      <th>0</th>\n",
       "      <td>258.518368</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>536.611000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>258.518368</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>538.226000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>258.518368</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>540.875000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">10</th>\n",
       "      <th>0</th>\n",
       "      <td>323.147960</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>669.522000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>323.147960</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>672.894000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>323.147960</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>671.978000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">12</th>\n",
       "      <th>0</th>\n",
       "      <td>387.777552</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>808.527000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>387.777552</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>807.042000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>387.777552</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>804.237000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">14</th>\n",
       "      <th>0</th>\n",
       "      <td>452.407144</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>937.107000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>452.407144</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>944.726000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>452.407144</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>938.090000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">16</th>\n",
       "      <th>0</th>\n",
       "      <td>517.036736</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>1078.190000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>517.036736</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>1072.820000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>517.036736</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>1086.130000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"24\" valign=\"top\">libdnn-cuda</th>\n",
       "      <th rowspan=\"3\" valign=\"top\">2</th>\n",
       "      <th>0</th>\n",
       "      <td>64.629592</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>32.291500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>64.629592</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>28.682600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>64.629592</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>43.390000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">4</th>\n",
       "      <th>0</th>\n",
       "      <td>129.259184</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>58.585900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>129.259184</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>49.974100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>129.259184</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>49.609600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">6</th>\n",
       "      <th>0</th>\n",
       "      <td>193.888776</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>76.880300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>193.888776</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>67.614700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>193.888776</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>69.939300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">8</th>\n",
       "      <th>0</th>\n",
       "      <td>258.518368</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>94.871500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>258.518368</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>92.338400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>258.518368</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>96.141600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">10</th>\n",
       "      <th>0</th>\n",
       "      <td>323.147960</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>113.236000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>323.147960</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>113.252000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>323.147960</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>105.192000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">12</th>\n",
       "      <th>0</th>\n",
       "      <td>387.777552</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>125.267000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>387.777552</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>124.526000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>387.777552</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>133.248000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">14</th>\n",
       "      <th>0</th>\n",
       "      <td>452.407144</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
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       "      <th>1</th>\n",
       "      <td>452.407144</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>145.605000</td>\n",
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       "      <td>452.407144</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>150.738000</td>\n",
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       "      <th rowspan=\"3\" valign=\"top\">16</th>\n",
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       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
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       "      <th>1</th>\n",
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       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
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       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>165.273000</td>\n",
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       "    <tr>\n",
       "      <th rowspan=\"24\" valign=\"top\">nvidia-cuda</th>\n",
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       "      <th>0</th>\n",
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       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
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       "      <td>64.629592</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
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       "      <td>64.629592</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
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       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
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       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
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       "      <td>129.259184</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>49.688400</td>\n",
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       "      <th rowspan=\"3\" valign=\"top\">6</th>\n",
       "      <th>0</th>\n",
       "      <td>193.888776</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
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       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
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       "      <td>193.888776</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
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       "      <th rowspan=\"3\" valign=\"top\">8</th>\n",
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       "      <td>258.518368</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>86.360600</td>\n",
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       "      <th>1</th>\n",
       "      <td>258.518368</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>85.752100</td>\n",
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       "      <th>2</th>\n",
       "      <td>258.518368</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>83.783000</td>\n",
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       "      <th rowspan=\"3\" valign=\"top\">10</th>\n",
       "      <th>0</th>\n",
       "      <td>323.147960</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>100.881000</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>323.147960</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>104.219000</td>\n",
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       "      <td>323.147960</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>102.938000</td>\n",
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       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">12</th>\n",
       "      <th>0</th>\n",
       "      <td>387.777552</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>122.704000</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>387.777552</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>120.847000</td>\n",
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       "    <tr>\n",
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       "      <td>387.777552</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>119.593000</td>\n",
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       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">14</th>\n",
       "      <th>0</th>\n",
       "      <td>452.407144</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>138.405000</td>\n",
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       "      <td>452.407144</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>140.048000</td>\n",
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       "      <td>452.407144</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>138.398000</td>\n",
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       "      <th rowspan=\"3\" valign=\"top\">16</th>\n",
       "      <th>0</th>\n",
       "      <td>517.036736</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>156.537000</td>\n",
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       "      <td>517.036736</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>157.109000</td>\n",
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       "      <th>2</th>\n",
       "      <td>517.036736</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>159.291000</td>\n",
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       "    <tr>\n",
       "      <th rowspan=\"24\" valign=\"top\">nvidia-cudnn</th>\n",
       "      <th rowspan=\"3\" valign=\"top\">2</th>\n",
       "      <th>0</th>\n",
       "      <td>64.629592</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
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       "      <td>64.629592</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>25.696100</td>\n",
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       "      <th>2</th>\n",
       "      <td>64.629592</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
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       "      <th rowspan=\"3\" valign=\"top\">4</th>\n",
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       "      <td>129.259184</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
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       "      <th>1</th>\n",
       "      <td>129.259184</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>38.335900</td>\n",
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       "      <th>2</th>\n",
       "      <td>129.259184</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>38.896700</td>\n",
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       "      <th rowspan=\"3\" valign=\"top\">6</th>\n",
       "      <th>0</th>\n",
       "      <td>193.888776</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>51.485400</td>\n",
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       "      <th>1</th>\n",
       "      <td>193.888776</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>51.531400</td>\n",
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       "      <th>2</th>\n",
       "      <td>193.888776</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>51.437000</td>\n",
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       "      <th rowspan=\"3\" valign=\"top\">8</th>\n",
       "      <th>0</th>\n",
       "      <td>258.518368</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>65.522900</td>\n",
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       "      <th>1</th>\n",
       "      <td>258.518368</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>64.851700</td>\n",
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       "      <th>2</th>\n",
       "      <td>258.518368</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>64.468300</td>\n",
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       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">10</th>\n",
       "      <th>0</th>\n",
       "      <td>323.147960</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>80.538000</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>323.147960</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>78.080400</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>323.147960</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>78.383700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">12</th>\n",
       "      <th>0</th>\n",
       "      <td>387.777552</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>91.085900</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>387.777552</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>94.015300</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>387.777552</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>91.812000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">14</th>\n",
       "      <th>0</th>\n",
       "      <td>452.407144</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>109.639000</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>452.407144</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>107.549000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>452.407144</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>109.073000</td>\n",
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       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">16</th>\n",
       "      <th>0</th>\n",
       "      <td>517.036736</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>119.433000</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>517.036736</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>118.776000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>517.036736</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>129.538000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"24\" valign=\"top\">nvidia-fp16-cuda</th>\n",
       "      <th rowspan=\"3\" valign=\"top\">2</th>\n",
       "      <th>0</th>\n",
       "      <td>31.696448</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>18.812500</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>31.696448</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>19.924600</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>31.696448</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>18.920100</td>\n",
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       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">4</th>\n",
       "      <th>0</th>\n",
       "      <td>63.392896</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>34.724900</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>63.392896</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>34.908400</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>63.392896</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>35.160200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">6</th>\n",
       "      <th>0</th>\n",
       "      <td>95.089344</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>50.611900</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>95.089344</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>52.941600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>95.089344</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>52.888400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">8</th>\n",
       "      <th>0</th>\n",
       "      <td>126.785792</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>66.761300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>126.785792</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>66.974200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>126.785792</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>67.070000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">10</th>\n",
       "      <th>0</th>\n",
       "      <td>158.482240</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>82.873300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>158.482240</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>85.610100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>158.482240</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>82.638900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">12</th>\n",
       "      <th>0</th>\n",
       "      <td>190.178688</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>98.593900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>190.178688</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>100.466000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>190.178688</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>98.875200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">14</th>\n",
       "      <th>0</th>\n",
       "      <td>221.875136</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>114.812000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>221.875136</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>116.996000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>221.875136</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>114.821000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">16</th>\n",
       "      <th>0</th>\n",
       "      <td>253.571584</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>140.382000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>253.571584</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>131.114000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>253.571584</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>131.181000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"24\" valign=\"top\">nvidia-fp16-cudnn</th>\n",
       "      <th rowspan=\"3\" valign=\"top\">2</th>\n",
       "      <th>0</th>\n",
       "      <td>31.696448</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>14.147600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>31.696448</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>12.577200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>31.696448</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>12.529400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">4</th>\n",
       "      <th>0</th>\n",
       "      <td>63.392896</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>23.465500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>63.392896</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>23.520600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>63.392896</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>23.469500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">6</th>\n",
       "      <th>0</th>\n",
       "      <td>95.089344</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>35.551900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>95.089344</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>35.491700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>95.089344</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>35.629900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">8</th>\n",
       "      <th>0</th>\n",
       "      <td>126.785792</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>40.318700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>126.785792</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>40.286800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>126.785792</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>40.327100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">10</th>\n",
       "      <th>0</th>\n",
       "      <td>158.482240</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>58.051000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>158.482240</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>58.168500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>158.482240</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>58.068100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">12</th>\n",
       "      <th>0</th>\n",
       "      <td>190.178688</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>67.803500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>190.178688</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>67.663000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>190.178688</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>69.536100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">14</th>\n",
       "      <th>0</th>\n",
       "      <td>221.875136</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>81.022400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>221.875136</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>81.185500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>221.875136</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>80.984100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">16</th>\n",
       "      <th>0</th>\n",
       "      <td>253.571584</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>79.594500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>253.571584</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>80.570500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>253.571584</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'tim...</td>\n",
       "      <td>yes</td>\n",
       "      <td>80.427800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"24\" valign=\"top\">tensorrt-fp16</th>\n",
       "      <th rowspan=\"3\" valign=\"top\">2</th>\n",
       "      <th>0</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'nam...</td>\n",
       "      <td>yes</td>\n",
       "      <td>5.377155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'nam...</td>\n",
       "      <td>yes</td>\n",
       "      <td>5.414945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'nam...</td>\n",
       "      <td>yes</td>\n",
       "      <td>5.411576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">4</th>\n",
       "      <th>0</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'nam...</td>\n",
       "      <td>yes</td>\n",
       "      <td>9.027626</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'nam...</td>\n",
       "      <td>yes</td>\n",
       "      <td>9.028205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'nam...</td>\n",
       "      <td>yes</td>\n",
       "      <td>8.962101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">6</th>\n",
       "      <th>0</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'nam...</td>\n",
       "      <td>yes</td>\n",
       "      <td>12.251047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'nam...</td>\n",
       "      <td>yes</td>\n",
       "      <td>12.367309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'nam...</td>\n",
       "      <td>yes</td>\n",
       "      <td>12.304204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">8</th>\n",
       "      <th>0</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'nam...</td>\n",
       "      <td>yes</td>\n",
       "      <td>15.868360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'nam...</td>\n",
       "      <td>yes</td>\n",
       "      <td>15.945623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'nam...</td>\n",
       "      <td>yes</td>\n",
       "      <td>15.835097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">10</th>\n",
       "      <th>0</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'nam...</td>\n",
       "      <td>yes</td>\n",
       "      <td>18.976411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'nam...</td>\n",
       "      <td>yes</td>\n",
       "      <td>18.924622</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'nam...</td>\n",
       "      <td>yes</td>\n",
       "      <td>19.173148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">12</th>\n",
       "      <th>0</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'nam...</td>\n",
       "      <td>yes</td>\n",
       "      <td>23.086462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'nam...</td>\n",
       "      <td>yes</td>\n",
       "      <td>23.001304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'nam...</td>\n",
       "      <td>yes</td>\n",
       "      <td>23.013410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">14</th>\n",
       "      <th>0</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'nam...</td>\n",
       "      <td>yes</td>\n",
       "      <td>26.214039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'nam...</td>\n",
       "      <td>yes</td>\n",
       "      <td>26.271934</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'nam...</td>\n",
       "      <td>yes</td>\n",
       "      <td>26.415460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">16</th>\n",
       "      <th>0</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'nam...</td>\n",
       "      <td>yes</td>\n",
       "      <td>30.113932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'nam...</td>\n",
       "      <td>yes</td>\n",
       "      <td>30.275458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'nam...</td>\n",
       "      <td>yes</td>\n",
       "      <td>30.134406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"24\" valign=\"top\">tensorrt-fp32</th>\n",
       "      <th rowspan=\"3\" valign=\"top\">2</th>\n",
       "      <th>0</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'nam...</td>\n",
       "      <td>yes</td>\n",
       "      <td>8.265153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'nam...</td>\n",
       "      <td>yes</td>\n",
       "      <td>8.238364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'nam...</td>\n",
       "      <td>yes</td>\n",
       "      <td>8.144259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">4</th>\n",
       "      <th>0</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'nam...</td>\n",
       "      <td>yes</td>\n",
       "      <td>15.685308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'nam...</td>\n",
       "      <td>yes</td>\n",
       "      <td>15.143676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'nam...</td>\n",
       "      <td>yes</td>\n",
       "      <td>15.116308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">6</th>\n",
       "      <th>0</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'nam...</td>\n",
       "      <td>yes</td>\n",
       "      <td>22.320673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'nam...</td>\n",
       "      <td>yes</td>\n",
       "      <td>22.175621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'nam...</td>\n",
       "      <td>yes</td>\n",
       "      <td>22.011515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">8</th>\n",
       "      <th>0</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'nam...</td>\n",
       "      <td>yes</td>\n",
       "      <td>29.065248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'nam...</td>\n",
       "      <td>yes</td>\n",
       "      <td>29.047458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'nam...</td>\n",
       "      <td>yes</td>\n",
       "      <td>29.676406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">10</th>\n",
       "      <th>0</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'nam...</td>\n",
       "      <td>yes</td>\n",
       "      <td>36.717139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'nam...</td>\n",
       "      <td>yes</td>\n",
       "      <td>36.331982</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'nam...</td>\n",
       "      <td>yes</td>\n",
       "      <td>36.023297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">12</th>\n",
       "      <th>0</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'nam...</td>\n",
       "      <td>yes</td>\n",
       "      <td>43.034188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'nam...</td>\n",
       "      <td>yes</td>\n",
       "      <td>43.000188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'nam...</td>\n",
       "      <td>yes</td>\n",
       "      <td>42.970767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">14</th>\n",
       "      <th>0</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'nam...</td>\n",
       "      <td>yes</td>\n",
       "      <td>49.826817</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'nam...</td>\n",
       "      <td>yes</td>\n",
       "      <td>49.500343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'nam...</td>\n",
       "      <td>yes</td>\n",
       "      <td>49.993396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">16</th>\n",
       "      <th>0</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'nam...</td>\n",
       "      <td>yes</td>\n",
       "      <td>57.256970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'nam...</td>\n",
       "      <td>yes</td>\n",
       "      <td>57.296445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[{u'index': 0, u'direction': u'forward', u'nam...</td>\n",
       "      <td>yes</td>\n",
       "      <td>58.138023</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "run characteristic                                                memory (MB)  \\\n",
       "model                    lib               batch size repetition                \n",
       "bvlc-alexnet             cpu               2          0             16.646488   \n",
       "                                                      1             16.646488   \n",
       "                                                      2             16.646488   \n",
       "                                           4          0             33.292976   \n",
       "                                                      1             33.292976   \n",
       "                                                      2             33.292976   \n",
       "                                           6          0             49.939464   \n",
       "                                                      1             49.939464   \n",
       "                                                      2             49.939464   \n",
       "                                           8          0             66.585952   \n",
       "                                                      1             66.585952   \n",
       "                                                      2             66.585952   \n",
       "                                           10         0             83.232440   \n",
       "                                                      1             83.232440   \n",
       "                                                      2             83.232440   \n",
       "                                           12         0             99.878928   \n",
       "                                                      1             99.878928   \n",
       "                                                      2             99.878928   \n",
       "                                           14         0            116.525416   \n",
       "                                                      1            116.525416   \n",
       "                                                      2            116.525416   \n",
       "                                           16         0            133.171904   \n",
       "                                                      1            133.171904   \n",
       "                                                      2            133.171904   \n",
       "                         libdnn-cuda       2          0             16.646488   \n",
       "                                                      1             16.646488   \n",
       "                                                      2             16.646488   \n",
       "                                           4          0             33.292976   \n",
       "                                                      1             33.292976   \n",
       "                                                      2             33.292976   \n",
       "                                           6          0             49.939464   \n",
       "                                                      1             49.939464   \n",
       "                                                      2             49.939464   \n",
       "                                           8          0             66.585952   \n",
       "                                                      1             66.585952   \n",
       "                                                      2             66.585952   \n",
       "                                           10         0             83.232440   \n",
       "                                                      1             83.232440   \n",
       "                                                      2             83.232440   \n",
       "                                           12         0             99.878928   \n",
       "                                                      1             99.878928   \n",
       "                                                      2             99.878928   \n",
       "                                           14         0            116.525416   \n",
       "                                                      1            116.525416   \n",
       "                                                      2            116.525416   \n",
       "                                           16         0            133.171904   \n",
       "                                                      1            133.171904   \n",
       "                                                      2            133.171904   \n",
       "                         nvidia-cuda       2          0             16.646488   \n",
       "                                                      1             16.646488   \n",
       "                                                      2             16.646488   \n",
       "                                           4          0             33.292976   \n",
       "                                                      1             33.292976   \n",
       "                                                      2             33.292976   \n",
       "                                           6          0             49.939464   \n",
       "                                                      1             49.939464   \n",
       "                                                      2             49.939464   \n",
       "                                           8          0             66.585952   \n",
       "                                                      1             66.585952   \n",
       "                                                      2             66.585952   \n",
       "                                           10         0             83.232440   \n",
       "                                                      1             83.232440   \n",
       "                                                      2             83.232440   \n",
       "                                           12         0             99.878928   \n",
       "                                                      1             99.878928   \n",
       "                                                      2             99.878928   \n",
       "                                           14         0            116.525416   \n",
       "                                                      1            116.525416   \n",
       "                                                      2            116.525416   \n",
       "                                           16         0            133.171904   \n",
       "                                                      1            133.171904   \n",
       "                                                      2            133.171904   \n",
       "                         nvidia-cudnn      2          0             16.646488   \n",
       "                                                      1             16.646488   \n",
       "                                                      2             16.646488   \n",
       "                                           4          0             33.292976   \n",
       "                                                      1             33.292976   \n",
       "                                                      2             33.292976   \n",
       "                                           6          0             49.939464   \n",
       "                                                      1             49.939464   \n",
       "                                                      2             49.939464   \n",
       "                                           8          0             66.585952   \n",
       "                                                      1             66.585952   \n",
       "                                                      2             66.585952   \n",
       "                                           10         0             83.232440   \n",
       "                                                      1             83.232440   \n",
       "                                                      2             83.232440   \n",
       "                                           12         0             99.878928   \n",
       "                                                      1             99.878928   \n",
       "                                                      2             99.878928   \n",
       "                                           14         0            116.525416   \n",
       "                                                      1            116.525416   \n",
       "                                                      2            116.525416   \n",
       "                                           16         0            133.171904   \n",
       "                                                      1            133.171904   \n",
       "                                                      2            133.171904   \n",
       "                         nvidia-fp16-cuda  2          0              7.704896   \n",
       "                                                      1              7.704896   \n",
       "                                                      2              7.704896   \n",
       "                                           4          0             15.409792   \n",
       "                                                      1             15.409792   \n",
       "                                                      2             15.409792   \n",
       "                                           6          0             23.114688   \n",
       "                                                      1             23.114688   \n",
       "                                                      2             23.114688   \n",
       "                                           8          0             30.819584   \n",
       "                                                      1             30.819584   \n",
       "                                                      2             30.819584   \n",
       "                                           10         0             38.524480   \n",
       "                                                      1             38.524480   \n",
       "                                                      2             38.524480   \n",
       "                                           12         0             46.229376   \n",
       "                                                      1             46.229376   \n",
       "                                                      2             46.229376   \n",
       "                                           14         0             53.934272   \n",
       "                                                      1             53.934272   \n",
       "                                                      2             53.934272   \n",
       "                                           16         0             61.639168   \n",
       "                                                      1             61.639168   \n",
       "                                                      2             61.639168   \n",
       "                         nvidia-fp16-cudnn 2          0              7.704896   \n",
       "                                                      1              7.704896   \n",
       "                                                      2              7.704896   \n",
       "                                           4          0             15.409792   \n",
       "                                                      1             15.409792   \n",
       "                                                      2             15.409792   \n",
       "                                           6          0             23.114688   \n",
       "                                                      1             23.114688   \n",
       "                                                      2             23.114688   \n",
       "                                           8          0             30.819584   \n",
       "                                                      1             30.819584   \n",
       "                                                      2             30.819584   \n",
       "                                           10         0             38.524480   \n",
       "                                                      1             38.524480   \n",
       "                                                      2             38.524480   \n",
       "                                           12         0             46.229376   \n",
       "                                                      1             46.229376   \n",
       "                                                      2             46.229376   \n",
       "                                           14         0             53.934272   \n",
       "                                                      1             53.934272   \n",
       "                                                      2             53.934272   \n",
       "                                           16         0             61.639168   \n",
       "                                                      1             61.639168   \n",
       "                                                      2             61.639168   \n",
       "                         tensorrt-fp16     2          0              0.000000   \n",
       "                                                      1              0.000000   \n",
       "                                                      2              0.000000   \n",
       "                                           4          0              0.000000   \n",
       "                                                      1              0.000000   \n",
       "                                                      2              0.000000   \n",
       "                                           6          0              0.000000   \n",
       "                                                      1              0.000000   \n",
       "                                                      2              0.000000   \n",
       "                                           8          0              0.000000   \n",
       "                                                      1              0.000000   \n",
       "                                                      2              0.000000   \n",
       "                                           10         0              0.000000   \n",
       "                                                      1              0.000000   \n",
       "                                                      2              0.000000   \n",
       "                                           12         0              0.000000   \n",
       "                                                      1              0.000000   \n",
       "                                                      2              0.000000   \n",
       "                                           14         0              0.000000   \n",
       "                                                      1              0.000000   \n",
       "                                                      2              0.000000   \n",
       "                                           16         0              0.000000   \n",
       "                                                      1              0.000000   \n",
       "                                                      2              0.000000   \n",
       "                         tensorrt-fp32     2          0              0.000000   \n",
       "                                                      1              0.000000   \n",
       "                                                      2              0.000000   \n",
       "                                           4          0              0.000000   \n",
       "                                                      1              0.000000   \n",
       "                                                      2              0.000000   \n",
       "                                           6          0              0.000000   \n",
       "                                                      1              0.000000   \n",
       "                                                      2              0.000000   \n",
       "                                           8          0              0.000000   \n",
       "                                                      1              0.000000   \n",
       "                                                      2              0.000000   \n",
       "                                           10         0              0.000000   \n",
       "                                                      1              0.000000   \n",
       "                                                      2              0.000000   \n",
       "                                           12         0              0.000000   \n",
       "                                                      1              0.000000   \n",
       "                                                      2              0.000000   \n",
       "                                           14         0              0.000000   \n",
       "                                                      1              0.000000   \n",
       "                                                      2              0.000000   \n",
       "                                           16         0              0.000000   \n",
       "                                                      1              0.000000   \n",
       "                                                      2              0.000000   \n",
       "bvlc-googlenet           cpu               2          0            110.306688   \n",
       "                                                      1            110.306688   \n",
       "                                                      2            110.306688   \n",
       "                                           4          0            220.613376   \n",
       "                                                      1            220.613376   \n",
       "                                                      2            220.613376   \n",
       "                                           6          0            330.920064   \n",
       "                                                      1            330.920064   \n",
       "                                                      2            330.920064   \n",
       "                                           8          0            441.226752   \n",
       "                                                      1            441.226752   \n",
       "                                                      2            441.226752   \n",
       "                                           10         0            551.533440   \n",
       "                                                      1            551.533440   \n",
       "                                                      2            551.533440   \n",
       "                                           12         0            661.840128   \n",
       "                                                      1            661.840128   \n",
       "                                                      2            661.840128   \n",
       "                                           14         0            772.146816   \n",
       "                                                      1            772.146816   \n",
       "                                                      2            772.146816   \n",
       "                                           16         0            882.453504   \n",
       "                                                      1            882.453504   \n",
       "                                                      2            882.453504   \n",
       "                         libdnn-cuda       2          0            110.306688   \n",
       "                                                      1            110.306688   \n",
       "                                                      2            110.306688   \n",
       "                                           4          0            220.613376   \n",
       "                                                      1            220.613376   \n",
       "                                                      2            220.613376   \n",
       "                                           6          0            330.920064   \n",
       "                                                      1            330.920064   \n",
       "                                                      2            330.920064   \n",
       "                                           8          0            441.226752   \n",
       "                                                      1            441.226752   \n",
       "                                                      2            441.226752   \n",
       "                                           10         0            551.533440   \n",
       "                                                      1            551.533440   \n",
       "                                                      2            551.533440   \n",
       "                                           12         0            661.840128   \n",
       "                                                      1            661.840128   \n",
       "                                                      2            661.840128   \n",
       "                                           14         0            772.146816   \n",
       "                                                      1            772.146816   \n",
       "                                                      2            772.146816   \n",
       "                                           16         0            882.453504   \n",
       "                                                      1            882.453504   \n",
       "                                                      2            882.453504   \n",
       "                         nvidia-cuda       2          0            110.306688   \n",
       "                                                      1            110.306688   \n",
       "                                                      2            110.306688   \n",
       "                                           4          0            220.613376   \n",
       "                                                      1            220.613376   \n",
       "                                                      2            220.613376   \n",
       "                                           6          0            330.920064   \n",
       "                                                      1            330.920064   \n",
       "                                                      2            330.920064   \n",
       "                                           8          0            441.226752   \n",
       "                                                      1            441.226752   \n",
       "                                                      2            441.226752   \n",
       "                                           10         0            551.533440   \n",
       "                                                      1            551.533440   \n",
       "                                                      2            551.533440   \n",
       "                                           12         0            661.840128   \n",
       "                                                      1            661.840128   \n",
       "                                                      2            661.840128   \n",
       "                                           14         0            772.146816   \n",
       "                                                      1            772.146816   \n",
       "                                                      2            772.146816   \n",
       "                                           16         0            882.453504   \n",
       "                                                      1            882.453504   \n",
       "                                                      2            882.453504   \n",
       "                         nvidia-cudnn      2          0            110.306688   \n",
       "                                                      1            110.306688   \n",
       "                                                      2            110.306688   \n",
       "                                           4          0            220.613376   \n",
       "                                                      1            220.613376   \n",
       "                                                      2            220.613376   \n",
       "                                           6          0            330.920064   \n",
       "                                                      1            330.920064   \n",
       "                                                      2            330.920064   \n",
       "                                           8          0            441.226752   \n",
       "                                                      1            441.226752   \n",
       "                                                      2            441.226752   \n",
       "                                           10         0            551.533440   \n",
       "                                                      1            551.533440   \n",
       "                                                      2            551.533440   \n",
       "                                           12         0            661.840128   \n",
       "                                                      1            661.840128   \n",
       "                                                      2            661.840128   \n",
       "                                           14         0            772.146816   \n",
       "                                                      1            772.146816   \n",
       "                                                      2            772.146816   \n",
       "                                           16         0            882.453504   \n",
       "                                                      1            882.453504   \n",
       "                                                      2            882.453504   \n",
       "                         nvidia-fp16-cuda  2          0             54.551232   \n",
       "                                                      1             54.551232   \n",
       "                                                      2             54.551232   \n",
       "                                           4          0            109.102464   \n",
       "                                                      1            109.102464   \n",
       "                                                      2            109.102464   \n",
       "                                           6          0            163.653696   \n",
       "                                                      1            163.653696   \n",
       "                                                      2            163.653696   \n",
       "                                           8          0            218.204928   \n",
       "                                                      1            218.204928   \n",
       "                                                      2            218.204928   \n",
       "                                           10         0            272.756160   \n",
       "                                                      1            272.756160   \n",
       "                                                      2            272.756160   \n",
       "                                           12         0            327.307392   \n",
       "                                                      1            327.307392   \n",
       "                                                      2            327.307392   \n",
       "                                           14         0            381.858624   \n",
       "                                                      1            381.858624   \n",
       "                                                      2            381.858624   \n",
       "                                           16         0            436.409856   \n",
       "                                                      1            436.409856   \n",
       "                                                      2            436.409856   \n",
       "                         nvidia-fp16-cudnn 2          0             54.551232   \n",
       "                                                      1             54.551232   \n",
       "                                                      2             54.551232   \n",
       "                                           4          0            109.102464   \n",
       "                                                      1            109.102464   \n",
       "                                                      2            109.102464   \n",
       "                                           6          0            163.653696   \n",
       "                                                      1            163.653696   \n",
       "                                                      2            163.653696   \n",
       "                                           8          0            218.204928   \n",
       "                                                      1            218.204928   \n",
       "                                                      2            218.204928   \n",
       "                                           10         0            272.756160   \n",
       "                                                      1            272.756160   \n",
       "                                                      2            272.756160   \n",
       "                                           12         0            327.307392   \n",
       "                                                      1            327.307392   \n",
       "                                                      2            327.307392   \n",
       "                                           14         0            381.858624   \n",
       "                                                      1            381.858624   \n",
       "                                                      2            381.858624   \n",
       "                                           16         0            436.409856   \n",
       "                                                      1            436.409856   \n",
       "                                                      2            436.409856   \n",
       "                         tensorrt-fp16     2          0              0.000000   \n",
       "                                                      1              0.000000   \n",
       "                                                      2              0.000000   \n",
       "                                           4          0              0.000000   \n",
       "                                                      1              0.000000   \n",
       "                                                      2              0.000000   \n",
       "                                           6          0              0.000000   \n",
       "                                                      1              0.000000   \n",
       "                                                      2              0.000000   \n",
       "                                           8          0              0.000000   \n",
       "                                                      1              0.000000   \n",
       "                                                      2              0.000000   \n",
       "                                           10         0              0.000000   \n",
       "                                                      1              0.000000   \n",
       "                                                      2              0.000000   \n",
       "                                           12         0              0.000000   \n",
       "                                                      1              0.000000   \n",
       "                                                      2              0.000000   \n",
       "                                           14         0              0.000000   \n",
       "                                                      1              0.000000   \n",
       "                                                      2              0.000000   \n",
       "                                           16         0              0.000000   \n",
       "                                                      1              0.000000   \n",
       "                                                      2              0.000000   \n",
       "                         tensorrt-fp32     2          0              0.000000   \n",
       "                                                      1              0.000000   \n",
       "                                                      2              0.000000   \n",
       "                                           4          0              0.000000   \n",
       "                                                      1              0.000000   \n",
       "                                                      2              0.000000   \n",
       "                                           6          0              0.000000   \n",
       "                                                      1              0.000000   \n",
       "                                                      2              0.000000   \n",
       "                                           8          0              0.000000   \n",
       "                                                      1              0.000000   \n",
       "                                                      2              0.000000   \n",
       "                                           10         0              0.000000   \n",
       "                                                      1              0.000000   \n",
       "                                                      2              0.000000   \n",
       "                                           12         0              0.000000   \n",
       "                                                      1              0.000000   \n",
       "                                                      2              0.000000   \n",
       "                                           14         0              0.000000   \n",
       "                                                      1              0.000000   \n",
       "                                                      2              0.000000   \n",
       "                                           16         0              0.000000   \n",
       "                                                      1              0.000000   \n",
       "                                                      2              0.000000   \n",
       "deepscale-squeezenet-1.0 cpu               2          0            106.120408   \n",
       "                                                      1            106.120408   \n",
       "                                                      2            106.120408   \n",
       "                                           4          0            212.240816   \n",
       "                                                      1            212.240816   \n",
       "                                                      2            212.240816   \n",
       "                                           6          0            318.361224   \n",
       "                                                      1            318.361224   \n",
       "                                                      2            318.361224   \n",
       "                                           8          0            424.481632   \n",
       "                                                      1            424.481632   \n",
       "                                                      2            424.481632   \n",
       "                                           10         0            530.602040   \n",
       "                                                      1            530.602040   \n",
       "                                                      2            530.602040   \n",
       "                                           12         0            636.722448   \n",
       "                                                      1            636.722448   \n",
       "                                                      2            636.722448   \n",
       "                                           14         0            742.842856   \n",
       "                                                      1            742.842856   \n",
       "                                                      2            742.842856   \n",
       "                                           16         0            848.963264   \n",
       "                                                      1            848.963264   \n",
       "                                                      2            848.963264   \n",
       "                         libdnn-cuda       2          0            106.120408   \n",
       "                                                      1            106.120408   \n",
       "                                                      2            106.120408   \n",
       "                                           4          0            212.240816   \n",
       "                                                      1            212.240816   \n",
       "                                                      2            212.240816   \n",
       "                                           6          0            318.361224   \n",
       "                                                      1            318.361224   \n",
       "                                                      2            318.361224   \n",
       "                                           8          0            424.481632   \n",
       "                                                      1            424.481632   \n",
       "                                                      2            424.481632   \n",
       "                                           10         0            530.602040   \n",
       "                                                      1            530.602040   \n",
       "                                                      2            530.602040   \n",
       "                                           12         0            636.722448   \n",
       "                                                      1            636.722448   \n",
       "                                                      2            636.722448   \n",
       "                                           14         0            742.842856   \n",
       "                                                      1            742.842856   \n",
       "                                                      2            742.842856   \n",
       "                                           16         0            848.963264   \n",
       "                                                      1            848.963264   \n",
       "                                                      2            848.963264   \n",
       "                         nvidia-cuda       2          0            106.120408   \n",
       "                                                      1            106.120408   \n",
       "                                                      2            106.120408   \n",
       "                                           4          0            212.240816   \n",
       "                                                      1            212.240816   \n",
       "                                                      2            212.240816   \n",
       "                                           6          0            318.361224   \n",
       "                                                      1            318.361224   \n",
       "                                                      2            318.361224   \n",
       "                                           8          0            424.481632   \n",
       "                                                      1            424.481632   \n",
       "                                                      2            424.481632   \n",
       "                                           10         0            530.602040   \n",
       "                                                      1            530.602040   \n",
       "                                                      2            530.602040   \n",
       "                                           12         0            636.722448   \n",
       "                                                      1            636.722448   \n",
       "                                                      2            636.722448   \n",
       "                                           14         0            742.842856   \n",
       "                                                      1            742.842856   \n",
       "                                                      2            742.842856   \n",
       "                                           16         0            848.963264   \n",
       "                                                      1            848.963264   \n",
       "                                                      2            848.963264   \n",
       "                         nvidia-cudnn      2          0            106.120408   \n",
       "                                                      1            106.120408   \n",
       "                                                      2            106.120408   \n",
       "                                           4          0            212.240816   \n",
       "                                                      1            212.240816   \n",
       "                                                      2            212.240816   \n",
       "                                           6          0            318.361224   \n",
       "                                                      1            318.361224   \n",
       "                                                      2            318.361224   \n",
       "                                           8          0            424.481632   \n",
       "                                                      1            424.481632   \n",
       "                                                      2            424.481632   \n",
       "                                           10         0            530.602040   \n",
       "                                                      1            530.602040   \n",
       "                                                      2            530.602040   \n",
       "                                           12         0            636.722448   \n",
       "                                                      1            636.722448   \n",
       "                                                      2            636.722448   \n",
       "                                           14         0            742.842856   \n",
       "                                                      1            742.842856   \n",
       "                                                      2            742.842856   \n",
       "                                           16         0            848.963264   \n",
       "                                                      1            848.963264   \n",
       "                                                      2            848.963264   \n",
       "                         nvidia-fp16-cuda  2          0             52.441856   \n",
       "                                                      1             52.441856   \n",
       "                                                      2             52.441856   \n",
       "                                           4          0            104.883712   \n",
       "                                                      1            104.883712   \n",
       "                                                      2            104.883712   \n",
       "                                           6          0            157.325568   \n",
       "                                                      1            157.325568   \n",
       "                                                      2            157.325568   \n",
       "                                           8          0            209.767424   \n",
       "                                                      1            209.767424   \n",
       "                                                      2            209.767424   \n",
       "                                           10         0            262.209280   \n",
       "                                                      1            262.209280   \n",
       "                                                      2            262.209280   \n",
       "                                           12         0            314.651136   \n",
       "                                                      1            314.651136   \n",
       "                                                      2            314.651136   \n",
       "                                           14         0            367.092992   \n",
       "                                                      1            367.092992   \n",
       "                                                      2            367.092992   \n",
       "                                           16         0            419.534848   \n",
       "                                                      1            419.534848   \n",
       "                                                      2            419.534848   \n",
       "                         nvidia-fp16-cudnn 2          0             52.441856   \n",
       "                                                      1             52.441856   \n",
       "                                                      2             52.441856   \n",
       "                                           4          0            104.883712   \n",
       "                                                      1            104.883712   \n",
       "                                                      2            104.883712   \n",
       "                                           6          0            157.325568   \n",
       "                                                      1            157.325568   \n",
       "                                                      2            157.325568   \n",
       "                                           8          0            209.767424   \n",
       "                                                      1            209.767424   \n",
       "                                                      2            209.767424   \n",
       "                                           10         0            262.209280   \n",
       "                                                      1            262.209280   \n",
       "                                                      2            262.209280   \n",
       "                                           12         0            314.651136   \n",
       "                                                      1            314.651136   \n",
       "                                                      2            314.651136   \n",
       "                                           14         0            367.092992   \n",
       "                                                      1            367.092992   \n",
       "                                                      2            367.092992   \n",
       "                                           16         0            419.534848   \n",
       "                                                      1            419.534848   \n",
       "                                                      2            419.534848   \n",
       "                         tensorrt-fp16     2          0              0.000000   \n",
       "                                                      1              0.000000   \n",
       "                                                      2              0.000000   \n",
       "                                           4          0              0.000000   \n",
       "                                                      1              0.000000   \n",
       "                                                      2              0.000000   \n",
       "                                           6          0              0.000000   \n",
       "                                                      1              0.000000   \n",
       "                                                      2              0.000000   \n",
       "                                           8          0              0.000000   \n",
       "                                                      1              0.000000   \n",
       "                                                      2              0.000000   \n",
       "                                           10         0              0.000000   \n",
       "                                                      1              0.000000   \n",
       "                                                      2              0.000000   \n",
       "                                           12         0              0.000000   \n",
       "                                                      1              0.000000   \n",
       "                                                      2              0.000000   \n",
       "                                           14         0              0.000000   \n",
       "                                                      1              0.000000   \n",
       "                                                      2              0.000000   \n",
       "                                           16         0              0.000000   \n",
       "                                                      1              0.000000   \n",
       "                                                      2              0.000000   \n",
       "                         tensorrt-fp32     2          0              0.000000   \n",
       "                                                      1              0.000000   \n",
       "                                                      2              0.000000   \n",
       "                                           4          0              0.000000   \n",
       "                                                      1              0.000000   \n",
       "                                                      2              0.000000   \n",
       "                                           6          0              0.000000   \n",
       "                                                      1              0.000000   \n",
       "                                                      2              0.000000   \n",
       "                                           8          0              0.000000   \n",
       "                                                      1              0.000000   \n",
       "                                                      2              0.000000   \n",
       "                                           10         0              0.000000   \n",
       "                                                      1              0.000000   \n",
       "                                                      2              0.000000   \n",
       "                                           12         0              0.000000   \n",
       "                                                      1              0.000000   \n",
       "                                                      2              0.000000   \n",
       "                                           14         0              0.000000   \n",
       "                                                      1              0.000000   \n",
       "                                                      2              0.000000   \n",
       "                                           16         0              0.000000   \n",
       "                                                      1              0.000000   \n",
       "                                                      2              0.000000   \n",
       "deepscale-squeezenet-1.1 cpu               2          0             64.629592   \n",
       "                                                      1             64.629592   \n",
       "                                                      2             64.629592   \n",
       "                                           4          0            129.259184   \n",
       "                                                      1            129.259184   \n",
       "                                                      2            129.259184   \n",
       "                                           6          0            193.888776   \n",
       "                                                      1            193.888776   \n",
       "                                                      2            193.888776   \n",
       "                                           8          0            258.518368   \n",
       "                                                      1            258.518368   \n",
       "                                                      2            258.518368   \n",
       "                                           10         0            323.147960   \n",
       "                                                      1            323.147960   \n",
       "                                                      2            323.147960   \n",
       "                                           12         0            387.777552   \n",
       "                                                      1            387.777552   \n",
       "                                                      2            387.777552   \n",
       "                                           14         0            452.407144   \n",
       "                                                      1            452.407144   \n",
       "                                                      2            452.407144   \n",
       "                                           16         0            517.036736   \n",
       "                                                      1            517.036736   \n",
       "                                                      2            517.036736   \n",
       "                         libdnn-cuda       2          0             64.629592   \n",
       "                                                      1             64.629592   \n",
       "                                                      2             64.629592   \n",
       "                                           4          0            129.259184   \n",
       "                                                      1            129.259184   \n",
       "                                                      2            129.259184   \n",
       "                                           6          0            193.888776   \n",
       "                                                      1            193.888776   \n",
       "                                                      2            193.888776   \n",
       "                                           8          0            258.518368   \n",
       "                                                      1            258.518368   \n",
       "                                                      2            258.518368   \n",
       "                                           10         0            323.147960   \n",
       "                                                      1            323.147960   \n",
       "                                                      2            323.147960   \n",
       "                                           12         0            387.777552   \n",
       "                                                      1            387.777552   \n",
       "                                                      2            387.777552   \n",
       "                                           14         0            452.407144   \n",
       "                                                      1            452.407144   \n",
       "                                                      2            452.407144   \n",
       "                                           16         0            517.036736   \n",
       "                                                      1            517.036736   \n",
       "                                                      2            517.036736   \n",
       "                         nvidia-cuda       2          0             64.629592   \n",
       "                                                      1             64.629592   \n",
       "                                                      2             64.629592   \n",
       "                                           4          0            129.259184   \n",
       "                                                      1            129.259184   \n",
       "                                                      2            129.259184   \n",
       "                                           6          0            193.888776   \n",
       "                                                      1            193.888776   \n",
       "                                                      2            193.888776   \n",
       "                                           8          0            258.518368   \n",
       "                                                      1            258.518368   \n",
       "                                                      2            258.518368   \n",
       "                                           10         0            323.147960   \n",
       "                                                      1            323.147960   \n",
       "                                                      2            323.147960   \n",
       "                                           12         0            387.777552   \n",
       "                                                      1            387.777552   \n",
       "                                                      2            387.777552   \n",
       "                                           14         0            452.407144   \n",
       "                                                      1            452.407144   \n",
       "                                                      2            452.407144   \n",
       "                                           16         0            517.036736   \n",
       "                                                      1            517.036736   \n",
       "                                                      2            517.036736   \n",
       "                         nvidia-cudnn      2          0             64.629592   \n",
       "                                                      1             64.629592   \n",
       "                                                      2             64.629592   \n",
       "                                           4          0            129.259184   \n",
       "                                                      1            129.259184   \n",
       "                                                      2            129.259184   \n",
       "                                           6          0            193.888776   \n",
       "                                                      1            193.888776   \n",
       "                                                      2            193.888776   \n",
       "                                           8          0            258.518368   \n",
       "                                                      1            258.518368   \n",
       "                                                      2            258.518368   \n",
       "                                           10         0            323.147960   \n",
       "                                                      1            323.147960   \n",
       "                                                      2            323.147960   \n",
       "                                           12         0            387.777552   \n",
       "                                                      1            387.777552   \n",
       "                                                      2            387.777552   \n",
       "                                           14         0            452.407144   \n",
       "                                                      1            452.407144   \n",
       "                                                      2            452.407144   \n",
       "                                           16         0            517.036736   \n",
       "                                                      1            517.036736   \n",
       "                                                      2            517.036736   \n",
       "                         nvidia-fp16-cuda  2          0             31.696448   \n",
       "                                                      1             31.696448   \n",
       "                                                      2             31.696448   \n",
       "                                           4          0             63.392896   \n",
       "                                                      1             63.392896   \n",
       "                                                      2             63.392896   \n",
       "                                           6          0             95.089344   \n",
       "                                                      1             95.089344   \n",
       "                                                      2             95.089344   \n",
       "                                           8          0            126.785792   \n",
       "                                                      1            126.785792   \n",
       "                                                      2            126.785792   \n",
       "                                           10         0            158.482240   \n",
       "                                                      1            158.482240   \n",
       "                                                      2            158.482240   \n",
       "                                           12         0            190.178688   \n",
       "                                                      1            190.178688   \n",
       "                                                      2            190.178688   \n",
       "                                           14         0            221.875136   \n",
       "                                                      1            221.875136   \n",
       "                                                      2            221.875136   \n",
       "                                           16         0            253.571584   \n",
       "                                                      1            253.571584   \n",
       "                                                      2            253.571584   \n",
       "                         nvidia-fp16-cudnn 2          0             31.696448   \n",
       "                                                      1             31.696448   \n",
       "                                                      2             31.696448   \n",
       "                                           4          0             63.392896   \n",
       "                                                      1             63.392896   \n",
       "                                                      2             63.392896   \n",
       "                                           6          0             95.089344   \n",
       "                                                      1             95.089344   \n",
       "                                                      2             95.089344   \n",
       "                                           8          0            126.785792   \n",
       "                                                      1            126.785792   \n",
       "                                                      2            126.785792   \n",
       "                                           10         0            158.482240   \n",
       "                                                      1            158.482240   \n",
       "                                                      2            158.482240   \n",
       "                                           12         0            190.178688   \n",
       "                                                      1            190.178688   \n",
       "                                                      2            190.178688   \n",
       "                                           14         0            221.875136   \n",
       "                                                      1            221.875136   \n",
       "                                                      2            221.875136   \n",
       "                                           16         0            253.571584   \n",
       "                                                      1            253.571584   \n",
       "                                                      2            253.571584   \n",
       "                         tensorrt-fp16     2          0              0.000000   \n",
       "                                                      1              0.000000   \n",
       "                                                      2              0.000000   \n",
       "                                           4          0              0.000000   \n",
       "                                                      1              0.000000   \n",
       "                                                      2              0.000000   \n",
       "                                           6          0              0.000000   \n",
       "                                                      1              0.000000   \n",
       "                                                      2              0.000000   \n",
       "                                           8          0              0.000000   \n",
       "                                                      1              0.000000   \n",
       "                                                      2              0.000000   \n",
       "                                           10         0              0.000000   \n",
       "                                                      1              0.000000   \n",
       "                                                      2              0.000000   \n",
       "                                           12         0              0.000000   \n",
       "                                                      1              0.000000   \n",
       "                                                      2              0.000000   \n",
       "                                           14         0              0.000000   \n",
       "                                                      1              0.000000   \n",
       "                                                      2              0.000000   \n",
       "                                           16         0              0.000000   \n",
       "                                                      1              0.000000   \n",
       "                                                      2              0.000000   \n",
       "                         tensorrt-fp32     2          0              0.000000   \n",
       "                                                      1              0.000000   \n",
       "                                                      2              0.000000   \n",
       "                                           4          0              0.000000   \n",
       "                                                      1              0.000000   \n",
       "                                                      2              0.000000   \n",
       "                                           6          0              0.000000   \n",
       "                                                      1              0.000000   \n",
       "                                                      2              0.000000   \n",
       "                                           8          0              0.000000   \n",
       "                                                      1              0.000000   \n",
       "                                                      2              0.000000   \n",
       "                                           10         0              0.000000   \n",
       "                                                      1              0.000000   \n",
       "                                                      2              0.000000   \n",
       "                                           12         0              0.000000   \n",
       "                                                      1              0.000000   \n",
       "                                                      2              0.000000   \n",
       "                                           14         0              0.000000   \n",
       "                                                      1              0.000000   \n",
       "                                                      2              0.000000   \n",
       "                                           16         0              0.000000   \n",
       "                                                      1              0.000000   \n",
       "                                                      2              0.000000   \n",
       "\n",
       "run characteristic                                                                                   per layer info  \\\n",
       "model                    lib               batch size repetition                                                      \n",
       "bvlc-alexnet             cpu               2          0           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                           4          0           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                           6          0           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                           8          0           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                           10         0           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                           12         0           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                           14         0           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                           16         0           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                         libdnn-cuda       2          0           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                           4          0           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                           6          0           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                           8          0           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                           10         0           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                           12         0           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                           14         0           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                           16         0           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                         nvidia-cuda       2          0           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                           4          0           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                           6          0           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                           8          0           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                           10         0           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                           12         0           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                           14         0           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                           16         0           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                         nvidia-cudnn      2          0           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                           4          0           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                           6          0           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                           8          0           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                           10         0           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                           12         0           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                           14         0           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                           16         0           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                         nvidia-fp16-cuda  2          0           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                           4          0           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                           6          0           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                           8          0           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                           10         0           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                           12         0           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                           14         0           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                           16         0           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                         nvidia-fp16-cudnn 2          0           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                           4          0           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                           6          0           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                           8          0           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                           10         0           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                           12         0           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                           14         0           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                           16         0           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                         tensorrt-fp16     2          0           [{u'index': 0, u'direction': u'forward', u'nam...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'nam...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'nam...   \n",
       "                                           4          0           [{u'index': 0, u'direction': u'forward', u'nam...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'nam...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'nam...   \n",
       "                                           6          0           [{u'index': 0, u'direction': u'forward', u'nam...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'nam...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'nam...   \n",
       "                                           8          0           [{u'index': 0, u'direction': u'forward', u'nam...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'nam...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'nam...   \n",
       "                                           10         0           [{u'index': 0, u'direction': u'forward', u'nam...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'nam...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'nam...   \n",
       "                                           12         0           [{u'index': 0, u'direction': u'forward', u'nam...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'nam...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'nam...   \n",
       "                                           14         0           [{u'index': 0, u'direction': u'forward', u'nam...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'nam...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'nam...   \n",
       "                                           16         0           [{u'index': 0, u'direction': u'forward', u'nam...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'nam...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'nam...   \n",
       "                         tensorrt-fp32     2          0           [{u'index': 0, u'direction': u'forward', u'nam...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'nam...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'nam...   \n",
       "                                           4          0           [{u'index': 0, u'direction': u'forward', u'nam...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'nam...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'nam...   \n",
       "                                           6          0           [{u'index': 0, u'direction': u'forward', u'nam...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'nam...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'nam...   \n",
       "                                           8          0           [{u'index': 0, u'direction': u'forward', u'nam...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'nam...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'nam...   \n",
       "                                           10         0           [{u'index': 0, u'direction': u'forward', u'nam...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'nam...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'nam...   \n",
       "                                           12         0           [{u'index': 0, u'direction': u'forward', u'nam...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'nam...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'nam...   \n",
       "                                           14         0           [{u'index': 0, u'direction': u'forward', u'nam...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'nam...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'nam...   \n",
       "                                           16         0           [{u'index': 0, u'direction': u'forward', u'nam...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'nam...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'nam...   \n",
       "bvlc-googlenet           cpu               2          0           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                           4          0           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                           6          0           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                           8          0           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                           10         0           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                           12         0           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                           14         0           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                           16         0           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                         libdnn-cuda       2          0           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                           4          0           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                           6          0           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                           8          0           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                           10         0           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                           12         0           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                           14         0           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                           16         0           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                         nvidia-cuda       2          0           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                           4          0           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                           6          0           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                           8          0           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                           10         0           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                           12         0           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                           14         0           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                           16         0           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                         nvidia-cudnn      2          0           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                           4          0           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                           6          0           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                           8          0           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                           10         0           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                           12         0           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                           14         0           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                           16         0           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                         nvidia-fp16-cuda  2          0           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                           4          0           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                           6          0           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                           8          0           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                           10         0           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                           12         0           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                           14         0           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                           16         0           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                         nvidia-fp16-cudnn 2          0           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                           4          0           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                           6          0           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                           8          0           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                           10         0           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                           12         0           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                           14         0           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                           16         0           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                         tensorrt-fp16     2          0           [{u'index': 0, u'direction': u'forward', u'nam...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'nam...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'nam...   \n",
       "                                           4          0           [{u'index': 0, u'direction': u'forward', u'nam...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'nam...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'nam...   \n",
       "                                           6          0           [{u'index': 0, u'direction': u'forward', u'nam...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'nam...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'nam...   \n",
       "                                           8          0           [{u'index': 0, u'direction': u'forward', u'nam...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'nam...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'nam...   \n",
       "                                           10         0           [{u'index': 0, u'direction': u'forward', u'nam...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'nam...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'nam...   \n",
       "                                           12         0           [{u'index': 0, u'direction': u'forward', u'nam...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'nam...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'nam...   \n",
       "                                           14         0           [{u'index': 0, u'direction': u'forward', u'nam...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'nam...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'nam...   \n",
       "                                           16         0           [{u'index': 0, u'direction': u'forward', u'nam...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'nam...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'nam...   \n",
       "                         tensorrt-fp32     2          0           [{u'index': 0, u'direction': u'forward', u'nam...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'nam...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'nam...   \n",
       "                                           4          0           [{u'index': 0, u'direction': u'forward', u'nam...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'nam...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'nam...   \n",
       "                                           6          0           [{u'index': 0, u'direction': u'forward', u'nam...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'nam...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'nam...   \n",
       "                                           8          0           [{u'index': 0, u'direction': u'forward', u'nam...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'nam...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'nam...   \n",
       "                                           10         0           [{u'index': 0, u'direction': u'forward', u'nam...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'nam...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'nam...   \n",
       "                                           12         0           [{u'index': 0, u'direction': u'forward', u'nam...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'nam...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'nam...   \n",
       "                                           14         0           [{u'index': 0, u'direction': u'forward', u'nam...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'nam...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'nam...   \n",
       "                                           16         0           [{u'index': 0, u'direction': u'forward', u'nam...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'nam...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'nam...   \n",
       "deepscale-squeezenet-1.0 cpu               2          0           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                           4          0           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                           6          0           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                           8          0           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                           10         0           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                           12         0           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                           14         0           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                           16         0           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                         libdnn-cuda       2          0           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                           4          0           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                           6          0           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                           8          0           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                           10         0           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                           12         0           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                           14         0           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                           16         0           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                         nvidia-cuda       2          0           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                           4          0           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                           6          0           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                           8          0           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                           10         0           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                           12         0           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                           14         0           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                           16         0           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                         nvidia-cudnn      2          0           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                           4          0           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                           6          0           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                           8          0           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                           10         0           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                           12         0           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                           14         0           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                           16         0           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                         nvidia-fp16-cuda  2          0           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                           4          0           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                           6          0           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                           8          0           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                           10         0           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                           12         0           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                           14         0           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                           16         0           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                         nvidia-fp16-cudnn 2          0           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                           4          0           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                           6          0           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                           8          0           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                           10         0           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                           12         0           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                           14         0           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                           16         0           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                         tensorrt-fp16     2          0           [{u'index': 0, u'direction': u'forward', u'nam...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'nam...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'nam...   \n",
       "                                           4          0           [{u'index': 0, u'direction': u'forward', u'nam...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'nam...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'nam...   \n",
       "                                           6          0           [{u'index': 0, u'direction': u'forward', u'nam...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'nam...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'nam...   \n",
       "                                           8          0           [{u'index': 0, u'direction': u'forward', u'nam...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'nam...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'nam...   \n",
       "                                           10         0           [{u'index': 0, u'direction': u'forward', u'nam...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'nam...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'nam...   \n",
       "                                           12         0           [{u'index': 0, u'direction': u'forward', u'nam...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'nam...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'nam...   \n",
       "                                           14         0           [{u'index': 0, u'direction': u'forward', u'nam...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'nam...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'nam...   \n",
       "                                           16         0           [{u'index': 0, u'direction': u'forward', u'nam...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'nam...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'nam...   \n",
       "                         tensorrt-fp32     2          0           [{u'index': 0, u'direction': u'forward', u'nam...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'nam...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'nam...   \n",
       "                                           4          0           [{u'index': 0, u'direction': u'forward', u'nam...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'nam...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'nam...   \n",
       "                                           6          0           [{u'index': 0, u'direction': u'forward', u'nam...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'nam...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'nam...   \n",
       "                                           8          0           [{u'index': 0, u'direction': u'forward', u'nam...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'nam...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'nam...   \n",
       "                                           10         0           [{u'index': 0, u'direction': u'forward', u'nam...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'nam...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'nam...   \n",
       "                                           12         0           [{u'index': 0, u'direction': u'forward', u'nam...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'nam...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'nam...   \n",
       "                                           14         0           [{u'index': 0, u'direction': u'forward', u'nam...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'nam...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'nam...   \n",
       "                                           16         0           [{u'index': 0, u'direction': u'forward', u'nam...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'nam...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'nam...   \n",
       "deepscale-squeezenet-1.1 cpu               2          0           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                           4          0           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                           6          0           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                           8          0           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                           10         0           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                           12         0           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                           14         0           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                           16         0           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                         libdnn-cuda       2          0           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                           4          0           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                           6          0           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                           8          0           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                           10         0           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                           12         0           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                           14         0           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                           16         0           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                         nvidia-cuda       2          0           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                           4          0           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                           6          0           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                           8          0           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                           10         0           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                           12         0           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                           14         0           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                           16         0           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                         nvidia-cudnn      2          0           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                           4          0           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                           6          0           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                           8          0           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                           10         0           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                           12         0           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                           14         0           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                           16         0           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                         nvidia-fp16-cuda  2          0           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                           4          0           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                           6          0           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                           8          0           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                           10         0           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                           12         0           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                           14         0           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                           16         0           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                         nvidia-fp16-cudnn 2          0           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                           4          0           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                           6          0           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                           8          0           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                           10         0           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                           12         0           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                           14         0           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                           16         0           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'tim...   \n",
       "                         tensorrt-fp16     2          0           [{u'index': 0, u'direction': u'forward', u'nam...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'nam...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'nam...   \n",
       "                                           4          0           [{u'index': 0, u'direction': u'forward', u'nam...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'nam...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'nam...   \n",
       "                                           6          0           [{u'index': 0, u'direction': u'forward', u'nam...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'nam...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'nam...   \n",
       "                                           8          0           [{u'index': 0, u'direction': u'forward', u'nam...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'nam...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'nam...   \n",
       "                                           10         0           [{u'index': 0, u'direction': u'forward', u'nam...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'nam...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'nam...   \n",
       "                                           12         0           [{u'index': 0, u'direction': u'forward', u'nam...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'nam...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'nam...   \n",
       "                                           14         0           [{u'index': 0, u'direction': u'forward', u'nam...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'nam...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'nam...   \n",
       "                                           16         0           [{u'index': 0, u'direction': u'forward', u'nam...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'nam...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'nam...   \n",
       "                         tensorrt-fp32     2          0           [{u'index': 0, u'direction': u'forward', u'nam...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'nam...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'nam...   \n",
       "                                           4          0           [{u'index': 0, u'direction': u'forward', u'nam...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'nam...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'nam...   \n",
       "                                           6          0           [{u'index': 0, u'direction': u'forward', u'nam...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'nam...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'nam...   \n",
       "                                           8          0           [{u'index': 0, u'direction': u'forward', u'nam...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'nam...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'nam...   \n",
       "                                           10         0           [{u'index': 0, u'direction': u'forward', u'nam...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'nam...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'nam...   \n",
       "                                           12         0           [{u'index': 0, u'direction': u'forward', u'nam...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'nam...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'nam...   \n",
       "                                           14         0           [{u'index': 0, u'direction': u'forward', u'nam...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'nam...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'nam...   \n",
       "                                           16         0           [{u'index': 0, u'direction': u'forward', u'nam...   \n",
       "                                                      1           [{u'index': 0, u'direction': u'forward', u'nam...   \n",
       "                                                      2           [{u'index': 0, u'direction': u'forward', u'nam...   \n",
       "\n",
       "run characteristic                                               success?  \\\n",
       "model                    lib               batch size repetition            \n",
       "bvlc-alexnet             cpu               2          0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           4          0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           6          0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           8          0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           10         0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           12         0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           14         0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           16         0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                         libdnn-cuda       2          0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           4          0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           6          0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           8          0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           10         0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           12         0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           14         0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           16         0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                         nvidia-cuda       2          0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           4          0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           6          0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           8          0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           10         0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           12         0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           14         0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           16         0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                         nvidia-cudnn      2          0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           4          0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           6          0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           8          0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           10         0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           12         0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           14         0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           16         0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                         nvidia-fp16-cuda  2          0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           4          0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           6          0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           8          0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           10         0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           12         0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           14         0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           16         0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                         nvidia-fp16-cudnn 2          0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           4          0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           6          0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           8          0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           10         0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           12         0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           14         0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           16         0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                         tensorrt-fp16     2          0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           4          0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           6          0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           8          0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           10         0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           12         0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           14         0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           16         0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                         tensorrt-fp32     2          0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           4          0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           6          0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           8          0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           10         0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           12         0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           14         0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           16         0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "bvlc-googlenet           cpu               2          0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           4          0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           6          0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           8          0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           10         0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           12         0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           14         0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           16         0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                         libdnn-cuda       2          0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           4          0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           6          0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           8          0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           10         0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           12         0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           14         0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           16         0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                         nvidia-cuda       2          0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           4          0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           6          0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           8          0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           10         0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           12         0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           14         0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           16         0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                         nvidia-cudnn      2          0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           4          0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           6          0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           8          0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           10         0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           12         0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           14         0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           16         0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                         nvidia-fp16-cuda  2          0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           4          0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           6          0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           8          0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           10         0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           12         0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           14         0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           16         0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                         nvidia-fp16-cudnn 2          0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           4          0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           6          0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           8          0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           10         0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           12         0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           14         0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           16         0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                         tensorrt-fp16     2          0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           4          0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           6          0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           8          0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           10         0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           12         0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           14         0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           16         0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                         tensorrt-fp32     2          0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           4          0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           6          0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           8          0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           10         0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           12         0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           14         0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           16         0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "deepscale-squeezenet-1.0 cpu               2          0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           4          0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           6          0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           8          0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           10         0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           12         0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           14         0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           16         0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                         libdnn-cuda       2          0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           4          0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           6          0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           8          0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           10         0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           12         0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           14         0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           16         0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                         nvidia-cuda       2          0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           4          0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           6          0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           8          0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           10         0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           12         0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           14         0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           16         0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                         nvidia-cudnn      2          0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           4          0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           6          0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           8          0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           10         0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           12         0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           14         0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           16         0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                         nvidia-fp16-cuda  2          0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           4          0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           6          0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           8          0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           10         0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           12         0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           14         0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           16         0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                         nvidia-fp16-cudnn 2          0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           4          0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           6          0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           8          0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           10         0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           12         0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           14         0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           16         0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                         tensorrt-fp16     2          0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           4          0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           6          0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           8          0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           10         0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           12         0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           14         0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           16         0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                         tensorrt-fp32     2          0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           4          0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           6          0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           8          0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           10         0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           12         0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           14         0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           16         0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "deepscale-squeezenet-1.1 cpu               2          0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           4          0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           6          0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           8          0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           10         0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           12         0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           14         0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           16         0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                         libdnn-cuda       2          0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           4          0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           6          0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           8          0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           10         0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           12         0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           14         0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           16         0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                         nvidia-cuda       2          0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           4          0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           6          0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           8          0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           10         0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           12         0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           14         0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           16         0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                         nvidia-cudnn      2          0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           4          0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           6          0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           8          0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           10         0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           12         0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           14         0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           16         0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                         nvidia-fp16-cuda  2          0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           4          0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           6          0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           8          0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           10         0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           12         0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           14         0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           16         0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                         nvidia-fp16-cudnn 2          0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           4          0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           6          0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           8          0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           10         0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           12         0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           14         0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           16         0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                         tensorrt-fp16     2          0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           4          0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           6          0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           8          0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           10         0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           12         0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           14         0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           16         0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                         tensorrt-fp32     2          0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           4          0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           6          0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           8          0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           10         0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           12         0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           14         0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "                                           16         0               yes   \n",
       "                                                      1               yes   \n",
       "                                                      2               yes   \n",
       "\n",
       "run characteristic                                                  time (ms)  \n",
       "model                    lib               batch size repetition               \n",
       "bvlc-alexnet             cpu               2          0            429.404000  \n",
       "                                                      1            432.713000  \n",
       "                                                      2            438.301000  \n",
       "                                           4          0            657.945000  \n",
       "                                                      1            659.096000  \n",
       "                                                      2            654.448000  \n",
       "                                           6          0            926.569000  \n",
       "                                                      1            923.892000  \n",
       "                                                      2            927.562000  \n",
       "                                           8          0           1058.140000  \n",
       "                                                      1           1052.840000  \n",
       "                                                      2           1050.140000  \n",
       "                                           10         0           1293.970000  \n",
       "                                                      1           1279.210000  \n",
       "                                                      2           1294.980000  \n",
       "                                           12         0           1530.730000  \n",
       "                                                      1           1518.820000  \n",
       "                                                      2           1527.610000  \n",
       "                                           14         0           1768.870000  \n",
       "                                                      1           1750.410000  \n",
       "                                                      2           1767.410000  \n",
       "                                           16         0           1983.670000  \n",
       "                                                      1           2001.910000  \n",
       "                                                      2           1982.130000  \n",
       "                         libdnn-cuda       2          0             43.455800  \n",
       "                                                      1             45.209400  \n",
       "                                                      2             45.086200  \n",
       "                                           4          0             65.658900  \n",
       "                                                      1             65.170500  \n",
       "                                                      2             65.972100  \n",
       "                                           6          0             93.980300  \n",
       "                                                      1             93.099900  \n",
       "                                                      2             91.359400  \n",
       "                                           8          0            120.833000  \n",
       "                                                      1            118.322000  \n",
       "                                                      2            118.151000  \n",
       "                                           10         0            147.654000  \n",
       "                                                      1            147.449000  \n",
       "                                                      2            149.036000  \n",
       "                                           12         0            179.296000  \n",
       "                                                      1            177.986000  \n",
       "                                                      2            177.760000  \n",
       "                                           14         0            208.372000  \n",
       "                                                      1            208.853000  \n",
       "                                                      2            208.871000  \n",
       "                                           16         0            233.930000  \n",
       "                                                      1            232.266000  \n",
       "                                                      2            235.624000  \n",
       "                         nvidia-cuda       2          0             32.655300  \n",
       "                                                      1             34.419300  \n",
       "                                                      2             32.973400  \n",
       "                                           4          0             44.510100  \n",
       "                                                      1             44.684900  \n",
       "                                                      2             47.259100  \n",
       "                                           6          0             63.047100  \n",
       "                                                      1             62.730400  \n",
       "                                                      2             62.857800  \n",
       "                                           8          0             82.230200  \n",
       "                                                      1             81.707200  \n",
       "                                                      2             81.749900  \n",
       "                                           10         0            104.912000  \n",
       "                                                      1            102.048000  \n",
       "                                                      2            102.393000  \n",
       "                                           12         0            123.494000  \n",
       "                                                      1            122.632000  \n",
       "                                                      2            123.046000  \n",
       "                                           14         0            146.433000  \n",
       "                                                      1            145.012000  \n",
       "                                                      2            144.910000  \n",
       "                                           16         0            161.290000  \n",
       "                                                      1            159.418000  \n",
       "                                                      2            163.115000  \n",
       "                         nvidia-cudnn      2          0             30.962000  \n",
       "                                                      1             31.267200  \n",
       "                                                      2             29.678100  \n",
       "                                           4          0             36.172400  \n",
       "                                                      1             35.930800  \n",
       "                                                      2             35.934400  \n",
       "                                           6          0             49.516800  \n",
       "                                                      1             47.077900  \n",
       "                                                      2             48.064000  \n",
       "                                           8          0             61.372700  \n",
       "                                                      1             61.781200  \n",
       "                                                      2             62.266000  \n",
       "                                           10         0             77.890600  \n",
       "                                                      1             76.912900  \n",
       "                                                      2             77.028600  \n",
       "                                           12         0             90.423300  \n",
       "                                                      1             91.181600  \n",
       "                                                      2             90.351700  \n",
       "                                           14         0            108.288000  \n",
       "                                                      1            108.049000  \n",
       "                                                      2            110.727000  \n",
       "                                           16         0            118.699000  \n",
       "                                                      1            118.891000  \n",
       "                                                      2            118.251000  \n",
       "                         nvidia-fp16-cuda  2          0             24.763800  \n",
       "                                                      1             31.361200  \n",
       "                                                      2             24.386800  \n",
       "                                           4          0             35.685400  \n",
       "                                                      1             35.958800  \n",
       "                                                      2             35.573300  \n",
       "                                           6          0             54.463000  \n",
       "                                                      1             52.272600  \n",
       "                                                      2             52.196600  \n",
       "                                           8          0             69.339700  \n",
       "                                                      1             70.808600  \n",
       "                                                      2             69.177900  \n",
       "                                           10         0             87.918800  \n",
       "                                                      1             87.469800  \n",
       "                                                      2             87.699700  \n",
       "                                           12         0            103.192000  \n",
       "                                                      1            102.738000  \n",
       "                                                      2            103.069000  \n",
       "                                           14         0            128.973000  \n",
       "                                                      1            129.737000  \n",
       "                                                      2            128.318000  \n",
       "                                           16         0            138.174000  \n",
       "                                                      1            136.887000  \n",
       "                                                      2            138.502000  \n",
       "                         nvidia-fp16-cudnn 2          0             18.756000  \n",
       "                                                      1             18.588600  \n",
       "                                                      2             18.794500  \n",
       "                                           4          0             23.403500  \n",
       "                                                      1             23.671800  \n",
       "                                                      2             23.406400  \n",
       "                                           6          0             32.481200  \n",
       "                                                      1             32.601000  \n",
       "                                                      2             32.558900  \n",
       "                                           8          0             41.800500  \n",
       "                                                      1             41.906000  \n",
       "                                                      2             41.736200  \n",
       "                                           10         0             56.119700  \n",
       "                                                      1             55.608400  \n",
       "                                                      2             56.036900  \n",
       "                                           12         0             62.911300  \n",
       "                                                      1             62.975500  \n",
       "                                                      2             63.190200  \n",
       "                                           14         0             83.796800  \n",
       "                                                      1             83.486300  \n",
       "                                                      2             84.114500  \n",
       "                                           16         0             81.327700  \n",
       "                                                      1             80.877900  \n",
       "                                                      2             80.907600  \n",
       "                         tensorrt-fp16     2          0             12.620625  \n",
       "                                                      1             12.564573  \n",
       "                                                      2             12.914520  \n",
       "                                           4          0             16.090150  \n",
       "                                                      1             15.953150  \n",
       "                                                      2             15.990307  \n",
       "                                           6          0             27.783512  \n",
       "                                                      1             26.235513  \n",
       "                                                      2             26.120513  \n",
       "                                           8          0             30.959194  \n",
       "                                                      1             30.995985  \n",
       "                                                      2             30.967563  \n",
       "                                           10         0             34.830140  \n",
       "                                                      1             34.651298  \n",
       "                                                      2             34.610877  \n",
       "                                           12         0             39.653559  \n",
       "                                                      1             39.588243  \n",
       "                                                      2             39.469033  \n",
       "                                           14         0             43.443609  \n",
       "                                                      1             43.248030  \n",
       "                                                      2             43.646662  \n",
       "                                           16         0             47.710660  \n",
       "                                                      1             47.555765  \n",
       "                                                      2             47.390291  \n",
       "                         tensorrt-fp32     2          0             29.353353  \n",
       "                                                      1             20.505832  \n",
       "                                                      2             20.473990  \n",
       "                                           4          0             28.293512  \n",
       "                                                      1             28.337670  \n",
       "                                                      2             28.287670  \n",
       "                                           6          0             40.110348  \n",
       "                                                      1             40.261453  \n",
       "                                                      2             40.066032  \n",
       "                                           8          0             52.178815  \n",
       "                                                      1             52.142868  \n",
       "                                                      2             52.086184  \n",
       "                                           10         0             63.779229  \n",
       "                                                      1             63.143178  \n",
       "                                                      2             63.218810  \n",
       "                                           12         0             71.943542  \n",
       "                                                      1             72.634436  \n",
       "                                                      2             71.585332  \n",
       "                                           14         0             79.590329  \n",
       "                                                      1             79.380643  \n",
       "                                                      2             78.854327  \n",
       "                                           16         0            104.076155  \n",
       "                                                      1            103.862054  \n",
       "                                                      2            103.895998  \n",
       "bvlc-googlenet           cpu               2          0            726.576000  \n",
       "                                                      1            721.600000  \n",
       "                                                      2            721.741000  \n",
       "                                           4          0           1433.930000  \n",
       "                                                      1           1435.080000  \n",
       "                                                      2           1452.440000  \n",
       "                                           6          0           2161.430000  \n",
       "                                                      1           2169.870000  \n",
       "                                                      2           2165.080000  \n",
       "                                           8          0           2872.770000  \n",
       "                                                      1           2895.270000  \n",
       "                                                      2           2882.010000  \n",
       "                                           10         0           3577.710000  \n",
       "                                                      1           3592.950000  \n",
       "                                                      2           3600.860000  \n",
       "                                           12         0           4307.930000  \n",
       "                                                      1           4308.880000  \n",
       "                                                      2           4362.740000  \n",
       "                                           14         0           5008.490000  \n",
       "                                                      1           5067.070000  \n",
       "                                                      2           5023.240000  \n",
       "                                           16         0           5737.780000  \n",
       "                                                      1           5730.080000  \n",
       "                                                      2           5731.630000  \n",
       "                         libdnn-cuda       2          0             95.832100  \n",
       "                                                      1             98.095700  \n",
       "                                                      2            107.846000  \n",
       "                                           4          0            150.999000  \n",
       "                                                      1            152.185000  \n",
       "                                                      2            153.025000  \n",
       "                                           6          0            215.748000  \n",
       "                                                      1            216.037000  \n",
       "                                                      2            216.775000  \n",
       "                                           8          0            275.590000  \n",
       "                                                      1            275.458000  \n",
       "                                                      2            276.615000  \n",
       "                                           10         0            338.860000  \n",
       "                                                      1            349.021000  \n",
       "                                                      2            349.793000  \n",
       "                                           12         0            414.334000  \n",
       "                                                      1            411.590000  \n",
       "                                                      2            401.385000  \n",
       "                                           14         0            462.678000  \n",
       "                                                      1            468.287000  \n",
       "                                                      2            472.324000  \n",
       "                                           16         0            523.130000  \n",
       "                                                      1            522.837000  \n",
       "                                                      2            522.547000  \n",
       "                         nvidia-cuda       2          0             72.503000  \n",
       "                                                      1             67.808100  \n",
       "                                                      2             67.142200  \n",
       "                                           4          0            114.992000  \n",
       "                                                      1            113.434000  \n",
       "                                                      2            113.910000  \n",
       "                                           6          0            158.350000  \n",
       "                                                      1            158.556000  \n",
       "                                                      2            159.451000  \n",
       "                                           8          0            203.071000  \n",
       "                                                      1            204.409000  \n",
       "                                                      2            203.029000  \n",
       "                                           10         0            248.787000  \n",
       "                                                      1            248.164000  \n",
       "                                                      2            248.568000  \n",
       "                                           12         0            298.789000  \n",
       "                                                      1            294.533000  \n",
       "                                                      2            300.100000  \n",
       "                                           14         0            342.822000  \n",
       "                                                      1            339.103000  \n",
       "                                                      2            347.853000  \n",
       "                                           16         0            383.233000  \n",
       "                                                      1            386.597000  \n",
       "                                                      2            385.031000  \n",
       "                         nvidia-cudnn      2          0             55.022400  \n",
       "                                                      1             58.155200  \n",
       "                                                      2             55.736300  \n",
       "                                           4          0             86.435100  \n",
       "                                                      1             85.662900  \n",
       "                                                      2             86.069500  \n",
       "                                           6          0            117.502000  \n",
       "                                                      1            123.356000  \n",
       "                                                      2            117.326000  \n",
       "                                           8          0            148.409000  \n",
       "                                                      1            151.150000  \n",
       "                                                      2            149.771000  \n",
       "                                           10         0            182.625000  \n",
       "                                                      1            178.184000  \n",
       "                                                      2            177.301000  \n",
       "                                           12         0            211.654000  \n",
       "                                                      1            209.654000  \n",
       "                                                      2            211.429000  \n",
       "                                           14         0            243.337000  \n",
       "                                                      1            244.806000  \n",
       "                                                      2            242.718000  \n",
       "                                           16         0            272.625000  \n",
       "                                                      1            274.295000  \n",
       "                                                      2            274.347000  \n",
       "                         nvidia-fp16-cuda  2          0             49.495300  \n",
       "                                                      1             49.407600  \n",
       "                                                      2             51.201900  \n",
       "                                           4          0             91.893600  \n",
       "                                                      1             93.751800  \n",
       "                                                      2             91.854400  \n",
       "                                           6          0            134.170000  \n",
       "                                                      1            134.373000  \n",
       "                                                      2            133.610000  \n",
       "                                           8          0            176.348000  \n",
       "                                                      1            176.455000  \n",
       "                                                      2            176.885000  \n",
       "                                           10         0            219.665000  \n",
       "                                                      1            219.154000  \n",
       "                                                      2            221.270000  \n",
       "                                           12         0            263.124000  \n",
       "                                                      1            262.079000  \n",
       "                                                      2            263.535000  \n",
       "                                           14         0            306.364000  \n",
       "                                                      1            305.265000  \n",
       "                                                      2            306.503000  \n",
       "                                           16         0            348.909000  \n",
       "                                                      1            349.635000  \n",
       "                                                      2            348.834000  \n",
       "                         nvidia-fp16-cudnn 2          0             29.643400  \n",
       "                                                      1             29.601300  \n",
       "                                                      2             29.663000  \n",
       "                                           4          0             54.761200  \n",
       "                                                      1             54.610200  \n",
       "                                                      2             54.659300  \n",
       "                                           6          0             80.573500  \n",
       "                                                      1             80.484700  \n",
       "                                                      2             80.399300  \n",
       "                                           8          0             90.605100  \n",
       "                                                      1             90.459300  \n",
       "                                                      2             90.586500  \n",
       "                                           10         0            128.923000  \n",
       "                                                      1            129.616000  \n",
       "                                                      2            131.376000  \n",
       "                                           12         0            153.502000  \n",
       "                                                      1            153.667000  \n",
       "                                                      2            153.424000  \n",
       "                                           14         0            183.439000  \n",
       "                                                      1            181.383000  \n",
       "                                                      2            183.069000  \n",
       "                                           16         0            178.709000  \n",
       "                                                      1            177.023000  \n",
       "                                                      2            178.549000  \n",
       "                         tensorrt-fp16     2          0             14.485308  \n",
       "                                                      1             14.576308  \n",
       "                                                      2             14.585309  \n",
       "                                           4          0             24.238514  \n",
       "                                                      1             24.137461  \n",
       "                                                      2             24.254303  \n",
       "                                           6          0             34.025298  \n",
       "                                                      1             34.076772  \n",
       "                                                      2             34.172509  \n",
       "                                           8          0             44.013294  \n",
       "                                                      1             43.854451  \n",
       "                                                      2             43.804714  \n",
       "                                           10         0             52.217868  \n",
       "                                                      1             52.229500  \n",
       "                                                      2             52.229815  \n",
       "                                           12         0             64.078599  \n",
       "                                                      1             64.333019  \n",
       "                                                      2             64.391072  \n",
       "                                           14         0             74.021542  \n",
       "                                                      1             73.949804  \n",
       "                                                      2             74.062541  \n",
       "                                           16         0             83.507063  \n",
       "                                                      1             83.629536  \n",
       "                                                      2             83.874746  \n",
       "                         tensorrt-fp32     2          0             24.654566  \n",
       "                                                      1             24.706514  \n",
       "                                                      2             24.812513  \n",
       "                                           4          0             46.024871  \n",
       "                                                      1             46.235344  \n",
       "                                                      2             46.118398  \n",
       "                                           6          0             68.209123  \n",
       "                                                      1             68.337544  \n",
       "                                                      2             68.308860  \n",
       "                                           8          0             89.629849  \n",
       "                                                      1             88.754744  \n",
       "                                                      2             89.036270  \n",
       "                                           10         0            108.358576  \n",
       "                                                      1            109.368577  \n",
       "                                                      2            108.333629  \n",
       "                                           12         0            132.795089  \n",
       "                                                      1            132.970562  \n",
       "                                                      2            132.811038  \n",
       "                                           14         0            153.733922  \n",
       "                                                      1            153.970342  \n",
       "                                                      2            153.733921  \n",
       "                                           16         0            176.017908  \n",
       "                                                      1            177.000647  \n",
       "                                                      2            176.663119  \n",
       "deepscale-squeezenet-1.0 cpu               2          0            253.702000  \n",
       "                                                      1            255.276000  \n",
       "                                                      2            252.807000  \n",
       "                                           4          0            502.686000  \n",
       "                                                      1            504.233000  \n",
       "                                                      2            504.196000  \n",
       "                                           6          0            756.378000  \n",
       "                                                      1            752.891000  \n",
       "                                                      2            755.365000  \n",
       "                                           8          0           1002.240000  \n",
       "                                                      1           1003.510000  \n",
       "                                                      2           1011.790000  \n",
       "                                           10         0           1253.520000  \n",
       "                                                      1           1260.400000  \n",
       "                                                      2           1256.570000  \n",
       "                                           12         0           1504.420000  \n",
       "                                                      1           1505.160000  \n",
       "                                                      2           1505.980000  \n",
       "                                           14         0           1754.850000  \n",
       "                                                      1           1763.890000  \n",
       "                                                      2           1752.510000  \n",
       "                                           16         0           2017.420000  \n",
       "                                                      1           2005.910000  \n",
       "                                                      2           2027.760000  \n",
       "                         libdnn-cuda       2          0             48.651200  \n",
       "                                                      1             50.457500  \n",
       "                                                      2             55.726400  \n",
       "                                           4          0             88.424500  \n",
       "                                                      1             86.170400  \n",
       "                                                      2             89.805400  \n",
       "                                           6          0            126.186000  \n",
       "                                                      1            133.083000  \n",
       "                                                      2            123.676000  \n",
       "                                           8          0            166.935000  \n",
       "                                                      1            162.627000  \n",
       "                                                      2            163.530000  \n",
       "                                           10         0            196.310000  \n",
       "                                                      1            223.921000  \n",
       "                                                      2            197.104000  \n",
       "                                           12         0            233.976000  \n",
       "                                                      1            234.237000  \n",
       "                                                      2            235.000000  \n",
       "                                           14         0            270.913000  \n",
       "                                                      1            276.680000  \n",
       "                                                      2            271.152000  \n",
       "                                           16         0            314.305000  \n",
       "                                                      1            307.690000  \n",
       "                                                      2            314.576000  \n",
       "                         nvidia-cuda       2          0             40.542600  \n",
       "                                                      1             41.271000  \n",
       "                                                      2             41.370500  \n",
       "                                           4          0             77.570300  \n",
       "                                                      1             72.845500  \n",
       "                                                      2             71.853300  \n",
       "                                           6          0            103.481000  \n",
       "                                                      1            104.214000  \n",
       "                                                      2            107.186000  \n",
       "                                           8          0            133.831000  \n",
       "                                                      1            133.928000  \n",
       "                                                      2            136.559000  \n",
       "                                           10         0            166.995000  \n",
       "                                                      1            166.171000  \n",
       "                                                      2            163.876000  \n",
       "                                           12         0            195.475000  \n",
       "                                                      1            194.790000  \n",
       "                                                      2            197.505000  \n",
       "                                           14         0            225.449000  \n",
       "                                                      1            224.973000  \n",
       "                                                      2            227.388000  \n",
       "                                           16         0            255.899000  \n",
       "                                                      1            256.939000  \n",
       "                                                      2            257.944000  \n",
       "                         nvidia-cudnn      2          0             34.387000  \n",
       "                                                      1             36.602200  \n",
       "                                                      2             35.238500  \n",
       "                                           4          0             59.645700  \n",
       "                                                      1             60.952400  \n",
       "                                                      2             58.919300  \n",
       "                                           6          0             83.164300  \n",
       "                                                      1             83.634100  \n",
       "                                                      2             83.703200  \n",
       "                                           8          0            108.407000  \n",
       "                                                      1            110.041000  \n",
       "                                                      2            112.536000  \n",
       "                                           10         0            134.133000  \n",
       "                                                      1            132.381000  \n",
       "                                                      2            134.470000  \n",
       "                                           12         0            156.495000  \n",
       "                                                      1            156.894000  \n",
       "                                                      2            158.866000  \n",
       "                                           14         0            185.548000  \n",
       "                                                      1            183.169000  \n",
       "                                                      2            185.909000  \n",
       "                                           16         0            211.621000  \n",
       "                                                      1            206.880000  \n",
       "                                                      2            205.637000  \n",
       "                         nvidia-fp16-cuda  2          0             27.853600  \n",
       "                                                      1             27.492600  \n",
       "                                                      2             27.647800  \n",
       "                                           4          0             53.218000  \n",
       "                                                      1             53.337200  \n",
       "                                                      2             54.811700  \n",
       "                                           6          0             78.116900  \n",
       "                                                      1             78.267300  \n",
       "                                                      2             78.620300  \n",
       "                                           8          0            104.016000  \n",
       "                                                      1            103.641000  \n",
       "                                                      2            103.442000  \n",
       "                                           10         0            128.267000  \n",
       "                                                      1            128.886000  \n",
       "                                                      2            128.613000  \n",
       "                                           12         0            155.909000  \n",
       "                                                      1            156.209000  \n",
       "                                                      2            154.058000  \n",
       "                                           14         0            179.802000  \n",
       "                                                      1            179.570000  \n",
       "                                                      2            181.353000  \n",
       "                                           16         0            206.533000  \n",
       "                                                      1            204.565000  \n",
       "                                                      2            204.765000  \n",
       "                         nvidia-fp16-cudnn 2          0             20.285600  \n",
       "                                                      1             20.263700  \n",
       "                                                      2             20.176900  \n",
       "                                           4          0             39.222300  \n",
       "                                                      1             39.080000  \n",
       "                                                      2             39.138000  \n",
       "                                           6          0             58.987900  \n",
       "                                                      1             59.029000  \n",
       "                                                      2             59.102300  \n",
       "                                           8          0             75.810200  \n",
       "                                                      1             72.273200  \n",
       "                                                      2             71.923500  \n",
       "                                           10         0             98.192500  \n",
       "                                                      1             97.878600  \n",
       "                                                      2             97.853600  \n",
       "                                           12         0            116.559000  \n",
       "                                                      1            114.888000  \n",
       "                                                      2            117.108000  \n",
       "                                           14         0            136.638000  \n",
       "                                                      1            136.398000  \n",
       "                                                      2            136.875000  \n",
       "                                           16         0            142.985000  \n",
       "                                                      1            143.718000  \n",
       "                                                      2            143.840000  \n",
       "                         tensorrt-fp16     2          0              8.757838  \n",
       "                                                      1              8.738153  \n",
       "                                                      2              8.700311  \n",
       "                                           4          0             15.879676  \n",
       "                                                      1             15.922834  \n",
       "                                                      2             15.959466  \n",
       "                                           6          0             23.154199  \n",
       "                                                      1             23.167041  \n",
       "                                                      2             23.222724  \n",
       "                                           8          0             30.145195  \n",
       "                                                      1             30.219143  \n",
       "                                                      2             30.188668  \n",
       "                                           10         0             37.440718  \n",
       "                                                      1             37.072928  \n",
       "                                                      2             37.368454  \n",
       "                                           12         0             45.281240  \n",
       "                                                      1             44.407293  \n",
       "                                                      2             44.368872  \n",
       "                                           14         0             51.842710  \n",
       "                                                      1             51.829131  \n",
       "                                                      2             52.408868  \n",
       "                                           16         0             58.747601  \n",
       "                                                      1             58.934180  \n",
       "                                                      2             58.617443  \n",
       "                         tensorrt-fp32     2          0             15.774518  \n",
       "                                                      1             15.716466  \n",
       "                                                      2             15.731940  \n",
       "                                           4          0             30.450721  \n",
       "                                                      1             30.406879  \n",
       "                                                      2             30.546563  \n",
       "                                           6          0             45.192555  \n",
       "                                                      1             44.934661  \n",
       "                                                      2             45.055346  \n",
       "                                           8          0             59.673022  \n",
       "                                                      1             60.149180  \n",
       "                                                      2             59.989180  \n",
       "                                           10         0             75.474067  \n",
       "                                                      1             74.971699  \n",
       "                                                      2             74.683594  \n",
       "                                           12         0             90.008849  \n",
       "                                                      1             89.615848  \n",
       "                                                      2             89.681428  \n",
       "                                           14         0            104.018262  \n",
       "                                                      1            103.730684  \n",
       "                                                      2            104.923155  \n",
       "                                           16         0            119.386255  \n",
       "                                                      1            118.910782  \n",
       "                                                      2            119.409730  \n",
       "deepscale-squeezenet-1.1 cpu               2          0            136.270000  \n",
       "                                                      1            136.626000  \n",
       "                                                      2            137.120000  \n",
       "                                           4          0            270.304000  \n",
       "                                                      1            273.057000  \n",
       "                                                      2            270.515000  \n",
       "                                           6          0            408.191000  \n",
       "                                                      1            404.040000  \n",
       "                                                      2            406.419000  \n",
       "                                           8          0            536.611000  \n",
       "                                                      1            538.226000  \n",
       "                                                      2            540.875000  \n",
       "                                           10         0            669.522000  \n",
       "                                                      1            672.894000  \n",
       "                                                      2            671.978000  \n",
       "                                           12         0            808.527000  \n",
       "                                                      1            807.042000  \n",
       "                                                      2            804.237000  \n",
       "                                           14         0            937.107000  \n",
       "                                                      1            944.726000  \n",
       "                                                      2            938.090000  \n",
       "                                           16         0           1078.190000  \n",
       "                                                      1           1072.820000  \n",
       "                                                      2           1086.130000  \n",
       "                         libdnn-cuda       2          0             32.291500  \n",
       "                                                      1             28.682600  \n",
       "                                                      2             43.390000  \n",
       "                                           4          0             58.585900  \n",
       "                                                      1             49.974100  \n",
       "                                                      2             49.609600  \n",
       "                                           6          0             76.880300  \n",
       "                                                      1             67.614700  \n",
       "                                                      2             69.939300  \n",
       "                                           8          0             94.871500  \n",
       "                                                      1             92.338400  \n",
       "                                                      2             96.141600  \n",
       "                                           10         0            113.236000  \n",
       "                                                      1            113.252000  \n",
       "                                                      2            105.192000  \n",
       "                                           12         0            125.267000  \n",
       "                                                      1            124.526000  \n",
       "                                                      2            133.248000  \n",
       "                                           14         0            151.743000  \n",
       "                                                      1            145.605000  \n",
       "                                                      2            150.738000  \n",
       "                                           16         0            170.049000  \n",
       "                                                      1            174.297000  \n",
       "                                                      2            165.273000  \n",
       "                         nvidia-cuda       2          0             30.304500  \n",
       "                                                      1             28.741000  \n",
       "                                                      2             28.350700  \n",
       "                                           4          0             53.818100  \n",
       "                                                      1             55.737700  \n",
       "                                                      2             49.688400  \n",
       "                                           6          0             64.211500  \n",
       "                                                      1             64.305500  \n",
       "                                                      2             68.016700  \n",
       "                                           8          0             86.360600  \n",
       "                                                      1             85.752100  \n",
       "                                                      2             83.783000  \n",
       "                                           10         0            100.881000  \n",
       "                                                      1            104.219000  \n",
       "                                                      2            102.938000  \n",
       "                                           12         0            122.704000  \n",
       "                                                      1            120.847000  \n",
       "                                                      2            119.593000  \n",
       "                                           14         0            138.405000  \n",
       "                                                      1            140.048000  \n",
       "                                                      2            138.398000  \n",
       "                                           16         0            156.537000  \n",
       "                                                      1            157.109000  \n",
       "                                                      2            159.291000  \n",
       "                         nvidia-cudnn      2          0             24.500500  \n",
       "                                                      1             25.696100  \n",
       "                                                      2             25.903400  \n",
       "                                           4          0             37.777800  \n",
       "                                                      1             38.335900  \n",
       "                                                      2             38.896700  \n",
       "                                           6          0             51.485400  \n",
       "                                                      1             51.531400  \n",
       "                                                      2             51.437000  \n",
       "                                           8          0             65.522900  \n",
       "                                                      1             64.851700  \n",
       "                                                      2             64.468300  \n",
       "                                           10         0             80.538000  \n",
       "                                                      1             78.080400  \n",
       "                                                      2             78.383700  \n",
       "                                           12         0             91.085900  \n",
       "                                                      1             94.015300  \n",
       "                                                      2             91.812000  \n",
       "                                           14         0            109.639000  \n",
       "                                                      1            107.549000  \n",
       "                                                      2            109.073000  \n",
       "                                           16         0            119.433000  \n",
       "                                                      1            118.776000  \n",
       "                                                      2            129.538000  \n",
       "                         nvidia-fp16-cuda  2          0             18.812500  \n",
       "                                                      1             19.924600  \n",
       "                                                      2             18.920100  \n",
       "                                           4          0             34.724900  \n",
       "                                                      1             34.908400  \n",
       "                                                      2             35.160200  \n",
       "                                           6          0             50.611900  \n",
       "                                                      1             52.941600  \n",
       "                                                      2             52.888400  \n",
       "                                           8          0             66.761300  \n",
       "                                                      1             66.974200  \n",
       "                                                      2             67.070000  \n",
       "                                           10         0             82.873300  \n",
       "                                                      1             85.610100  \n",
       "                                                      2             82.638900  \n",
       "                                           12         0             98.593900  \n",
       "                                                      1            100.466000  \n",
       "                                                      2             98.875200  \n",
       "                                           14         0            114.812000  \n",
       "                                                      1            116.996000  \n",
       "                                                      2            114.821000  \n",
       "                                           16         0            140.382000  \n",
       "                                                      1            131.114000  \n",
       "                                                      2            131.181000  \n",
       "                         nvidia-fp16-cudnn 2          0             14.147600  \n",
       "                                                      1             12.577200  \n",
       "                                                      2             12.529400  \n",
       "                                           4          0             23.465500  \n",
       "                                                      1             23.520600  \n",
       "                                                      2             23.469500  \n",
       "                                           6          0             35.551900  \n",
       "                                                      1             35.491700  \n",
       "                                                      2             35.629900  \n",
       "                                           8          0             40.318700  \n",
       "                                                      1             40.286800  \n",
       "                                                      2             40.327100  \n",
       "                                           10         0             58.051000  \n",
       "                                                      1             58.168500  \n",
       "                                                      2             58.068100  \n",
       "                                           12         0             67.803500  \n",
       "                                                      1             67.663000  \n",
       "                                                      2             69.536100  \n",
       "                                           14         0             81.022400  \n",
       "                                                      1             81.185500  \n",
       "                                                      2             80.984100  \n",
       "                                           16         0             79.594500  \n",
       "                                                      1             80.570500  \n",
       "                                                      2             80.427800  \n",
       "                         tensorrt-fp16     2          0              5.377155  \n",
       "                                                      1              5.414945  \n",
       "                                                      2              5.411576  \n",
       "                                           4          0              9.027626  \n",
       "                                                      1              9.028205  \n",
       "                                                      2              8.962101  \n",
       "                                           6          0             12.251047  \n",
       "                                                      1             12.367309  \n",
       "                                                      2             12.304204  \n",
       "                                           8          0             15.868360  \n",
       "                                                      1             15.945623  \n",
       "                                                      2             15.835097  \n",
       "                                           10         0             18.976411  \n",
       "                                                      1             18.924622  \n",
       "                                                      2             19.173148  \n",
       "                                           12         0             23.086462  \n",
       "                                                      1             23.001304  \n",
       "                                                      2             23.013410  \n",
       "                                           14         0             26.214039  \n",
       "                                                      1             26.271934  \n",
       "                                                      2             26.415460  \n",
       "                                           16         0             30.113932  \n",
       "                                                      1             30.275458  \n",
       "                                                      2             30.134406  \n",
       "                         tensorrt-fp32     2          0              8.265153  \n",
       "                                                      1              8.238364  \n",
       "                                                      2              8.144259  \n",
       "                                           4          0             15.685308  \n",
       "                                                      1             15.143676  \n",
       "                                                      2             15.116308  \n",
       "                                           6          0             22.320673  \n",
       "                                                      1             22.175621  \n",
       "                                                      2             22.011515  \n",
       "                                           8          0             29.065248  \n",
       "                                                      1             29.047458  \n",
       "                                                      2             29.676406  \n",
       "                                           10         0             36.717139  \n",
       "                                                      1             36.331982  \n",
       "                                                      2             36.023297  \n",
       "                                           12         0             43.034188  \n",
       "                                                      1             43.000188  \n",
       "                                                      2             42.970767  \n",
       "                                           14         0             49.826817  \n",
       "                                                      1             49.500343  \n",
       "                                                      2             49.993396  \n",
       "                                           16         0             57.256970  \n",
       "                                                      1             57.296445  \n",
       "                                                      2             58.138023  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.options.display.max_columns = len(df_all.columns)\n",
    "pd.options.display.max_rows = len(df_all.index)\n",
    "df_all"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"df_time\"></a>\n",
    "### All execution time data indexed by repetitions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df_time = df_all['time (ms)'].unstack(df_all.index.names[:-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th>model</th>\n",
       "      <th colspan=\"64\" halign=\"left\">bvlc-alexnet</th>\n",
       "      <th colspan=\"64\" halign=\"left\">bvlc-googlenet</th>\n",
       "      <th colspan=\"64\" halign=\"left\">deepscale-squeezenet-1.0</th>\n",
       "      <th colspan=\"64\" halign=\"left\">deepscale-squeezenet-1.1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lib</th>\n",
       "      <th colspan=\"8\" halign=\"left\">cpu</th>\n",
       "      <th colspan=\"8\" halign=\"left\">libdnn-cuda</th>\n",
       "      <th colspan=\"8\" halign=\"left\">nvidia-cuda</th>\n",
       "      <th colspan=\"8\" halign=\"left\">nvidia-cudnn</th>\n",
       "      <th colspan=\"8\" halign=\"left\">nvidia-fp16-cuda</th>\n",
       "      <th colspan=\"8\" halign=\"left\">nvidia-fp16-cudnn</th>\n",
       "      <th colspan=\"8\" halign=\"left\">tensorrt-fp16</th>\n",
       "      <th colspan=\"8\" halign=\"left\">tensorrt-fp32</th>\n",
       "      <th colspan=\"8\" halign=\"left\">cpu</th>\n",
       "      <th colspan=\"8\" halign=\"left\">libdnn-cuda</th>\n",
       "      <th colspan=\"8\" halign=\"left\">nvidia-cuda</th>\n",
       "      <th colspan=\"8\" halign=\"left\">nvidia-cudnn</th>\n",
       "      <th colspan=\"8\" halign=\"left\">nvidia-fp16-cuda</th>\n",
       "      <th colspan=\"8\" halign=\"left\">nvidia-fp16-cudnn</th>\n",
       "      <th colspan=\"8\" halign=\"left\">tensorrt-fp16</th>\n",
       "      <th colspan=\"8\" halign=\"left\">tensorrt-fp32</th>\n",
       "      <th colspan=\"8\" halign=\"left\">cpu</th>\n",
       "      <th colspan=\"8\" halign=\"left\">libdnn-cuda</th>\n",
       "      <th colspan=\"8\" halign=\"left\">nvidia-cuda</th>\n",
       "      <th colspan=\"8\" halign=\"left\">nvidia-cudnn</th>\n",
       "      <th colspan=\"8\" halign=\"left\">nvidia-fp16-cuda</th>\n",
       "      <th colspan=\"8\" halign=\"left\">nvidia-fp16-cudnn</th>\n",
       "      <th colspan=\"8\" halign=\"left\">tensorrt-fp16</th>\n",
       "      <th colspan=\"8\" halign=\"left\">tensorrt-fp32</th>\n",
       "      <th colspan=\"8\" halign=\"left\">cpu</th>\n",
       "      <th colspan=\"8\" halign=\"left\">libdnn-cuda</th>\n",
       "      <th colspan=\"8\" halign=\"left\">nvidia-cuda</th>\n",
       "      <th colspan=\"8\" halign=\"left\">nvidia-cudnn</th>\n",
       "      <th colspan=\"8\" halign=\"left\">nvidia-fp16-cuda</th>\n",
       "      <th colspan=\"8\" halign=\"left\">nvidia-fp16-cudnn</th>\n",
       "      <th colspan=\"8\" halign=\"left\">tensorrt-fp16</th>\n",
       "      <th colspan=\"8\" halign=\"left\">tensorrt-fp32</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>batch size</th>\n",
       "      <th>2</th>\n",
       "      <th>4</th>\n",
       "      <th>6</th>\n",
       "      <th>8</th>\n",
       "      <th>10</th>\n",
       "      <th>12</th>\n",
       "      <th>14</th>\n",
       "      <th>16</th>\n",
       "      <th>2</th>\n",
       "      <th>4</th>\n",
       "      <th>6</th>\n",
       "      <th>8</th>\n",
       "      <th>10</th>\n",
       "      <th>12</th>\n",
       "      <th>14</th>\n",
       "      <th>16</th>\n",
       "      <th>2</th>\n",
       "      <th>4</th>\n",
       "      <th>6</th>\n",
       "      <th>8</th>\n",
       "      <th>10</th>\n",
       "      <th>12</th>\n",
       "      <th>14</th>\n",
       "      <th>16</th>\n",
       "      <th>2</th>\n",
       "      <th>4</th>\n",
       "      <th>6</th>\n",
       "      <th>8</th>\n",
       "      <th>10</th>\n",
       "      <th>12</th>\n",
       "      <th>14</th>\n",
       "      <th>16</th>\n",
       "      <th>2</th>\n",
       "      <th>4</th>\n",
       "      <th>6</th>\n",
       "      <th>8</th>\n",
       "      <th>10</th>\n",
       "      <th>12</th>\n",
       "      <th>14</th>\n",
       "      <th>16</th>\n",
       "      <th>2</th>\n",
       "      <th>4</th>\n",
       "      <th>6</th>\n",
       "      <th>8</th>\n",
       "      <th>10</th>\n",
       "      <th>12</th>\n",
       "      <th>14</th>\n",
       "      <th>16</th>\n",
       "      <th>2</th>\n",
       "      <th>4</th>\n",
       "      <th>6</th>\n",
       "      <th>8</th>\n",
       "      <th>10</th>\n",
       "      <th>12</th>\n",
       "      <th>14</th>\n",
       "      <th>16</th>\n",
       "      <th>2</th>\n",
       "      <th>4</th>\n",
       "      <th>6</th>\n",
       "      <th>8</th>\n",
       "      <th>10</th>\n",
       "      <th>12</th>\n",
       "      <th>14</th>\n",
       "      <th>16</th>\n",
       "      <th>2</th>\n",
       "      <th>4</th>\n",
       "      <th>6</th>\n",
       "      <th>8</th>\n",
       "      <th>10</th>\n",
       "      <th>12</th>\n",
       "      <th>14</th>\n",
       "      <th>16</th>\n",
       "      <th>2</th>\n",
       "      <th>4</th>\n",
       "      <th>6</th>\n",
       "      <th>8</th>\n",
       "      <th>10</th>\n",
       "      <th>12</th>\n",
       "      <th>14</th>\n",
       "      <th>16</th>\n",
       "      <th>2</th>\n",
       "      <th>4</th>\n",
       "      <th>6</th>\n",
       "      <th>8</th>\n",
       "      <th>10</th>\n",
       "      <th>12</th>\n",
       "      <th>14</th>\n",
       "      <th>16</th>\n",
       "      <th>2</th>\n",
       "      <th>4</th>\n",
       "      <th>6</th>\n",
       "      <th>8</th>\n",
       "      <th>10</th>\n",
       "      <th>12</th>\n",
       "      <th>14</th>\n",
       "      <th>16</th>\n",
       "      <th>2</th>\n",
       "      <th>4</th>\n",
       "      <th>6</th>\n",
       "      <th>8</th>\n",
       "      <th>10</th>\n",
       "      <th>12</th>\n",
       "      <th>14</th>\n",
       "      <th>16</th>\n",
       "      <th>2</th>\n",
       "      <th>4</th>\n",
       "      <th>6</th>\n",
       "      <th>8</th>\n",
       "      <th>10</th>\n",
       "      <th>12</th>\n",
       "      <th>14</th>\n",
       "      <th>16</th>\n",
       "      <th>2</th>\n",
       "      <th>4</th>\n",
       "      <th>6</th>\n",
       "      <th>8</th>\n",
       "      <th>10</th>\n",
       "      <th>12</th>\n",
       "      <th>14</th>\n",
       "      <th>16</th>\n",
       "      <th>2</th>\n",
       "      <th>4</th>\n",
       "      <th>6</th>\n",
       "      <th>8</th>\n",
       "      <th>10</th>\n",
       "      <th>12</th>\n",
       "      <th>14</th>\n",
       "      <th>16</th>\n",
       "      <th>2</th>\n",
       "      <th>4</th>\n",
       "      <th>6</th>\n",
       "      <th>8</th>\n",
       "      <th>10</th>\n",
       "      <th>12</th>\n",
       "      <th>14</th>\n",
       "      <th>16</th>\n",
       "      <th>2</th>\n",
       "      <th>4</th>\n",
       "      <th>6</th>\n",
       "      <th>8</th>\n",
       "      <th>10</th>\n",
       "      <th>12</th>\n",
       "      <th>14</th>\n",
       "      <th>16</th>\n",
       "      <th>2</th>\n",
       "      <th>4</th>\n",
       "      <th>6</th>\n",
       "      <th>8</th>\n",
       "      <th>10</th>\n",
       "      <th>12</th>\n",
       "      <th>14</th>\n",
       "      <th>16</th>\n",
       "      <th>2</th>\n",
       "      <th>4</th>\n",
       "      <th>6</th>\n",
       "      <th>8</th>\n",
       "      <th>10</th>\n",
       "      <th>12</th>\n",
       "      <th>14</th>\n",
       "      <th>16</th>\n",
       "      <th>2</th>\n",
       "      <th>4</th>\n",
       "      <th>6</th>\n",
       "      <th>8</th>\n",
       "      <th>10</th>\n",
       "      <th>12</th>\n",
       "      <th>14</th>\n",
       "      <th>16</th>\n",
       "      <th>2</th>\n",
       "      <th>4</th>\n",
       "      <th>6</th>\n",
       "      <th>8</th>\n",
       "      <th>10</th>\n",
       "      <th>12</th>\n",
       "      <th>14</th>\n",
       "      <th>16</th>\n",
       "      <th>2</th>\n",
       "      <th>4</th>\n",
       "      <th>6</th>\n",
       "      <th>8</th>\n",
       "      <th>10</th>\n",
       "      <th>12</th>\n",
       "      <th>14</th>\n",
       "      <th>16</th>\n",
       "      <th>2</th>\n",
       "      <th>4</th>\n",
       "      <th>6</th>\n",
       "      <th>8</th>\n",
       "      <th>10</th>\n",
       "      <th>12</th>\n",
       "      <th>14</th>\n",
       "      <th>16</th>\n",
       "      <th>2</th>\n",
       "      <th>4</th>\n",
       "      <th>6</th>\n",
       "      <th>8</th>\n",
       "      <th>10</th>\n",
       "      <th>12</th>\n",
       "      <th>14</th>\n",
       "      <th>16</th>\n",
       "      <th>2</th>\n",
       "      <th>4</th>\n",
       "      <th>6</th>\n",
       "      <th>8</th>\n",
       "      <th>10</th>\n",
       "      <th>12</th>\n",
       "      <th>14</th>\n",
       "      <th>16</th>\n",
       "      <th>2</th>\n",
       "      <th>4</th>\n",
       "      <th>6</th>\n",
       "      <th>8</th>\n",
       "      <th>10</th>\n",
       "      <th>12</th>\n",
       "      <th>14</th>\n",
       "      <th>16</th>\n",
       "      <th>2</th>\n",
       "      <th>4</th>\n",
       "      <th>6</th>\n",
       "      <th>8</th>\n",
       "      <th>10</th>\n",
       "      <th>12</th>\n",
       "      <th>14</th>\n",
       "      <th>16</th>\n",
       "      <th>2</th>\n",
       "      <th>4</th>\n",
       "      <th>6</th>\n",
       "      <th>8</th>\n",
       "      <th>10</th>\n",
       "      <th>12</th>\n",
       "      <th>14</th>\n",
       "      <th>16</th>\n",
       "      <th>2</th>\n",
       "      <th>4</th>\n",
       "      <th>6</th>\n",
       "      <th>8</th>\n",
       "      <th>10</th>\n",
       "      <th>12</th>\n",
       "      <th>14</th>\n",
       "      <th>16</th>\n",
       "      <th>2</th>\n",
       "      <th>4</th>\n",
       "      <th>6</th>\n",
       "      <th>8</th>\n",
       "      <th>10</th>\n",
       "      <th>12</th>\n",
       "      <th>14</th>\n",
       "      <th>16</th>\n",
       "      <th>2</th>\n",
       "      <th>4</th>\n",
       "      <th>6</th>\n",
       "      <th>8</th>\n",
       "      <th>10</th>\n",
       "      <th>12</th>\n",
       "      <th>14</th>\n",
       "      <th>16</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>repetition</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>429.404</td>\n",
       "      <td>657.945</td>\n",
       "      <td>926.569</td>\n",
       "      <td>1058.14</td>\n",
       "      <td>1293.97</td>\n",
       "      <td>1530.73</td>\n",
       "      <td>1768.87</td>\n",
       "      <td>1983.67</td>\n",
       "      <td>43.4558</td>\n",
       "      <td>65.6589</td>\n",
       "      <td>93.9803</td>\n",
       "      <td>120.833</td>\n",
       "      <td>147.654</td>\n",
       "      <td>179.296</td>\n",
       "      <td>208.372</td>\n",
       "      <td>233.930</td>\n",
       "      <td>32.6553</td>\n",
       "      <td>44.5101</td>\n",
       "      <td>63.0471</td>\n",
       "      <td>82.2302</td>\n",
       "      <td>104.912</td>\n",
       "      <td>123.494</td>\n",
       "      <td>146.433</td>\n",
       "      <td>161.290</td>\n",
       "      <td>30.9620</td>\n",
       "      <td>36.1724</td>\n",
       "      <td>49.5168</td>\n",
       "      <td>61.3727</td>\n",
       "      <td>77.8906</td>\n",
       "      <td>90.4233</td>\n",
       "      <td>108.288</td>\n",
       "      <td>118.699</td>\n",
       "      <td>24.7638</td>\n",
       "      <td>35.6854</td>\n",
       "      <td>54.4630</td>\n",
       "      <td>69.3397</td>\n",
       "      <td>87.9188</td>\n",
       "      <td>103.192</td>\n",
       "      <td>128.973</td>\n",
       "      <td>138.174</td>\n",
       "      <td>18.7560</td>\n",
       "      <td>23.4035</td>\n",
       "      <td>32.4812</td>\n",
       "      <td>41.8005</td>\n",
       "      <td>56.1197</td>\n",
       "      <td>62.9113</td>\n",
       "      <td>83.7968</td>\n",
       "      <td>81.3277</td>\n",
       "      <td>12.620625</td>\n",
       "      <td>16.090150</td>\n",
       "      <td>27.783512</td>\n",
       "      <td>30.959194</td>\n",
       "      <td>34.830140</td>\n",
       "      <td>39.653559</td>\n",
       "      <td>43.443609</td>\n",
       "      <td>47.710660</td>\n",
       "      <td>29.353353</td>\n",
       "      <td>28.293512</td>\n",
       "      <td>40.110348</td>\n",
       "      <td>52.178815</td>\n",
       "      <td>63.779229</td>\n",
       "      <td>71.943542</td>\n",
       "      <td>79.590329</td>\n",
       "      <td>104.076155</td>\n",
       "      <td>726.576</td>\n",
       "      <td>1433.93</td>\n",
       "      <td>2161.43</td>\n",
       "      <td>2872.77</td>\n",
       "      <td>3577.71</td>\n",
       "      <td>4307.93</td>\n",
       "      <td>5008.49</td>\n",
       "      <td>5737.78</td>\n",
       "      <td>95.8321</td>\n",
       "      <td>150.999</td>\n",
       "      <td>215.748</td>\n",
       "      <td>275.590</td>\n",
       "      <td>338.860</td>\n",
       "      <td>414.334</td>\n",
       "      <td>462.678</td>\n",
       "      <td>523.130</td>\n",
       "      <td>72.5030</td>\n",
       "      <td>114.992</td>\n",
       "      <td>158.350</td>\n",
       "      <td>203.071</td>\n",
       "      <td>248.787</td>\n",
       "      <td>298.789</td>\n",
       "      <td>342.822</td>\n",
       "      <td>383.233</td>\n",
       "      <td>55.0224</td>\n",
       "      <td>86.4351</td>\n",
       "      <td>117.502</td>\n",
       "      <td>148.409</td>\n",
       "      <td>182.625</td>\n",
       "      <td>211.654</td>\n",
       "      <td>243.337</td>\n",
       "      <td>272.625</td>\n",
       "      <td>49.4953</td>\n",
       "      <td>91.8936</td>\n",
       "      <td>134.170</td>\n",
       "      <td>176.348</td>\n",
       "      <td>219.665</td>\n",
       "      <td>263.124</td>\n",
       "      <td>306.364</td>\n",
       "      <td>348.909</td>\n",
       "      <td>29.6434</td>\n",
       "      <td>54.7612</td>\n",
       "      <td>80.5735</td>\n",
       "      <td>90.6051</td>\n",
       "      <td>128.923</td>\n",
       "      <td>153.502</td>\n",
       "      <td>183.439</td>\n",
       "      <td>178.709</td>\n",
       "      <td>14.485308</td>\n",
       "      <td>24.238514</td>\n",
       "      <td>34.025298</td>\n",
       "      <td>44.013294</td>\n",
       "      <td>52.217868</td>\n",
       "      <td>64.078599</td>\n",
       "      <td>74.021542</td>\n",
       "      <td>83.507063</td>\n",
       "      <td>24.654566</td>\n",
       "      <td>46.024871</td>\n",
       "      <td>68.209123</td>\n",
       "      <td>89.629849</td>\n",
       "      <td>108.358576</td>\n",
       "      <td>132.795089</td>\n",
       "      <td>153.733922</td>\n",
       "      <td>176.017908</td>\n",
       "      <td>253.702</td>\n",
       "      <td>502.686</td>\n",
       "      <td>756.378</td>\n",
       "      <td>1002.24</td>\n",
       "      <td>1253.52</td>\n",
       "      <td>1504.42</td>\n",
       "      <td>1754.85</td>\n",
       "      <td>2017.42</td>\n",
       "      <td>48.6512</td>\n",
       "      <td>88.4245</td>\n",
       "      <td>126.186</td>\n",
       "      <td>166.935</td>\n",
       "      <td>196.310</td>\n",
       "      <td>233.976</td>\n",
       "      <td>270.913</td>\n",
       "      <td>314.305</td>\n",
       "      <td>40.5426</td>\n",
       "      <td>77.5703</td>\n",
       "      <td>103.481</td>\n",
       "      <td>133.831</td>\n",
       "      <td>166.995</td>\n",
       "      <td>195.475</td>\n",
       "      <td>225.449</td>\n",
       "      <td>255.899</td>\n",
       "      <td>34.3870</td>\n",
       "      <td>59.6457</td>\n",
       "      <td>83.1643</td>\n",
       "      <td>108.407</td>\n",
       "      <td>134.133</td>\n",
       "      <td>156.495</td>\n",
       "      <td>185.548</td>\n",
       "      <td>211.621</td>\n",
       "      <td>27.8536</td>\n",
       "      <td>53.2180</td>\n",
       "      <td>78.1169</td>\n",
       "      <td>104.016</td>\n",
       "      <td>128.267</td>\n",
       "      <td>155.909</td>\n",
       "      <td>179.802</td>\n",
       "      <td>206.533</td>\n",
       "      <td>20.2856</td>\n",
       "      <td>39.2223</td>\n",
       "      <td>58.9879</td>\n",
       "      <td>75.8102</td>\n",
       "      <td>98.1925</td>\n",
       "      <td>116.559</td>\n",
       "      <td>136.638</td>\n",
       "      <td>142.985</td>\n",
       "      <td>8.757838</td>\n",
       "      <td>15.879676</td>\n",
       "      <td>23.154199</td>\n",
       "      <td>30.145195</td>\n",
       "      <td>37.440718</td>\n",
       "      <td>45.281240</td>\n",
       "      <td>51.842710</td>\n",
       "      <td>58.747601</td>\n",
       "      <td>15.774518</td>\n",
       "      <td>30.450721</td>\n",
       "      <td>45.192555</td>\n",
       "      <td>59.673022</td>\n",
       "      <td>75.474067</td>\n",
       "      <td>90.008849</td>\n",
       "      <td>104.018262</td>\n",
       "      <td>119.386255</td>\n",
       "      <td>136.270</td>\n",
       "      <td>270.304</td>\n",
       "      <td>408.191</td>\n",
       "      <td>536.611</td>\n",
       "      <td>669.522</td>\n",
       "      <td>808.527</td>\n",
       "      <td>937.107</td>\n",
       "      <td>1078.19</td>\n",
       "      <td>32.2915</td>\n",
       "      <td>58.5859</td>\n",
       "      <td>76.8803</td>\n",
       "      <td>94.8715</td>\n",
       "      <td>113.236</td>\n",
       "      <td>125.267</td>\n",
       "      <td>151.743</td>\n",
       "      <td>170.049</td>\n",
       "      <td>30.3045</td>\n",
       "      <td>53.8181</td>\n",
       "      <td>64.2115</td>\n",
       "      <td>86.3606</td>\n",
       "      <td>100.881</td>\n",
       "      <td>122.704</td>\n",
       "      <td>138.405</td>\n",
       "      <td>156.537</td>\n",
       "      <td>24.5005</td>\n",
       "      <td>37.7778</td>\n",
       "      <td>51.4854</td>\n",
       "      <td>65.5229</td>\n",
       "      <td>80.5380</td>\n",
       "      <td>91.0859</td>\n",
       "      <td>109.639</td>\n",
       "      <td>119.433</td>\n",
       "      <td>18.8125</td>\n",
       "      <td>34.7249</td>\n",
       "      <td>50.6119</td>\n",
       "      <td>66.7613</td>\n",
       "      <td>82.8733</td>\n",
       "      <td>98.5939</td>\n",
       "      <td>114.812</td>\n",
       "      <td>140.382</td>\n",
       "      <td>14.1476</td>\n",
       "      <td>23.4655</td>\n",
       "      <td>35.5519</td>\n",
       "      <td>40.3187</td>\n",
       "      <td>58.0510</td>\n",
       "      <td>67.8035</td>\n",
       "      <td>81.0224</td>\n",
       "      <td>79.5945</td>\n",
       "      <td>5.377155</td>\n",
       "      <td>9.027626</td>\n",
       "      <td>12.251047</td>\n",
       "      <td>15.868360</td>\n",
       "      <td>18.976411</td>\n",
       "      <td>23.086462</td>\n",
       "      <td>26.214039</td>\n",
       "      <td>30.113932</td>\n",
       "      <td>8.265153</td>\n",
       "      <td>15.685308</td>\n",
       "      <td>22.320673</td>\n",
       "      <td>29.065248</td>\n",
       "      <td>36.717139</td>\n",
       "      <td>43.034188</td>\n",
       "      <td>49.826817</td>\n",
       "      <td>57.256970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>432.713</td>\n",
       "      <td>659.096</td>\n",
       "      <td>923.892</td>\n",
       "      <td>1052.84</td>\n",
       "      <td>1279.21</td>\n",
       "      <td>1518.82</td>\n",
       "      <td>1750.41</td>\n",
       "      <td>2001.91</td>\n",
       "      <td>45.2094</td>\n",
       "      <td>65.1705</td>\n",
       "      <td>93.0999</td>\n",
       "      <td>118.322</td>\n",
       "      <td>147.449</td>\n",
       "      <td>177.986</td>\n",
       "      <td>208.853</td>\n",
       "      <td>232.266</td>\n",
       "      <td>34.4193</td>\n",
       "      <td>44.6849</td>\n",
       "      <td>62.7304</td>\n",
       "      <td>81.7072</td>\n",
       "      <td>102.048</td>\n",
       "      <td>122.632</td>\n",
       "      <td>145.012</td>\n",
       "      <td>159.418</td>\n",
       "      <td>31.2672</td>\n",
       "      <td>35.9308</td>\n",
       "      <td>47.0779</td>\n",
       "      <td>61.7812</td>\n",
       "      <td>76.9129</td>\n",
       "      <td>91.1816</td>\n",
       "      <td>108.049</td>\n",
       "      <td>118.891</td>\n",
       "      <td>31.3612</td>\n",
       "      <td>35.9588</td>\n",
       "      <td>52.2726</td>\n",
       "      <td>70.8086</td>\n",
       "      <td>87.4698</td>\n",
       "      <td>102.738</td>\n",
       "      <td>129.737</td>\n",
       "      <td>136.887</td>\n",
       "      <td>18.5886</td>\n",
       "      <td>23.6718</td>\n",
       "      <td>32.6010</td>\n",
       "      <td>41.9060</td>\n",
       "      <td>55.6084</td>\n",
       "      <td>62.9755</td>\n",
       "      <td>83.4863</td>\n",
       "      <td>80.8779</td>\n",
       "      <td>12.564573</td>\n",
       "      <td>15.953150</td>\n",
       "      <td>26.235513</td>\n",
       "      <td>30.995985</td>\n",
       "      <td>34.651298</td>\n",
       "      <td>39.588243</td>\n",
       "      <td>43.248030</td>\n",
       "      <td>47.555765</td>\n",
       "      <td>20.505832</td>\n",
       "      <td>28.337670</td>\n",
       "      <td>40.261453</td>\n",
       "      <td>52.142868</td>\n",
       "      <td>63.143178</td>\n",
       "      <td>72.634436</td>\n",
       "      <td>79.380643</td>\n",
       "      <td>103.862054</td>\n",
       "      <td>721.600</td>\n",
       "      <td>1435.08</td>\n",
       "      <td>2169.87</td>\n",
       "      <td>2895.27</td>\n",
       "      <td>3592.95</td>\n",
       "      <td>4308.88</td>\n",
       "      <td>5067.07</td>\n",
       "      <td>5730.08</td>\n",
       "      <td>98.0957</td>\n",
       "      <td>152.185</td>\n",
       "      <td>216.037</td>\n",
       "      <td>275.458</td>\n",
       "      <td>349.021</td>\n",
       "      <td>411.590</td>\n",
       "      <td>468.287</td>\n",
       "      <td>522.837</td>\n",
       "      <td>67.8081</td>\n",
       "      <td>113.434</td>\n",
       "      <td>158.556</td>\n",
       "      <td>204.409</td>\n",
       "      <td>248.164</td>\n",
       "      <td>294.533</td>\n",
       "      <td>339.103</td>\n",
       "      <td>386.597</td>\n",
       "      <td>58.1552</td>\n",
       "      <td>85.6629</td>\n",
       "      <td>123.356</td>\n",
       "      <td>151.150</td>\n",
       "      <td>178.184</td>\n",
       "      <td>209.654</td>\n",
       "      <td>244.806</td>\n",
       "      <td>274.295</td>\n",
       "      <td>49.4076</td>\n",
       "      <td>93.7518</td>\n",
       "      <td>134.373</td>\n",
       "      <td>176.455</td>\n",
       "      <td>219.154</td>\n",
       "      <td>262.079</td>\n",
       "      <td>305.265</td>\n",
       "      <td>349.635</td>\n",
       "      <td>29.6013</td>\n",
       "      <td>54.6102</td>\n",
       "      <td>80.4847</td>\n",
       "      <td>90.4593</td>\n",
       "      <td>129.616</td>\n",
       "      <td>153.667</td>\n",
       "      <td>181.383</td>\n",
       "      <td>177.023</td>\n",
       "      <td>14.576308</td>\n",
       "      <td>24.137461</td>\n",
       "      <td>34.076772</td>\n",
       "      <td>43.854451</td>\n",
       "      <td>52.229500</td>\n",
       "      <td>64.333019</td>\n",
       "      <td>73.949804</td>\n",
       "      <td>83.629536</td>\n",
       "      <td>24.706514</td>\n",
       "      <td>46.235344</td>\n",
       "      <td>68.337544</td>\n",
       "      <td>88.754744</td>\n",
       "      <td>109.368577</td>\n",
       "      <td>132.970562</td>\n",
       "      <td>153.970342</td>\n",
       "      <td>177.000647</td>\n",
       "      <td>255.276</td>\n",
       "      <td>504.233</td>\n",
       "      <td>752.891</td>\n",
       "      <td>1003.51</td>\n",
       "      <td>1260.40</td>\n",
       "      <td>1505.16</td>\n",
       "      <td>1763.89</td>\n",
       "      <td>2005.91</td>\n",
       "      <td>50.4575</td>\n",
       "      <td>86.1704</td>\n",
       "      <td>133.083</td>\n",
       "      <td>162.627</td>\n",
       "      <td>223.921</td>\n",
       "      <td>234.237</td>\n",
       "      <td>276.680</td>\n",
       "      <td>307.690</td>\n",
       "      <td>41.2710</td>\n",
       "      <td>72.8455</td>\n",
       "      <td>104.214</td>\n",
       "      <td>133.928</td>\n",
       "      <td>166.171</td>\n",
       "      <td>194.790</td>\n",
       "      <td>224.973</td>\n",
       "      <td>256.939</td>\n",
       "      <td>36.6022</td>\n",
       "      <td>60.9524</td>\n",
       "      <td>83.6341</td>\n",
       "      <td>110.041</td>\n",
       "      <td>132.381</td>\n",
       "      <td>156.894</td>\n",
       "      <td>183.169</td>\n",
       "      <td>206.880</td>\n",
       "      <td>27.4926</td>\n",
       "      <td>53.3372</td>\n",
       "      <td>78.2673</td>\n",
       "      <td>103.641</td>\n",
       "      <td>128.886</td>\n",
       "      <td>156.209</td>\n",
       "      <td>179.570</td>\n",
       "      <td>204.565</td>\n",
       "      <td>20.2637</td>\n",
       "      <td>39.0800</td>\n",
       "      <td>59.0290</td>\n",
       "      <td>72.2732</td>\n",
       "      <td>97.8786</td>\n",
       "      <td>114.888</td>\n",
       "      <td>136.398</td>\n",
       "      <td>143.718</td>\n",
       "      <td>8.738153</td>\n",
       "      <td>15.922834</td>\n",
       "      <td>23.167041</td>\n",
       "      <td>30.219143</td>\n",
       "      <td>37.072928</td>\n",
       "      <td>44.407293</td>\n",
       "      <td>51.829131</td>\n",
       "      <td>58.934180</td>\n",
       "      <td>15.716466</td>\n",
       "      <td>30.406879</td>\n",
       "      <td>44.934661</td>\n",
       "      <td>60.149180</td>\n",
       "      <td>74.971699</td>\n",
       "      <td>89.615848</td>\n",
       "      <td>103.730684</td>\n",
       "      <td>118.910782</td>\n",
       "      <td>136.626</td>\n",
       "      <td>273.057</td>\n",
       "      <td>404.040</td>\n",
       "      <td>538.226</td>\n",
       "      <td>672.894</td>\n",
       "      <td>807.042</td>\n",
       "      <td>944.726</td>\n",
       "      <td>1072.82</td>\n",
       "      <td>28.6826</td>\n",
       "      <td>49.9741</td>\n",
       "      <td>67.6147</td>\n",
       "      <td>92.3384</td>\n",
       "      <td>113.252</td>\n",
       "      <td>124.526</td>\n",
       "      <td>145.605</td>\n",
       "      <td>174.297</td>\n",
       "      <td>28.7410</td>\n",
       "      <td>55.7377</td>\n",
       "      <td>64.3055</td>\n",
       "      <td>85.7521</td>\n",
       "      <td>104.219</td>\n",
       "      <td>120.847</td>\n",
       "      <td>140.048</td>\n",
       "      <td>157.109</td>\n",
       "      <td>25.6961</td>\n",
       "      <td>38.3359</td>\n",
       "      <td>51.5314</td>\n",
       "      <td>64.8517</td>\n",
       "      <td>78.0804</td>\n",
       "      <td>94.0153</td>\n",
       "      <td>107.549</td>\n",
       "      <td>118.776</td>\n",
       "      <td>19.9246</td>\n",
       "      <td>34.9084</td>\n",
       "      <td>52.9416</td>\n",
       "      <td>66.9742</td>\n",
       "      <td>85.6101</td>\n",
       "      <td>100.4660</td>\n",
       "      <td>116.996</td>\n",
       "      <td>131.114</td>\n",
       "      <td>12.5772</td>\n",
       "      <td>23.5206</td>\n",
       "      <td>35.4917</td>\n",
       "      <td>40.2868</td>\n",
       "      <td>58.1685</td>\n",
       "      <td>67.6630</td>\n",
       "      <td>81.1855</td>\n",
       "      <td>80.5705</td>\n",
       "      <td>5.414945</td>\n",
       "      <td>9.028205</td>\n",
       "      <td>12.367309</td>\n",
       "      <td>15.945623</td>\n",
       "      <td>18.924622</td>\n",
       "      <td>23.001304</td>\n",
       "      <td>26.271934</td>\n",
       "      <td>30.275458</td>\n",
       "      <td>8.238364</td>\n",
       "      <td>15.143676</td>\n",
       "      <td>22.175621</td>\n",
       "      <td>29.047458</td>\n",
       "      <td>36.331982</td>\n",
       "      <td>43.000188</td>\n",
       "      <td>49.500343</td>\n",
       "      <td>57.296445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>438.301</td>\n",
       "      <td>654.448</td>\n",
       "      <td>927.562</td>\n",
       "      <td>1050.14</td>\n",
       "      <td>1294.98</td>\n",
       "      <td>1527.61</td>\n",
       "      <td>1767.41</td>\n",
       "      <td>1982.13</td>\n",
       "      <td>45.0862</td>\n",
       "      <td>65.9721</td>\n",
       "      <td>91.3594</td>\n",
       "      <td>118.151</td>\n",
       "      <td>149.036</td>\n",
       "      <td>177.760</td>\n",
       "      <td>208.871</td>\n",
       "      <td>235.624</td>\n",
       "      <td>32.9734</td>\n",
       "      <td>47.2591</td>\n",
       "      <td>62.8578</td>\n",
       "      <td>81.7499</td>\n",
       "      <td>102.393</td>\n",
       "      <td>123.046</td>\n",
       "      <td>144.910</td>\n",
       "      <td>163.115</td>\n",
       "      <td>29.6781</td>\n",
       "      <td>35.9344</td>\n",
       "      <td>48.0640</td>\n",
       "      <td>62.2660</td>\n",
       "      <td>77.0286</td>\n",
       "      <td>90.3517</td>\n",
       "      <td>110.727</td>\n",
       "      <td>118.251</td>\n",
       "      <td>24.3868</td>\n",
       "      <td>35.5733</td>\n",
       "      <td>52.1966</td>\n",
       "      <td>69.1779</td>\n",
       "      <td>87.6997</td>\n",
       "      <td>103.069</td>\n",
       "      <td>128.318</td>\n",
       "      <td>138.502</td>\n",
       "      <td>18.7945</td>\n",
       "      <td>23.4064</td>\n",
       "      <td>32.5589</td>\n",
       "      <td>41.7362</td>\n",
       "      <td>56.0369</td>\n",
       "      <td>63.1902</td>\n",
       "      <td>84.1145</td>\n",
       "      <td>80.9076</td>\n",
       "      <td>12.914520</td>\n",
       "      <td>15.990307</td>\n",
       "      <td>26.120513</td>\n",
       "      <td>30.967563</td>\n",
       "      <td>34.610877</td>\n",
       "      <td>39.469033</td>\n",
       "      <td>43.646662</td>\n",
       "      <td>47.390291</td>\n",
       "      <td>20.473990</td>\n",
       "      <td>28.287670</td>\n",
       "      <td>40.066032</td>\n",
       "      <td>52.086184</td>\n",
       "      <td>63.218810</td>\n",
       "      <td>71.585332</td>\n",
       "      <td>78.854327</td>\n",
       "      <td>103.895998</td>\n",
       "      <td>721.741</td>\n",
       "      <td>1452.44</td>\n",
       "      <td>2165.08</td>\n",
       "      <td>2882.01</td>\n",
       "      <td>3600.86</td>\n",
       "      <td>4362.74</td>\n",
       "      <td>5023.24</td>\n",
       "      <td>5731.63</td>\n",
       "      <td>107.8460</td>\n",
       "      <td>153.025</td>\n",
       "      <td>216.775</td>\n",
       "      <td>276.615</td>\n",
       "      <td>349.793</td>\n",
       "      <td>401.385</td>\n",
       "      <td>472.324</td>\n",
       "      <td>522.547</td>\n",
       "      <td>67.1422</td>\n",
       "      <td>113.910</td>\n",
       "      <td>159.451</td>\n",
       "      <td>203.029</td>\n",
       "      <td>248.568</td>\n",
       "      <td>300.100</td>\n",
       "      <td>347.853</td>\n",
       "      <td>385.031</td>\n",
       "      <td>55.7363</td>\n",
       "      <td>86.0695</td>\n",
       "      <td>117.326</td>\n",
       "      <td>149.771</td>\n",
       "      <td>177.301</td>\n",
       "      <td>211.429</td>\n",
       "      <td>242.718</td>\n",
       "      <td>274.347</td>\n",
       "      <td>51.2019</td>\n",
       "      <td>91.8544</td>\n",
       "      <td>133.610</td>\n",
       "      <td>176.885</td>\n",
       "      <td>221.270</td>\n",
       "      <td>263.535</td>\n",
       "      <td>306.503</td>\n",
       "      <td>348.834</td>\n",
       "      <td>29.6630</td>\n",
       "      <td>54.6593</td>\n",
       "      <td>80.3993</td>\n",
       "      <td>90.5865</td>\n",
       "      <td>131.376</td>\n",
       "      <td>153.424</td>\n",
       "      <td>183.069</td>\n",
       "      <td>178.549</td>\n",
       "      <td>14.585309</td>\n",
       "      <td>24.254303</td>\n",
       "      <td>34.172509</td>\n",
       "      <td>43.804714</td>\n",
       "      <td>52.229815</td>\n",
       "      <td>64.391072</td>\n",
       "      <td>74.062541</td>\n",
       "      <td>83.874746</td>\n",
       "      <td>24.812513</td>\n",
       "      <td>46.118398</td>\n",
       "      <td>68.308860</td>\n",
       "      <td>89.036270</td>\n",
       "      <td>108.333629</td>\n",
       "      <td>132.811038</td>\n",
       "      <td>153.733921</td>\n",
       "      <td>176.663119</td>\n",
       "      <td>252.807</td>\n",
       "      <td>504.196</td>\n",
       "      <td>755.365</td>\n",
       "      <td>1011.79</td>\n",
       "      <td>1256.57</td>\n",
       "      <td>1505.98</td>\n",
       "      <td>1752.51</td>\n",
       "      <td>2027.76</td>\n",
       "      <td>55.7264</td>\n",
       "      <td>89.8054</td>\n",
       "      <td>123.676</td>\n",
       "      <td>163.530</td>\n",
       "      <td>197.104</td>\n",
       "      <td>235.000</td>\n",
       "      <td>271.152</td>\n",
       "      <td>314.576</td>\n",
       "      <td>41.3705</td>\n",
       "      <td>71.8533</td>\n",
       "      <td>107.186</td>\n",
       "      <td>136.559</td>\n",
       "      <td>163.876</td>\n",
       "      <td>197.505</td>\n",
       "      <td>227.388</td>\n",
       "      <td>257.944</td>\n",
       "      <td>35.2385</td>\n",
       "      <td>58.9193</td>\n",
       "      <td>83.7032</td>\n",
       "      <td>112.536</td>\n",
       "      <td>134.470</td>\n",
       "      <td>158.866</td>\n",
       "      <td>185.909</td>\n",
       "      <td>205.637</td>\n",
       "      <td>27.6478</td>\n",
       "      <td>54.8117</td>\n",
       "      <td>78.6203</td>\n",
       "      <td>103.442</td>\n",
       "      <td>128.613</td>\n",
       "      <td>154.058</td>\n",
       "      <td>181.353</td>\n",
       "      <td>204.765</td>\n",
       "      <td>20.1769</td>\n",
       "      <td>39.1380</td>\n",
       "      <td>59.1023</td>\n",
       "      <td>71.9235</td>\n",
       "      <td>97.8536</td>\n",
       "      <td>117.108</td>\n",
       "      <td>136.875</td>\n",
       "      <td>143.840</td>\n",
       "      <td>8.700311</td>\n",
       "      <td>15.959466</td>\n",
       "      <td>23.222724</td>\n",
       "      <td>30.188668</td>\n",
       "      <td>37.368454</td>\n",
       "      <td>44.368872</td>\n",
       "      <td>52.408868</td>\n",
       "      <td>58.617443</td>\n",
       "      <td>15.731940</td>\n",
       "      <td>30.546563</td>\n",
       "      <td>45.055346</td>\n",
       "      <td>59.989180</td>\n",
       "      <td>74.683594</td>\n",
       "      <td>89.681428</td>\n",
       "      <td>104.923155</td>\n",
       "      <td>119.409730</td>\n",
       "      <td>137.120</td>\n",
       "      <td>270.515</td>\n",
       "      <td>406.419</td>\n",
       "      <td>540.875</td>\n",
       "      <td>671.978</td>\n",
       "      <td>804.237</td>\n",
       "      <td>938.090</td>\n",
       "      <td>1086.13</td>\n",
       "      <td>43.3900</td>\n",
       "      <td>49.6096</td>\n",
       "      <td>69.9393</td>\n",
       "      <td>96.1416</td>\n",
       "      <td>105.192</td>\n",
       "      <td>133.248</td>\n",
       "      <td>150.738</td>\n",
       "      <td>165.273</td>\n",
       "      <td>28.3507</td>\n",
       "      <td>49.6884</td>\n",
       "      <td>68.0167</td>\n",
       "      <td>83.7830</td>\n",
       "      <td>102.938</td>\n",
       "      <td>119.593</td>\n",
       "      <td>138.398</td>\n",
       "      <td>159.291</td>\n",
       "      <td>25.9034</td>\n",
       "      <td>38.8967</td>\n",
       "      <td>51.4370</td>\n",
       "      <td>64.4683</td>\n",
       "      <td>78.3837</td>\n",
       "      <td>91.8120</td>\n",
       "      <td>109.073</td>\n",
       "      <td>129.538</td>\n",
       "      <td>18.9201</td>\n",
       "      <td>35.1602</td>\n",
       "      <td>52.8884</td>\n",
       "      <td>67.0700</td>\n",
       "      <td>82.6389</td>\n",
       "      <td>98.8752</td>\n",
       "      <td>114.821</td>\n",
       "      <td>131.181</td>\n",
       "      <td>12.5294</td>\n",
       "      <td>23.4695</td>\n",
       "      <td>35.6299</td>\n",
       "      <td>40.3271</td>\n",
       "      <td>58.0681</td>\n",
       "      <td>69.5361</td>\n",
       "      <td>80.9841</td>\n",
       "      <td>80.4278</td>\n",
       "      <td>5.411576</td>\n",
       "      <td>8.962101</td>\n",
       "      <td>12.304204</td>\n",
       "      <td>15.835097</td>\n",
       "      <td>19.173148</td>\n",
       "      <td>23.013410</td>\n",
       "      <td>26.415460</td>\n",
       "      <td>30.134406</td>\n",
       "      <td>8.144259</td>\n",
       "      <td>15.116308</td>\n",
       "      <td>22.011515</td>\n",
       "      <td>29.676406</td>\n",
       "      <td>36.023297</td>\n",
       "      <td>42.970767</td>\n",
       "      <td>49.993396</td>\n",
       "      <td>58.138023</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "model      bvlc-alexnet                                                        \\\n",
       "lib                 cpu                                                         \n",
       "batch size           2        4        6        8        10       12       14   \n",
       "repetition                                                                      \n",
       "0               429.404  657.945  926.569  1058.14  1293.97  1530.73  1768.87   \n",
       "1               432.713  659.096  923.892  1052.84  1279.21  1518.82  1750.41   \n",
       "2               438.301  654.448  927.562  1050.14  1294.98  1527.61  1767.41   \n",
       "\n",
       "model                                                                         \\\n",
       "lib                 libdnn-cuda                                                \n",
       "batch size       16          2        4        6        8        10       12   \n",
       "repetition                                                                     \n",
       "0           1983.67     43.4558  65.6589  93.9803  120.833  147.654  179.296   \n",
       "1           2001.91     45.2094  65.1705  93.0999  118.322  147.449  177.986   \n",
       "2           1982.13     45.0862  65.9721  91.3594  118.151  149.036  177.760   \n",
       "\n",
       "model                                                                         \\\n",
       "lib                          nvidia-cuda                                       \n",
       "batch size       14       16          2        4        6        8        10   \n",
       "repetition                                                                     \n",
       "0           208.372  233.930     32.6553  44.5101  63.0471  82.2302  104.912   \n",
       "1           208.853  232.266     34.4193  44.6849  62.7304  81.7072  102.048   \n",
       "2           208.871  235.624     32.9734  47.2591  62.8578  81.7499  102.393   \n",
       "\n",
       "model                                                                          \\\n",
       "lib                                   nvidia-cudnn                              \n",
       "batch size       12       14       16           2        4        6        8    \n",
       "repetition                                                                      \n",
       "0           123.494  146.433  161.290      30.9620  36.1724  49.5168  61.3727   \n",
       "1           122.632  145.012  159.418      31.2672  35.9308  47.0779  61.7812   \n",
       "2           123.046  144.910  163.115      29.6781  35.9344  48.0640  62.2660   \n",
       "\n",
       "model                                                                     \\\n",
       "lib                                            nvidia-fp16-cuda            \n",
       "batch size       10       12       14       16               2        4    \n",
       "repetition                                                                 \n",
       "0           77.8906  90.4233  108.288  118.699          24.7638  35.6854   \n",
       "1           76.9129  91.1816  108.049  118.891          31.3612  35.9588   \n",
       "2           77.0286  90.3517  110.727  118.251          24.3868  35.5733   \n",
       "\n",
       "model                                                             \\\n",
       "lib                                                                \n",
       "batch size       6        8        10       12       14       16   \n",
       "repetition                                                         \n",
       "0           54.4630  69.3397  87.9188  103.192  128.973  138.174   \n",
       "1           52.2726  70.8086  87.4698  102.738  129.737  136.887   \n",
       "2           52.1966  69.1779  87.6997  103.069  128.318  138.502   \n",
       "\n",
       "model                                                                      \\\n",
       "lib        nvidia-fp16-cudnn                                                \n",
       "batch size                2        4        6        8        10       12   \n",
       "repetition                                                                  \n",
       "0                    18.7560  23.4035  32.4812  41.8005  56.1197  62.9113   \n",
       "1                    18.5886  23.6718  32.6010  41.9060  55.6084  62.9755   \n",
       "2                    18.7945  23.4064  32.5589  41.7362  56.0369  63.1902   \n",
       "\n",
       "model                                                                        \\\n",
       "lib                          tensorrt-fp16                                    \n",
       "batch size       14       16            2          4          6          8    \n",
       "repetition                                                                    \n",
       "0           83.7968  81.3277     12.620625  16.090150  27.783512  30.959194   \n",
       "1           83.4863  80.8779     12.564573  15.953150  26.235513  30.995985   \n",
       "2           84.1145  80.9076     12.914520  15.990307  26.120513  30.967563   \n",
       "\n",
       "model                                                                 \\\n",
       "lib                                                    tensorrt-fp32   \n",
       "batch size         10         12         14         16            2    \n",
       "repetition                                                             \n",
       "0           34.830140  39.653559  43.443609  47.710660     29.353353   \n",
       "1           34.651298  39.588243  43.248030  47.555765     20.505832   \n",
       "2           34.610877  39.469033  43.646662  47.390291     20.473990   \n",
       "\n",
       "model                                                                         \\\n",
       "lib                                                                            \n",
       "batch size         4          6          8          10         12         14   \n",
       "repetition                                                                     \n",
       "0           28.293512  40.110348  52.178815  63.779229  71.943542  79.590329   \n",
       "1           28.337670  40.261453  52.142868  63.143178  72.634436  79.380643   \n",
       "2           28.287670  40.066032  52.086184  63.218810  71.585332  78.854327   \n",
       "\n",
       "model                  bvlc-googlenet                                      \\\n",
       "lib                               cpu                                       \n",
       "batch size          16             2        4        6        8        10   \n",
       "repetition                                                                  \n",
       "0           104.076155        726.576  1433.93  2161.43  2872.77  3577.71   \n",
       "1           103.862054        721.600  1435.08  2169.87  2895.27  3592.95   \n",
       "2           103.895998        721.741  1452.44  2165.08  2882.01  3600.86   \n",
       "\n",
       "model                                                                         \\\n",
       "lib                                   libdnn-cuda                              \n",
       "batch size       12       14       16          2        4        6        8    \n",
       "repetition                                                                     \n",
       "0           4307.93  5008.49  5737.78     95.8321  150.999  215.748  275.590   \n",
       "1           4308.88  5067.07  5730.08     98.0957  152.185  216.037  275.458   \n",
       "2           4362.74  5023.24  5731.63    107.8460  153.025  216.775  276.615   \n",
       "\n",
       "model                                                                         \\\n",
       "lib                                            nvidia-cuda                     \n",
       "batch size       10       12       14       16          2        4        6    \n",
       "repetition                                                                     \n",
       "0           338.860  414.334  462.678  523.130     72.5030  114.992  158.350   \n",
       "1           349.021  411.590  468.287  522.837     67.8081  113.434  158.556   \n",
       "2           349.793  401.385  472.324  522.547     67.1422  113.910  159.451   \n",
       "\n",
       "model                                                                          \\\n",
       "lib                                                     nvidia-cudnn            \n",
       "batch size       8        10       12       14       16           2        4    \n",
       "repetition                                                                      \n",
       "0           203.071  248.787  298.789  342.822  383.233      55.0224  86.4351   \n",
       "1           204.409  248.164  294.533  339.103  386.597      58.1552  85.6629   \n",
       "2           203.029  248.568  300.100  347.853  385.031      55.7363  86.0695   \n",
       "\n",
       "model                                                             \\\n",
       "lib                                                                \n",
       "batch size       6        8        10       12       14       16   \n",
       "repetition                                                         \n",
       "0           117.502  148.409  182.625  211.654  243.337  272.625   \n",
       "1           123.356  151.150  178.184  209.654  244.806  274.295   \n",
       "2           117.326  149.771  177.301  211.429  242.718  274.347   \n",
       "\n",
       "model                                                                     \\\n",
       "lib        nvidia-fp16-cuda                                                \n",
       "batch size               2        4        6        8        10       12   \n",
       "repetition                                                                 \n",
       "0                   49.4953  91.8936  134.170  176.348  219.665  263.124   \n",
       "1                   49.4076  93.7518  134.373  176.455  219.154  262.079   \n",
       "2                   51.2019  91.8544  133.610  176.885  221.270  263.535   \n",
       "\n",
       "model                                                                      \\\n",
       "lib                          nvidia-fp16-cudnn                              \n",
       "batch size       14       16                2        4        6        8    \n",
       "repetition                                                                  \n",
       "0           306.364  348.909           29.6434  54.7612  80.5735  90.6051   \n",
       "1           305.265  349.635           29.6013  54.6102  80.4847  90.4593   \n",
       "2           306.503  348.834           29.6630  54.6593  80.3993  90.5865   \n",
       "\n",
       "model                                                                    \\\n",
       "lib                                            tensorrt-fp16              \n",
       "batch size       10       12       14       16            2          4    \n",
       "repetition                                                                \n",
       "0           128.923  153.502  183.439  178.709     14.485308  24.238514   \n",
       "1           129.616  153.667  181.383  177.023     14.576308  24.137461   \n",
       "2           131.376  153.424  183.069  178.549     14.585309  24.254303   \n",
       "\n",
       "model                                                                         \\\n",
       "lib                                                                            \n",
       "batch size         6          8          10         12         14         16   \n",
       "repetition                                                                     \n",
       "0           34.025298  44.013294  52.217868  64.078599  74.021542  83.507063   \n",
       "1           34.076772  43.854451  52.229500  64.333019  73.949804  83.629536   \n",
       "2           34.172509  43.804714  52.229815  64.391072  74.062541  83.874746   \n",
       "\n",
       "model                                                                  \\\n",
       "lib        tensorrt-fp32                                                \n",
       "batch size            2          4          6          8           10   \n",
       "repetition                                                              \n",
       "0              24.654566  46.024871  68.209123  89.629849  108.358576   \n",
       "1              24.706514  46.235344  68.337544  88.754744  109.368577   \n",
       "2              24.812513  46.118398  68.308860  89.036270  108.333629   \n",
       "\n",
       "model                                          deepscale-squeezenet-1.0  \\\n",
       "lib                                                                 cpu   \n",
       "batch size          12          14          16                       2    \n",
       "repetition                                                                \n",
       "0           132.795089  153.733922  176.017908                  253.702   \n",
       "1           132.970562  153.970342  177.000647                  255.276   \n",
       "2           132.811038  153.733921  176.663119                  252.807   \n",
       "\n",
       "model                                                                      \\\n",
       "lib                                                                         \n",
       "batch size       4        6        8        10       12       14       16   \n",
       "repetition                                                                  \n",
       "0           502.686  756.378  1002.24  1253.52  1504.42  1754.85  2017.42   \n",
       "1           504.233  752.891  1003.51  1260.40  1505.16  1763.89  2005.91   \n",
       "2           504.196  755.365  1011.79  1256.57  1505.98  1752.51  2027.76   \n",
       "\n",
       "model                                                                         \\\n",
       "lib        libdnn-cuda                                                         \n",
       "batch size          2        4        6        8        10       12       14   \n",
       "repetition                                                                     \n",
       "0              48.6512  88.4245  126.186  166.935  196.310  233.976  270.913   \n",
       "1              50.4575  86.1704  133.083  162.627  223.921  234.237  276.680   \n",
       "2              55.7264  89.8054  123.676  163.530  197.104  235.000  271.152   \n",
       "\n",
       "model                                                                         \\\n",
       "lib                 nvidia-cuda                                                \n",
       "batch size       16          2        4        6        8        10       12   \n",
       "repetition                                                                     \n",
       "0           314.305     40.5426  77.5703  103.481  133.831  166.995  195.475   \n",
       "1           307.690     41.2710  72.8455  104.214  133.928  166.171  194.790   \n",
       "2           314.576     41.3705  71.8533  107.186  136.559  163.876  197.505   \n",
       "\n",
       "model                                                                          \\\n",
       "lib                          nvidia-cudnn                                       \n",
       "batch size       14       16           2        4        6        8        10   \n",
       "repetition                                                                      \n",
       "0           225.449  255.899      34.3870  59.6457  83.1643  108.407  134.133   \n",
       "1           224.973  256.939      36.6022  60.9524  83.6341  110.041  132.381   \n",
       "2           227.388  257.944      35.2385  58.9193  83.7032  112.536  134.470   \n",
       "\n",
       "model                                                                     \\\n",
       "lib                                   nvidia-fp16-cuda                     \n",
       "batch size       12       14       16               2        4        6    \n",
       "repetition                                                                 \n",
       "0           156.495  185.548  211.621          27.8536  53.2180  78.1169   \n",
       "1           156.894  183.169  206.880          27.4926  53.3372  78.2673   \n",
       "2           158.866  185.909  205.637          27.6478  54.8117  78.6203   \n",
       "\n",
       "model                                                                      \\\n",
       "lib                                                     nvidia-fp16-cudnn   \n",
       "batch size       8        10       12       14       16                2    \n",
       "repetition                                                                  \n",
       "0           104.016  128.267  155.909  179.802  206.533           20.2856   \n",
       "1           103.641  128.886  156.209  179.570  204.565           20.2637   \n",
       "2           103.442  128.613  154.058  181.353  204.765           20.1769   \n",
       "\n",
       "model                                                                      \\\n",
       "lib                                                                         \n",
       "batch size       4        6        8        10       12       14       16   \n",
       "repetition                                                                  \n",
       "0           39.2223  58.9879  75.8102  98.1925  116.559  136.638  142.985   \n",
       "1           39.0800  59.0290  72.2732  97.8786  114.888  136.398  143.718   \n",
       "2           39.1380  59.1023  71.9235  97.8536  117.108  136.875  143.840   \n",
       "\n",
       "model                                                                 \\\n",
       "lib        tensorrt-fp16                                               \n",
       "batch size            2          4          6          8          10   \n",
       "repetition                                                             \n",
       "0               8.757838  15.879676  23.154199  30.145195  37.440718   \n",
       "1               8.738153  15.922834  23.167041  30.219143  37.072928   \n",
       "2               8.700311  15.959466  23.222724  30.188668  37.368454   \n",
       "\n",
       "model                                                                 \\\n",
       "lib                                         tensorrt-fp32              \n",
       "batch size         12         14         16            2          4    \n",
       "repetition                                                             \n",
       "0           45.281240  51.842710  58.747601     15.774518  30.450721   \n",
       "1           44.407293  51.829131  58.934180     15.716466  30.406879   \n",
       "2           44.368872  52.408868  58.617443     15.731940  30.546563   \n",
       "\n",
       "model                                                               \\\n",
       "lib                                                                  \n",
       "batch size         6          8          10         12          14   \n",
       "repetition                                                           \n",
       "0           45.192555  59.673022  75.474067  90.008849  104.018262   \n",
       "1           44.934661  60.149180  74.971699  89.615848  103.730684   \n",
       "2           45.055346  59.989180  74.683594  89.681428  104.923155   \n",
       "\n",
       "model                  deepscale-squeezenet-1.1                             \\\n",
       "lib                                         cpu                              \n",
       "batch size          16                       2        4        6        8    \n",
       "repetition                                                                   \n",
       "0           119.386255                  136.270  270.304  408.191  536.611   \n",
       "1           118.910782                  136.626  273.057  404.040  538.226   \n",
       "2           119.409730                  137.120  270.515  406.419  540.875   \n",
       "\n",
       "model                                                                         \\\n",
       "lib                                            libdnn-cuda                     \n",
       "batch size       10       12       14       16          2        4        6    \n",
       "repetition                                                                     \n",
       "0           669.522  808.527  937.107  1078.19     32.2915  58.5859  76.8803   \n",
       "1           672.894  807.042  944.726  1072.82     28.6826  49.9741  67.6147   \n",
       "2           671.978  804.237  938.090  1086.13     43.3900  49.6096  69.9393   \n",
       "\n",
       "model                                                                         \\\n",
       "lib                                                     nvidia-cuda            \n",
       "batch size       8        10       12       14       16          2        4    \n",
       "repetition                                                                     \n",
       "0           94.8715  113.236  125.267  151.743  170.049     30.3045  53.8181   \n",
       "1           92.3384  113.252  124.526  145.605  174.297     28.7410  55.7377   \n",
       "2           96.1416  105.192  133.248  150.738  165.273     28.3507  49.6884   \n",
       "\n",
       "model                                                                          \\\n",
       "lib                                                              nvidia-cudnn   \n",
       "batch size       6        8        10       12       14       16           2    \n",
       "repetition                                                                      \n",
       "0           64.2115  86.3606  100.881  122.704  138.405  156.537      24.5005   \n",
       "1           64.3055  85.7521  104.219  120.847  140.048  157.109      25.6961   \n",
       "2           68.0167  83.7830  102.938  119.593  138.398  159.291      25.9034   \n",
       "\n",
       "model                                                                      \\\n",
       "lib                                                                         \n",
       "batch size       4        6        8        10       12       14       16   \n",
       "repetition                                                                  \n",
       "0           37.7778  51.4854  65.5229  80.5380  91.0859  109.639  119.433   \n",
       "1           38.3359  51.5314  64.8517  78.0804  94.0153  107.549  118.776   \n",
       "2           38.8967  51.4370  64.4683  78.3837  91.8120  109.073  129.538   \n",
       "\n",
       "model                                                                      \\\n",
       "lib        nvidia-fp16-cuda                                                 \n",
       "batch size               2        4        6        8        10        12   \n",
       "repetition                                                                  \n",
       "0                   18.8125  34.7249  50.6119  66.7613  82.8733   98.5939   \n",
       "1                   19.9246  34.9084  52.9416  66.9742  85.6101  100.4660   \n",
       "2                   18.9201  35.1602  52.8884  67.0700  82.6389   98.8752   \n",
       "\n",
       "model                                                                      \\\n",
       "lib                          nvidia-fp16-cudnn                              \n",
       "batch size       14       16                2        4        6        8    \n",
       "repetition                                                                  \n",
       "0           114.812  140.382           14.1476  23.4655  35.5519  40.3187   \n",
       "1           116.996  131.114           12.5772  23.5206  35.4917  40.2868   \n",
       "2           114.821  131.181           12.5294  23.4695  35.6299  40.3271   \n",
       "\n",
       "model                                                                   \\\n",
       "lib                                            tensorrt-fp16             \n",
       "batch size       10       12       14       16            2         4    \n",
       "repetition                                                               \n",
       "0           58.0510  67.8035  81.0224  79.5945      5.377155  9.027626   \n",
       "1           58.1685  67.6630  81.1855  80.5705      5.414945  9.028205   \n",
       "2           58.0681  69.5361  80.9841  80.4278      5.411576  8.962101   \n",
       "\n",
       "model                                                                         \\\n",
       "lib                                                                            \n",
       "batch size         6          8          10         12         14         16   \n",
       "repetition                                                                     \n",
       "0           12.251047  15.868360  18.976411  23.086462  26.214039  30.113932   \n",
       "1           12.367309  15.945623  18.924622  23.001304  26.271934  30.275458   \n",
       "2           12.304204  15.835097  19.173148  23.013410  26.415460  30.134406   \n",
       "\n",
       "model                                                                 \\\n",
       "lib        tensorrt-fp32                                               \n",
       "batch size            2          4          6          8          10   \n",
       "repetition                                                             \n",
       "0               8.265153  15.685308  22.320673  29.065248  36.717139   \n",
       "1               8.238364  15.143676  22.175621  29.047458  36.331982   \n",
       "2               8.144259  15.116308  22.011515  29.676406  36.023297   \n",
       "\n",
       "model                                        \n",
       "lib                                          \n",
       "batch size         12         14         16  \n",
       "repetition                                   \n",
       "0           43.034188  49.826817  57.256970  \n",
       "1           43.000188  49.500343  57.296445  \n",
       "2           42.970767  49.993396  58.138023  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.options.display.max_columns = len(df_time.columns)\n",
    "pd.options.display.max_rows = len(df_time.index)\n",
    "df_time"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"df_mean_time_per_batch\"></a>\n",
    "### Mean execution time per batch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df_mean_time_per_batch = df_time.describe().ix['mean'].unstack(level='batch size')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>batch size</th>\n",
       "      <th>2</th>\n",
       "      <th>4</th>\n",
       "      <th>6</th>\n",
       "      <th>8</th>\n",
       "      <th>10</th>\n",
       "      <th>12</th>\n",
       "      <th>14</th>\n",
       "      <th>16</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model</th>\n",
       "      <th>lib</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"8\" valign=\"top\">bvlc-alexnet</th>\n",
       "      <th>cpu</th>\n",
       "      <td>433.472667</td>\n",
       "      <td>657.163000</td>\n",
       "      <td>926.007667</td>\n",
       "      <td>1053.706667</td>\n",
       "      <td>1289.386667</td>\n",
       "      <td>1525.720000</td>\n",
       "      <td>1762.230000</td>\n",
       "      <td>1989.236667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>libdnn-cuda</th>\n",
       "      <td>44.583800</td>\n",
       "      <td>65.600500</td>\n",
       "      <td>92.813200</td>\n",
       "      <td>119.102000</td>\n",
       "      <td>148.046333</td>\n",
       "      <td>178.347333</td>\n",
       "      <td>208.698667</td>\n",
       "      <td>233.940000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nvidia-cuda</th>\n",
       "      <td>33.349333</td>\n",
       "      <td>45.484700</td>\n",
       "      <td>62.878433</td>\n",
       "      <td>81.895767</td>\n",
       "      <td>103.117667</td>\n",
       "      <td>123.057333</td>\n",
       "      <td>145.451667</td>\n",
       "      <td>161.274333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nvidia-cudnn</th>\n",
       "      <td>30.635767</td>\n",
       "      <td>36.012533</td>\n",
       "      <td>48.219567</td>\n",
       "      <td>61.806633</td>\n",
       "      <td>77.277367</td>\n",
       "      <td>90.652200</td>\n",
       "      <td>109.021333</td>\n",
       "      <td>118.613667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nvidia-fp16-cuda</th>\n",
       "      <td>26.837267</td>\n",
       "      <td>35.739167</td>\n",
       "      <td>52.977400</td>\n",
       "      <td>69.775400</td>\n",
       "      <td>87.696100</td>\n",
       "      <td>102.999667</td>\n",
       "      <td>129.009333</td>\n",
       "      <td>137.854333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nvidia-fp16-cudnn</th>\n",
       "      <td>18.713033</td>\n",
       "      <td>23.493900</td>\n",
       "      <td>32.547033</td>\n",
       "      <td>41.814233</td>\n",
       "      <td>55.921667</td>\n",
       "      <td>63.025667</td>\n",
       "      <td>83.799200</td>\n",
       "      <td>81.037733</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tensorrt-fp16</th>\n",
       "      <td>12.699906</td>\n",
       "      <td>16.011202</td>\n",
       "      <td>26.713179</td>\n",
       "      <td>30.974247</td>\n",
       "      <td>34.697438</td>\n",
       "      <td>39.570278</td>\n",
       "      <td>43.446100</td>\n",
       "      <td>47.552239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tensorrt-fp32</th>\n",
       "      <td>23.444392</td>\n",
       "      <td>28.306284</td>\n",
       "      <td>40.145944</td>\n",
       "      <td>52.135956</td>\n",
       "      <td>63.380406</td>\n",
       "      <td>72.054437</td>\n",
       "      <td>79.275100</td>\n",
       "      <td>103.944736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"8\" valign=\"top\">bvlc-googlenet</th>\n",
       "      <th>cpu</th>\n",
       "      <td>723.305667</td>\n",
       "      <td>1440.483333</td>\n",
       "      <td>2165.460000</td>\n",
       "      <td>2883.350000</td>\n",
       "      <td>3590.506667</td>\n",
       "      <td>4326.516667</td>\n",
       "      <td>5032.933333</td>\n",
       "      <td>5733.163333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>libdnn-cuda</th>\n",
       "      <td>100.591267</td>\n",
       "      <td>152.069667</td>\n",
       "      <td>216.186667</td>\n",
       "      <td>275.887667</td>\n",
       "      <td>345.891333</td>\n",
       "      <td>409.103000</td>\n",
       "      <td>467.763000</td>\n",
       "      <td>522.838000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nvidia-cuda</th>\n",
       "      <td>69.151100</td>\n",
       "      <td>114.112000</td>\n",
       "      <td>158.785667</td>\n",
       "      <td>203.503000</td>\n",
       "      <td>248.506333</td>\n",
       "      <td>297.807333</td>\n",
       "      <td>343.259333</td>\n",
       "      <td>384.953667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nvidia-cudnn</th>\n",
       "      <td>56.304633</td>\n",
       "      <td>86.055833</td>\n",
       "      <td>119.394667</td>\n",
       "      <td>149.776667</td>\n",
       "      <td>179.370000</td>\n",
       "      <td>210.912333</td>\n",
       "      <td>243.620333</td>\n",
       "      <td>273.755667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nvidia-fp16-cuda</th>\n",
       "      <td>50.034933</td>\n",
       "      <td>92.499933</td>\n",
       "      <td>134.051000</td>\n",
       "      <td>176.562667</td>\n",
       "      <td>220.029667</td>\n",
       "      <td>262.912667</td>\n",
       "      <td>306.044000</td>\n",
       "      <td>349.126000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nvidia-fp16-cudnn</th>\n",
       "      <td>29.635900</td>\n",
       "      <td>54.676900</td>\n",
       "      <td>80.485833</td>\n",
       "      <td>90.550300</td>\n",
       "      <td>129.971667</td>\n",
       "      <td>153.531000</td>\n",
       "      <td>182.630333</td>\n",
       "      <td>178.093667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tensorrt-fp16</th>\n",
       "      <td>14.548975</td>\n",
       "      <td>24.210093</td>\n",
       "      <td>34.091526</td>\n",
       "      <td>43.890820</td>\n",
       "      <td>52.225728</td>\n",
       "      <td>64.267563</td>\n",
       "      <td>74.011296</td>\n",
       "      <td>83.670448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tensorrt-fp32</th>\n",
       "      <td>24.724531</td>\n",
       "      <td>46.126204</td>\n",
       "      <td>68.285176</td>\n",
       "      <td>89.140288</td>\n",
       "      <td>108.686927</td>\n",
       "      <td>132.858896</td>\n",
       "      <td>153.812728</td>\n",
       "      <td>176.560558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"8\" valign=\"top\">deepscale-squeezenet-1.0</th>\n",
       "      <th>cpu</th>\n",
       "      <td>253.928333</td>\n",
       "      <td>503.705000</td>\n",
       "      <td>754.878000</td>\n",
       "      <td>1005.846667</td>\n",
       "      <td>1256.830000</td>\n",
       "      <td>1505.186667</td>\n",
       "      <td>1757.083333</td>\n",
       "      <td>2017.030000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>libdnn-cuda</th>\n",
       "      <td>51.611700</td>\n",
       "      <td>88.133433</td>\n",
       "      <td>127.648333</td>\n",
       "      <td>164.364000</td>\n",
       "      <td>205.778333</td>\n",
       "      <td>234.404333</td>\n",
       "      <td>272.915000</td>\n",
       "      <td>312.190333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nvidia-cuda</th>\n",
       "      <td>41.061367</td>\n",
       "      <td>74.089700</td>\n",
       "      <td>104.960333</td>\n",
       "      <td>134.772667</td>\n",
       "      <td>165.680667</td>\n",
       "      <td>195.923333</td>\n",
       "      <td>225.936667</td>\n",
       "      <td>256.927333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nvidia-cudnn</th>\n",
       "      <td>35.409233</td>\n",
       "      <td>59.839133</td>\n",
       "      <td>83.500533</td>\n",
       "      <td>110.328000</td>\n",
       "      <td>133.661333</td>\n",
       "      <td>157.418333</td>\n",
       "      <td>184.875333</td>\n",
       "      <td>208.046000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nvidia-fp16-cuda</th>\n",
       "      <td>27.664667</td>\n",
       "      <td>53.788967</td>\n",
       "      <td>78.334833</td>\n",
       "      <td>103.699667</td>\n",
       "      <td>128.588667</td>\n",
       "      <td>155.392000</td>\n",
       "      <td>180.241667</td>\n",
       "      <td>205.287667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nvidia-fp16-cudnn</th>\n",
       "      <td>20.242067</td>\n",
       "      <td>39.146767</td>\n",
       "      <td>59.039733</td>\n",
       "      <td>73.335633</td>\n",
       "      <td>97.974900</td>\n",
       "      <td>116.185000</td>\n",
       "      <td>136.637000</td>\n",
       "      <td>143.514333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tensorrt-fp16</th>\n",
       "      <td>8.732101</td>\n",
       "      <td>15.920659</td>\n",
       "      <td>23.181321</td>\n",
       "      <td>30.184335</td>\n",
       "      <td>37.294033</td>\n",
       "      <td>44.685802</td>\n",
       "      <td>52.026903</td>\n",
       "      <td>58.766408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tensorrt-fp32</th>\n",
       "      <td>15.740975</td>\n",
       "      <td>30.468054</td>\n",
       "      <td>45.060854</td>\n",
       "      <td>59.937127</td>\n",
       "      <td>75.043120</td>\n",
       "      <td>89.768708</td>\n",
       "      <td>104.224034</td>\n",
       "      <td>119.235589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"8\" valign=\"top\">deepscale-squeezenet-1.1</th>\n",
       "      <th>cpu</th>\n",
       "      <td>136.672000</td>\n",
       "      <td>271.292000</td>\n",
       "      <td>406.216667</td>\n",
       "      <td>538.570667</td>\n",
       "      <td>671.464667</td>\n",
       "      <td>806.602000</td>\n",
       "      <td>939.974333</td>\n",
       "      <td>1079.046667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>libdnn-cuda</th>\n",
       "      <td>34.788033</td>\n",
       "      <td>52.723200</td>\n",
       "      <td>71.478100</td>\n",
       "      <td>94.450500</td>\n",
       "      <td>110.560000</td>\n",
       "      <td>127.680333</td>\n",
       "      <td>149.362000</td>\n",
       "      <td>169.873000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nvidia-cuda</th>\n",
       "      <td>29.132067</td>\n",
       "      <td>53.081400</td>\n",
       "      <td>65.511233</td>\n",
       "      <td>85.298567</td>\n",
       "      <td>102.679333</td>\n",
       "      <td>121.048000</td>\n",
       "      <td>138.950333</td>\n",
       "      <td>157.645667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nvidia-cudnn</th>\n",
       "      <td>25.366667</td>\n",
       "      <td>38.336800</td>\n",
       "      <td>51.484600</td>\n",
       "      <td>64.947633</td>\n",
       "      <td>79.000700</td>\n",
       "      <td>92.304400</td>\n",
       "      <td>108.753667</td>\n",
       "      <td>122.582333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nvidia-fp16-cuda</th>\n",
       "      <td>19.219067</td>\n",
       "      <td>34.931167</td>\n",
       "      <td>52.147300</td>\n",
       "      <td>66.935167</td>\n",
       "      <td>83.707433</td>\n",
       "      <td>99.311700</td>\n",
       "      <td>115.543000</td>\n",
       "      <td>134.225667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nvidia-fp16-cudnn</th>\n",
       "      <td>13.084733</td>\n",
       "      <td>23.485200</td>\n",
       "      <td>35.557833</td>\n",
       "      <td>40.310867</td>\n",
       "      <td>58.095867</td>\n",
       "      <td>68.334200</td>\n",
       "      <td>81.064000</td>\n",
       "      <td>80.197600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tensorrt-fp16</th>\n",
       "      <td>5.401225</td>\n",
       "      <td>9.005977</td>\n",
       "      <td>12.307520</td>\n",
       "      <td>15.883027</td>\n",
       "      <td>19.024727</td>\n",
       "      <td>23.033725</td>\n",
       "      <td>26.300478</td>\n",
       "      <td>30.174599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tensorrt-fp32</th>\n",
       "      <td>8.215925</td>\n",
       "      <td>15.315097</td>\n",
       "      <td>22.169270</td>\n",
       "      <td>29.263037</td>\n",
       "      <td>36.357473</td>\n",
       "      <td>43.001714</td>\n",
       "      <td>49.773519</td>\n",
       "      <td>57.563813</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "batch size                                          2            4   \\\n",
       "model                    lib                                          \n",
       "bvlc-alexnet             cpu                433.472667   657.163000   \n",
       "                         libdnn-cuda         44.583800    65.600500   \n",
       "                         nvidia-cuda         33.349333    45.484700   \n",
       "                         nvidia-cudnn        30.635767    36.012533   \n",
       "                         nvidia-fp16-cuda    26.837267    35.739167   \n",
       "                         nvidia-fp16-cudnn   18.713033    23.493900   \n",
       "                         tensorrt-fp16       12.699906    16.011202   \n",
       "                         tensorrt-fp32       23.444392    28.306284   \n",
       "bvlc-googlenet           cpu                723.305667  1440.483333   \n",
       "                         libdnn-cuda        100.591267   152.069667   \n",
       "                         nvidia-cuda         69.151100   114.112000   \n",
       "                         nvidia-cudnn        56.304633    86.055833   \n",
       "                         nvidia-fp16-cuda    50.034933    92.499933   \n",
       "                         nvidia-fp16-cudnn   29.635900    54.676900   \n",
       "                         tensorrt-fp16       14.548975    24.210093   \n",
       "                         tensorrt-fp32       24.724531    46.126204   \n",
       "deepscale-squeezenet-1.0 cpu                253.928333   503.705000   \n",
       "                         libdnn-cuda         51.611700    88.133433   \n",
       "                         nvidia-cuda         41.061367    74.089700   \n",
       "                         nvidia-cudnn        35.409233    59.839133   \n",
       "                         nvidia-fp16-cuda    27.664667    53.788967   \n",
       "                         nvidia-fp16-cudnn   20.242067    39.146767   \n",
       "                         tensorrt-fp16        8.732101    15.920659   \n",
       "                         tensorrt-fp32       15.740975    30.468054   \n",
       "deepscale-squeezenet-1.1 cpu                136.672000   271.292000   \n",
       "                         libdnn-cuda         34.788033    52.723200   \n",
       "                         nvidia-cuda         29.132067    53.081400   \n",
       "                         nvidia-cudnn        25.366667    38.336800   \n",
       "                         nvidia-fp16-cuda    19.219067    34.931167   \n",
       "                         nvidia-fp16-cudnn   13.084733    23.485200   \n",
       "                         tensorrt-fp16        5.401225     9.005977   \n",
       "                         tensorrt-fp32        8.215925    15.315097   \n",
       "\n",
       "batch size                                           6            8   \\\n",
       "model                    lib                                           \n",
       "bvlc-alexnet             cpu                 926.007667  1053.706667   \n",
       "                         libdnn-cuda          92.813200   119.102000   \n",
       "                         nvidia-cuda          62.878433    81.895767   \n",
       "                         nvidia-cudnn         48.219567    61.806633   \n",
       "                         nvidia-fp16-cuda     52.977400    69.775400   \n",
       "                         nvidia-fp16-cudnn    32.547033    41.814233   \n",
       "                         tensorrt-fp16        26.713179    30.974247   \n",
       "                         tensorrt-fp32        40.145944    52.135956   \n",
       "bvlc-googlenet           cpu                2165.460000  2883.350000   \n",
       "                         libdnn-cuda         216.186667   275.887667   \n",
       "                         nvidia-cuda         158.785667   203.503000   \n",
       "                         nvidia-cudnn        119.394667   149.776667   \n",
       "                         nvidia-fp16-cuda    134.051000   176.562667   \n",
       "                         nvidia-fp16-cudnn    80.485833    90.550300   \n",
       "                         tensorrt-fp16        34.091526    43.890820   \n",
       "                         tensorrt-fp32        68.285176    89.140288   \n",
       "deepscale-squeezenet-1.0 cpu                 754.878000  1005.846667   \n",
       "                         libdnn-cuda         127.648333   164.364000   \n",
       "                         nvidia-cuda         104.960333   134.772667   \n",
       "                         nvidia-cudnn         83.500533   110.328000   \n",
       "                         nvidia-fp16-cuda     78.334833   103.699667   \n",
       "                         nvidia-fp16-cudnn    59.039733    73.335633   \n",
       "                         tensorrt-fp16        23.181321    30.184335   \n",
       "                         tensorrt-fp32        45.060854    59.937127   \n",
       "deepscale-squeezenet-1.1 cpu                 406.216667   538.570667   \n",
       "                         libdnn-cuda          71.478100    94.450500   \n",
       "                         nvidia-cuda          65.511233    85.298567   \n",
       "                         nvidia-cudnn         51.484600    64.947633   \n",
       "                         nvidia-fp16-cuda     52.147300    66.935167   \n",
       "                         nvidia-fp16-cudnn    35.557833    40.310867   \n",
       "                         tensorrt-fp16        12.307520    15.883027   \n",
       "                         tensorrt-fp32        22.169270    29.263037   \n",
       "\n",
       "batch size                                           10           12  \\\n",
       "model                    lib                                           \n",
       "bvlc-alexnet             cpu                1289.386667  1525.720000   \n",
       "                         libdnn-cuda         148.046333   178.347333   \n",
       "                         nvidia-cuda         103.117667   123.057333   \n",
       "                         nvidia-cudnn         77.277367    90.652200   \n",
       "                         nvidia-fp16-cuda     87.696100   102.999667   \n",
       "                         nvidia-fp16-cudnn    55.921667    63.025667   \n",
       "                         tensorrt-fp16        34.697438    39.570278   \n",
       "                         tensorrt-fp32        63.380406    72.054437   \n",
       "bvlc-googlenet           cpu                3590.506667  4326.516667   \n",
       "                         libdnn-cuda         345.891333   409.103000   \n",
       "                         nvidia-cuda         248.506333   297.807333   \n",
       "                         nvidia-cudnn        179.370000   210.912333   \n",
       "                         nvidia-fp16-cuda    220.029667   262.912667   \n",
       "                         nvidia-fp16-cudnn   129.971667   153.531000   \n",
       "                         tensorrt-fp16        52.225728    64.267563   \n",
       "                         tensorrt-fp32       108.686927   132.858896   \n",
       "deepscale-squeezenet-1.0 cpu                1256.830000  1505.186667   \n",
       "                         libdnn-cuda         205.778333   234.404333   \n",
       "                         nvidia-cuda         165.680667   195.923333   \n",
       "                         nvidia-cudnn        133.661333   157.418333   \n",
       "                         nvidia-fp16-cuda    128.588667   155.392000   \n",
       "                         nvidia-fp16-cudnn    97.974900   116.185000   \n",
       "                         tensorrt-fp16        37.294033    44.685802   \n",
       "                         tensorrt-fp32        75.043120    89.768708   \n",
       "deepscale-squeezenet-1.1 cpu                 671.464667   806.602000   \n",
       "                         libdnn-cuda         110.560000   127.680333   \n",
       "                         nvidia-cuda         102.679333   121.048000   \n",
       "                         nvidia-cudnn         79.000700    92.304400   \n",
       "                         nvidia-fp16-cuda     83.707433    99.311700   \n",
       "                         nvidia-fp16-cudnn    58.095867    68.334200   \n",
       "                         tensorrt-fp16        19.024727    23.033725   \n",
       "                         tensorrt-fp32        36.357473    43.001714   \n",
       "\n",
       "batch size                                           14           16  \n",
       "model                    lib                                          \n",
       "bvlc-alexnet             cpu                1762.230000  1989.236667  \n",
       "                         libdnn-cuda         208.698667   233.940000  \n",
       "                         nvidia-cuda         145.451667   161.274333  \n",
       "                         nvidia-cudnn        109.021333   118.613667  \n",
       "                         nvidia-fp16-cuda    129.009333   137.854333  \n",
       "                         nvidia-fp16-cudnn    83.799200    81.037733  \n",
       "                         tensorrt-fp16        43.446100    47.552239  \n",
       "                         tensorrt-fp32        79.275100   103.944736  \n",
       "bvlc-googlenet           cpu                5032.933333  5733.163333  \n",
       "                         libdnn-cuda         467.763000   522.838000  \n",
       "                         nvidia-cuda         343.259333   384.953667  \n",
       "                         nvidia-cudnn        243.620333   273.755667  \n",
       "                         nvidia-fp16-cuda    306.044000   349.126000  \n",
       "                         nvidia-fp16-cudnn   182.630333   178.093667  \n",
       "                         tensorrt-fp16        74.011296    83.670448  \n",
       "                         tensorrt-fp32       153.812728   176.560558  \n",
       "deepscale-squeezenet-1.0 cpu                1757.083333  2017.030000  \n",
       "                         libdnn-cuda         272.915000   312.190333  \n",
       "                         nvidia-cuda         225.936667   256.927333  \n",
       "                         nvidia-cudnn        184.875333   208.046000  \n",
       "                         nvidia-fp16-cuda    180.241667   205.287667  \n",
       "                         nvidia-fp16-cudnn   136.637000   143.514333  \n",
       "                         tensorrt-fp16        52.026903    58.766408  \n",
       "                         tensorrt-fp32       104.224034   119.235589  \n",
       "deepscale-squeezenet-1.1 cpu                 939.974333  1079.046667  \n",
       "                         libdnn-cuda         149.362000   169.873000  \n",
       "                         nvidia-cuda         138.950333   157.645667  \n",
       "                         nvidia-cudnn        108.753667   122.582333  \n",
       "                         nvidia-fp16-cuda    115.543000   134.225667  \n",
       "                         nvidia-fp16-cudnn    81.064000    80.197600  \n",
       "                         tensorrt-fp16        26.300478    30.174599  \n",
       "                         tensorrt-fp32        49.773519    57.563813  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.options.display.max_columns = len(df_mean_time_per_batch.columns)\n",
    "pd.options.display.max_rows = len(df_mean_time_per_batch.index)\n",
    "df_mean_time_per_batch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "batch_sizes = df_mean_time_per_batch.columns.tolist()\n",
    "# batch_sizes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"df_mean_time_per_image\"></a>\n",
    "### Mean execution time per image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "df_mean_time_per_image = df_mean_time_per_batch / batch_sizes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>batch size</th>\n",
       "      <th>2</th>\n",
       "      <th>4</th>\n",
       "      <th>6</th>\n",
       "      <th>8</th>\n",
       "      <th>10</th>\n",
       "      <th>12</th>\n",
       "      <th>14</th>\n",
       "      <th>16</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model</th>\n",
       "      <th>lib</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"8\" valign=\"top\">bvlc-alexnet</th>\n",
       "      <th>cpu</th>\n",
       "      <td>216.736333</td>\n",
       "      <td>164.290750</td>\n",
       "      <td>154.334611</td>\n",
       "      <td>131.713333</td>\n",
       "      <td>128.938667</td>\n",
       "      <td>127.143333</td>\n",
       "      <td>125.873571</td>\n",
       "      <td>124.327292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>libdnn-cuda</th>\n",
       "      <td>22.291900</td>\n",
       "      <td>16.400125</td>\n",
       "      <td>15.468867</td>\n",
       "      <td>14.887750</td>\n",
       "      <td>14.804633</td>\n",
       "      <td>14.862278</td>\n",
       "      <td>14.907048</td>\n",
       "      <td>14.621250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nvidia-cuda</th>\n",
       "      <td>16.674667</td>\n",
       "      <td>11.371175</td>\n",
       "      <td>10.479739</td>\n",
       "      <td>10.236971</td>\n",
       "      <td>10.311767</td>\n",
       "      <td>10.254778</td>\n",
       "      <td>10.389405</td>\n",
       "      <td>10.079646</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nvidia-cudnn</th>\n",
       "      <td>15.317883</td>\n",
       "      <td>9.003133</td>\n",
       "      <td>8.036594</td>\n",
       "      <td>7.725829</td>\n",
       "      <td>7.727737</td>\n",
       "      <td>7.554350</td>\n",
       "      <td>7.787238</td>\n",
       "      <td>7.413354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nvidia-fp16-cuda</th>\n",
       "      <td>13.418633</td>\n",
       "      <td>8.934792</td>\n",
       "      <td>8.829567</td>\n",
       "      <td>8.721925</td>\n",
       "      <td>8.769610</td>\n",
       "      <td>8.583306</td>\n",
       "      <td>9.214952</td>\n",
       "      <td>8.615896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nvidia-fp16-cudnn</th>\n",
       "      <td>9.356517</td>\n",
       "      <td>5.873475</td>\n",
       "      <td>5.424506</td>\n",
       "      <td>5.226779</td>\n",
       "      <td>5.592167</td>\n",
       "      <td>5.252139</td>\n",
       "      <td>5.985657</td>\n",
       "      <td>5.064858</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tensorrt-fp16</th>\n",
       "      <td>6.349953</td>\n",
       "      <td>4.002801</td>\n",
       "      <td>4.452197</td>\n",
       "      <td>3.871781</td>\n",
       "      <td>3.469744</td>\n",
       "      <td>3.297523</td>\n",
       "      <td>3.103293</td>\n",
       "      <td>2.972015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tensorrt-fp32</th>\n",
       "      <td>11.722196</td>\n",
       "      <td>7.076571</td>\n",
       "      <td>6.690991</td>\n",
       "      <td>6.516994</td>\n",
       "      <td>6.338041</td>\n",
       "      <td>6.004536</td>\n",
       "      <td>5.662507</td>\n",
       "      <td>6.496546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"8\" valign=\"top\">bvlc-googlenet</th>\n",
       "      <th>cpu</th>\n",
       "      <td>361.652833</td>\n",
       "      <td>360.120833</td>\n",
       "      <td>360.910000</td>\n",
       "      <td>360.418750</td>\n",
       "      <td>359.050667</td>\n",
       "      <td>360.543056</td>\n",
       "      <td>359.495238</td>\n",
       "      <td>358.322708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>libdnn-cuda</th>\n",
       "      <td>50.295633</td>\n",
       "      <td>38.017417</td>\n",
       "      <td>36.031111</td>\n",
       "      <td>34.485958</td>\n",
       "      <td>34.589133</td>\n",
       "      <td>34.091917</td>\n",
       "      <td>33.411643</td>\n",
       "      <td>32.677375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nvidia-cuda</th>\n",
       "      <td>34.575550</td>\n",
       "      <td>28.528000</td>\n",
       "      <td>26.464278</td>\n",
       "      <td>25.437875</td>\n",
       "      <td>24.850633</td>\n",
       "      <td>24.817278</td>\n",
       "      <td>24.518524</td>\n",
       "      <td>24.059604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nvidia-cudnn</th>\n",
       "      <td>28.152317</td>\n",
       "      <td>21.513958</td>\n",
       "      <td>19.899111</td>\n",
       "      <td>18.722083</td>\n",
       "      <td>17.937000</td>\n",
       "      <td>17.576028</td>\n",
       "      <td>17.401452</td>\n",
       "      <td>17.109729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nvidia-fp16-cuda</th>\n",
       "      <td>25.017467</td>\n",
       "      <td>23.124983</td>\n",
       "      <td>22.341833</td>\n",
       "      <td>22.070333</td>\n",
       "      <td>22.002967</td>\n",
       "      <td>21.909389</td>\n",
       "      <td>21.860286</td>\n",
       "      <td>21.820375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nvidia-fp16-cudnn</th>\n",
       "      <td>14.817950</td>\n",
       "      <td>13.669225</td>\n",
       "      <td>13.414306</td>\n",
       "      <td>11.318787</td>\n",
       "      <td>12.997167</td>\n",
       "      <td>12.794250</td>\n",
       "      <td>13.045024</td>\n",
       "      <td>11.130854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tensorrt-fp16</th>\n",
       "      <td>7.274488</td>\n",
       "      <td>6.052523</td>\n",
       "      <td>5.681921</td>\n",
       "      <td>5.486352</td>\n",
       "      <td>5.222573</td>\n",
       "      <td>5.355630</td>\n",
       "      <td>5.286521</td>\n",
       "      <td>5.229403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tensorrt-fp32</th>\n",
       "      <td>12.362265</td>\n",
       "      <td>11.531551</td>\n",
       "      <td>11.380863</td>\n",
       "      <td>11.142536</td>\n",
       "      <td>10.868693</td>\n",
       "      <td>11.071575</td>\n",
       "      <td>10.986623</td>\n",
       "      <td>11.035035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"8\" valign=\"top\">deepscale-squeezenet-1.0</th>\n",
       "      <th>cpu</th>\n",
       "      <td>126.964167</td>\n",
       "      <td>125.926250</td>\n",
       "      <td>125.813000</td>\n",
       "      <td>125.730833</td>\n",
       "      <td>125.683000</td>\n",
       "      <td>125.432222</td>\n",
       "      <td>125.505952</td>\n",
       "      <td>126.064375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>libdnn-cuda</th>\n",
       "      <td>25.805850</td>\n",
       "      <td>22.033358</td>\n",
       "      <td>21.274722</td>\n",
       "      <td>20.545500</td>\n",
       "      <td>20.577833</td>\n",
       "      <td>19.533694</td>\n",
       "      <td>19.493929</td>\n",
       "      <td>19.511896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nvidia-cuda</th>\n",
       "      <td>20.530683</td>\n",
       "      <td>18.522425</td>\n",
       "      <td>17.493389</td>\n",
       "      <td>16.846583</td>\n",
       "      <td>16.568067</td>\n",
       "      <td>16.326944</td>\n",
       "      <td>16.138333</td>\n",
       "      <td>16.057958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nvidia-cudnn</th>\n",
       "      <td>17.704617</td>\n",
       "      <td>14.959783</td>\n",
       "      <td>13.916756</td>\n",
       "      <td>13.791000</td>\n",
       "      <td>13.366133</td>\n",
       "      <td>13.118194</td>\n",
       "      <td>13.205381</td>\n",
       "      <td>13.002875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nvidia-fp16-cuda</th>\n",
       "      <td>13.832333</td>\n",
       "      <td>13.447242</td>\n",
       "      <td>13.055806</td>\n",
       "      <td>12.962458</td>\n",
       "      <td>12.858867</td>\n",
       "      <td>12.949333</td>\n",
       "      <td>12.874405</td>\n",
       "      <td>12.830479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nvidia-fp16-cudnn</th>\n",
       "      <td>10.121033</td>\n",
       "      <td>9.786692</td>\n",
       "      <td>9.839956</td>\n",
       "      <td>9.166954</td>\n",
       "      <td>9.797490</td>\n",
       "      <td>9.682083</td>\n",
       "      <td>9.759786</td>\n",
       "      <td>8.969646</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tensorrt-fp16</th>\n",
       "      <td>4.366050</td>\n",
       "      <td>3.980165</td>\n",
       "      <td>3.863554</td>\n",
       "      <td>3.773042</td>\n",
       "      <td>3.729403</td>\n",
       "      <td>3.723817</td>\n",
       "      <td>3.716207</td>\n",
       "      <td>3.672901</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tensorrt-fp32</th>\n",
       "      <td>7.870487</td>\n",
       "      <td>7.617014</td>\n",
       "      <td>7.510142</td>\n",
       "      <td>7.492141</td>\n",
       "      <td>7.504312</td>\n",
       "      <td>7.480726</td>\n",
       "      <td>7.444574</td>\n",
       "      <td>7.452224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"8\" valign=\"top\">deepscale-squeezenet-1.1</th>\n",
       "      <th>cpu</th>\n",
       "      <td>68.336000</td>\n",
       "      <td>67.823000</td>\n",
       "      <td>67.702778</td>\n",
       "      <td>67.321333</td>\n",
       "      <td>67.146467</td>\n",
       "      <td>67.216833</td>\n",
       "      <td>67.141024</td>\n",
       "      <td>67.440417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>libdnn-cuda</th>\n",
       "      <td>17.394017</td>\n",
       "      <td>13.180800</td>\n",
       "      <td>11.913017</td>\n",
       "      <td>11.806312</td>\n",
       "      <td>11.056000</td>\n",
       "      <td>10.640028</td>\n",
       "      <td>10.668714</td>\n",
       "      <td>10.617063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nvidia-cuda</th>\n",
       "      <td>14.566033</td>\n",
       "      <td>13.270350</td>\n",
       "      <td>10.918539</td>\n",
       "      <td>10.662321</td>\n",
       "      <td>10.267933</td>\n",
       "      <td>10.087333</td>\n",
       "      <td>9.925024</td>\n",
       "      <td>9.852854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nvidia-cudnn</th>\n",
       "      <td>12.683333</td>\n",
       "      <td>9.584200</td>\n",
       "      <td>8.580767</td>\n",
       "      <td>8.118454</td>\n",
       "      <td>7.900070</td>\n",
       "      <td>7.692033</td>\n",
       "      <td>7.768119</td>\n",
       "      <td>7.661396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nvidia-fp16-cuda</th>\n",
       "      <td>9.609533</td>\n",
       "      <td>8.732792</td>\n",
       "      <td>8.691217</td>\n",
       "      <td>8.366896</td>\n",
       "      <td>8.370743</td>\n",
       "      <td>8.275975</td>\n",
       "      <td>8.253071</td>\n",
       "      <td>8.389104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nvidia-fp16-cudnn</th>\n",
       "      <td>6.542367</td>\n",
       "      <td>5.871300</td>\n",
       "      <td>5.926306</td>\n",
       "      <td>5.038858</td>\n",
       "      <td>5.809587</td>\n",
       "      <td>5.694517</td>\n",
       "      <td>5.790286</td>\n",
       "      <td>5.012350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tensorrt-fp16</th>\n",
       "      <td>2.700613</td>\n",
       "      <td>2.251494</td>\n",
       "      <td>2.051253</td>\n",
       "      <td>1.985378</td>\n",
       "      <td>1.902473</td>\n",
       "      <td>1.919477</td>\n",
       "      <td>1.878606</td>\n",
       "      <td>1.885912</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tensorrt-fp32</th>\n",
       "      <td>4.107963</td>\n",
       "      <td>3.828774</td>\n",
       "      <td>3.694878</td>\n",
       "      <td>3.657880</td>\n",
       "      <td>3.635747</td>\n",
       "      <td>3.583476</td>\n",
       "      <td>3.555251</td>\n",
       "      <td>3.597738</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "batch size                                          2           4   \\\n",
       "model                    lib                                         \n",
       "bvlc-alexnet             cpu                216.736333  164.290750   \n",
       "                         libdnn-cuda         22.291900   16.400125   \n",
       "                         nvidia-cuda         16.674667   11.371175   \n",
       "                         nvidia-cudnn        15.317883    9.003133   \n",
       "                         nvidia-fp16-cuda    13.418633    8.934792   \n",
       "                         nvidia-fp16-cudnn    9.356517    5.873475   \n",
       "                         tensorrt-fp16        6.349953    4.002801   \n",
       "                         tensorrt-fp32       11.722196    7.076571   \n",
       "bvlc-googlenet           cpu                361.652833  360.120833   \n",
       "                         libdnn-cuda         50.295633   38.017417   \n",
       "                         nvidia-cuda         34.575550   28.528000   \n",
       "                         nvidia-cudnn        28.152317   21.513958   \n",
       "                         nvidia-fp16-cuda    25.017467   23.124983   \n",
       "                         nvidia-fp16-cudnn   14.817950   13.669225   \n",
       "                         tensorrt-fp16        7.274488    6.052523   \n",
       "                         tensorrt-fp32       12.362265   11.531551   \n",
       "deepscale-squeezenet-1.0 cpu                126.964167  125.926250   \n",
       "                         libdnn-cuda         25.805850   22.033358   \n",
       "                         nvidia-cuda         20.530683   18.522425   \n",
       "                         nvidia-cudnn        17.704617   14.959783   \n",
       "                         nvidia-fp16-cuda    13.832333   13.447242   \n",
       "                         nvidia-fp16-cudnn   10.121033    9.786692   \n",
       "                         tensorrt-fp16        4.366050    3.980165   \n",
       "                         tensorrt-fp32        7.870487    7.617014   \n",
       "deepscale-squeezenet-1.1 cpu                 68.336000   67.823000   \n",
       "                         libdnn-cuda         17.394017   13.180800   \n",
       "                         nvidia-cuda         14.566033   13.270350   \n",
       "                         nvidia-cudnn        12.683333    9.584200   \n",
       "                         nvidia-fp16-cuda     9.609533    8.732792   \n",
       "                         nvidia-fp16-cudnn    6.542367    5.871300   \n",
       "                         tensorrt-fp16        2.700613    2.251494   \n",
       "                         tensorrt-fp32        4.107963    3.828774   \n",
       "\n",
       "batch size                                          6           8   \\\n",
       "model                    lib                                         \n",
       "bvlc-alexnet             cpu                154.334611  131.713333   \n",
       "                         libdnn-cuda         15.468867   14.887750   \n",
       "                         nvidia-cuda         10.479739   10.236971   \n",
       "                         nvidia-cudnn         8.036594    7.725829   \n",
       "                         nvidia-fp16-cuda     8.829567    8.721925   \n",
       "                         nvidia-fp16-cudnn    5.424506    5.226779   \n",
       "                         tensorrt-fp16        4.452197    3.871781   \n",
       "                         tensorrt-fp32        6.690991    6.516994   \n",
       "bvlc-googlenet           cpu                360.910000  360.418750   \n",
       "                         libdnn-cuda         36.031111   34.485958   \n",
       "                         nvidia-cuda         26.464278   25.437875   \n",
       "                         nvidia-cudnn        19.899111   18.722083   \n",
       "                         nvidia-fp16-cuda    22.341833   22.070333   \n",
       "                         nvidia-fp16-cudnn   13.414306   11.318787   \n",
       "                         tensorrt-fp16        5.681921    5.486352   \n",
       "                         tensorrt-fp32       11.380863   11.142536   \n",
       "deepscale-squeezenet-1.0 cpu                125.813000  125.730833   \n",
       "                         libdnn-cuda         21.274722   20.545500   \n",
       "                         nvidia-cuda         17.493389   16.846583   \n",
       "                         nvidia-cudnn        13.916756   13.791000   \n",
       "                         nvidia-fp16-cuda    13.055806   12.962458   \n",
       "                         nvidia-fp16-cudnn    9.839956    9.166954   \n",
       "                         tensorrt-fp16        3.863554    3.773042   \n",
       "                         tensorrt-fp32        7.510142    7.492141   \n",
       "deepscale-squeezenet-1.1 cpu                 67.702778   67.321333   \n",
       "                         libdnn-cuda         11.913017   11.806312   \n",
       "                         nvidia-cuda         10.918539   10.662321   \n",
       "                         nvidia-cudnn         8.580767    8.118454   \n",
       "                         nvidia-fp16-cuda     8.691217    8.366896   \n",
       "                         nvidia-fp16-cudnn    5.926306    5.038858   \n",
       "                         tensorrt-fp16        2.051253    1.985378   \n",
       "                         tensorrt-fp32        3.694878    3.657880   \n",
       "\n",
       "batch size                                          10          12  \\\n",
       "model                    lib                                         \n",
       "bvlc-alexnet             cpu                128.938667  127.143333   \n",
       "                         libdnn-cuda         14.804633   14.862278   \n",
       "                         nvidia-cuda         10.311767   10.254778   \n",
       "                         nvidia-cudnn         7.727737    7.554350   \n",
       "                         nvidia-fp16-cuda     8.769610    8.583306   \n",
       "                         nvidia-fp16-cudnn    5.592167    5.252139   \n",
       "                         tensorrt-fp16        3.469744    3.297523   \n",
       "                         tensorrt-fp32        6.338041    6.004536   \n",
       "bvlc-googlenet           cpu                359.050667  360.543056   \n",
       "                         libdnn-cuda         34.589133   34.091917   \n",
       "                         nvidia-cuda         24.850633   24.817278   \n",
       "                         nvidia-cudnn        17.937000   17.576028   \n",
       "                         nvidia-fp16-cuda    22.002967   21.909389   \n",
       "                         nvidia-fp16-cudnn   12.997167   12.794250   \n",
       "                         tensorrt-fp16        5.222573    5.355630   \n",
       "                         tensorrt-fp32       10.868693   11.071575   \n",
       "deepscale-squeezenet-1.0 cpu                125.683000  125.432222   \n",
       "                         libdnn-cuda         20.577833   19.533694   \n",
       "                         nvidia-cuda         16.568067   16.326944   \n",
       "                         nvidia-cudnn        13.366133   13.118194   \n",
       "                         nvidia-fp16-cuda    12.858867   12.949333   \n",
       "                         nvidia-fp16-cudnn    9.797490    9.682083   \n",
       "                         tensorrt-fp16        3.729403    3.723817   \n",
       "                         tensorrt-fp32        7.504312    7.480726   \n",
       "deepscale-squeezenet-1.1 cpu                 67.146467   67.216833   \n",
       "                         libdnn-cuda         11.056000   10.640028   \n",
       "                         nvidia-cuda         10.267933   10.087333   \n",
       "                         nvidia-cudnn         7.900070    7.692033   \n",
       "                         nvidia-fp16-cuda     8.370743    8.275975   \n",
       "                         nvidia-fp16-cudnn    5.809587    5.694517   \n",
       "                         tensorrt-fp16        1.902473    1.919477   \n",
       "                         tensorrt-fp32        3.635747    3.583476   \n",
       "\n",
       "batch size                                          14          16  \n",
       "model                    lib                                        \n",
       "bvlc-alexnet             cpu                125.873571  124.327292  \n",
       "                         libdnn-cuda         14.907048   14.621250  \n",
       "                         nvidia-cuda         10.389405   10.079646  \n",
       "                         nvidia-cudnn         7.787238    7.413354  \n",
       "                         nvidia-fp16-cuda     9.214952    8.615896  \n",
       "                         nvidia-fp16-cudnn    5.985657    5.064858  \n",
       "                         tensorrt-fp16        3.103293    2.972015  \n",
       "                         tensorrt-fp32        5.662507    6.496546  \n",
       "bvlc-googlenet           cpu                359.495238  358.322708  \n",
       "                         libdnn-cuda         33.411643   32.677375  \n",
       "                         nvidia-cuda         24.518524   24.059604  \n",
       "                         nvidia-cudnn        17.401452   17.109729  \n",
       "                         nvidia-fp16-cuda    21.860286   21.820375  \n",
       "                         nvidia-fp16-cudnn   13.045024   11.130854  \n",
       "                         tensorrt-fp16        5.286521    5.229403  \n",
       "                         tensorrt-fp32       10.986623   11.035035  \n",
       "deepscale-squeezenet-1.0 cpu                125.505952  126.064375  \n",
       "                         libdnn-cuda         19.493929   19.511896  \n",
       "                         nvidia-cuda         16.138333   16.057958  \n",
       "                         nvidia-cudnn        13.205381   13.002875  \n",
       "                         nvidia-fp16-cuda    12.874405   12.830479  \n",
       "                         nvidia-fp16-cudnn    9.759786    8.969646  \n",
       "                         tensorrt-fp16        3.716207    3.672901  \n",
       "                         tensorrt-fp32        7.444574    7.452224  \n",
       "deepscale-squeezenet-1.1 cpu                 67.141024   67.440417  \n",
       "                         libdnn-cuda         10.668714   10.617063  \n",
       "                         nvidia-cuda          9.925024    9.852854  \n",
       "                         nvidia-cudnn         7.768119    7.661396  \n",
       "                         nvidia-fp16-cuda     8.253071    8.389104  \n",
       "                         nvidia-fp16-cudnn    5.790286    5.012350  \n",
       "                         tensorrt-fp16        1.878606    1.885912  \n",
       "                         tensorrt-fp32        3.555251    3.597738  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.options.display.max_columns = len(df_mean_time_per_image.columns)\n",
    "pd.options.display.max_rows = len(df_mean_time_per_image.index)\n",
    "df_mean_time_per_image"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"df_best_mean_time_per_image\"></a>\n",
    "### Best mean execution time per image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "model                     lib              \n",
       "bvlc-alexnet              cpu                  124.327292\n",
       "                          libdnn-cuda           14.621250\n",
       "                          nvidia-cuda           10.079646\n",
       "                          nvidia-cudnn           7.413354\n",
       "                          nvidia-fp16-cuda       8.583306\n",
       "                          nvidia-fp16-cudnn      5.064858\n",
       "                          tensorrt-fp16          2.972015\n",
       "                          tensorrt-fp32          5.662507\n",
       "bvlc-googlenet            cpu                  358.322708\n",
       "                          libdnn-cuda           32.677375\n",
       "                          nvidia-cuda           24.059604\n",
       "                          nvidia-cudnn          17.109729\n",
       "                          nvidia-fp16-cuda      21.820375\n",
       "                          nvidia-fp16-cudnn     11.130854\n",
       "                          tensorrt-fp16          5.222573\n",
       "                          tensorrt-fp32         10.868693\n",
       "deepscale-squeezenet-1.0  cpu                  125.432222\n",
       "                          libdnn-cuda           19.493929\n",
       "                          nvidia-cuda           16.057958\n",
       "                          nvidia-cudnn          13.002875\n",
       "                          nvidia-fp16-cuda      12.830479\n",
       "                          nvidia-fp16-cudnn      8.969646\n",
       "                          tensorrt-fp16          3.672901\n",
       "                          tensorrt-fp32          7.444574\n",
       "deepscale-squeezenet-1.1  cpu                   67.141024\n",
       "                          libdnn-cuda           10.617063\n",
       "                          nvidia-cuda            9.852854\n",
       "                          nvidia-cudnn           7.661396\n",
       "                          nvidia-fp16-cuda       8.253071\n",
       "                          nvidia-fp16-cudnn      5.012350\n",
       "                          tensorrt-fp16          1.878606\n",
       "                          tensorrt-fp32          3.555251\n",
       "dtype: float64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_mean_time_per_image.min(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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TTjjB3XzzzS47O9ufZ+HChc7M3E033VTk3vbt27upU6c655x74YUXnJm5Bx54\noEie+vXru48//jhgzH/729/c3LlzQ3q/NWvWuDPOOMPVrFnTxcfHu06dOrk1a9b40x944AHXrFkz\nFxMT4xo3blwilooW6DslIiIiIiKHnu/38xJtwaN77ety4vl5My6NsB2enzeHNf4+ffqwbds2cnJy\nmD17Nj///DOnn346mzf/+dxq1aoxffp0MjIyApYTHx/PhAkT+OOPP0J+tsfj4aSTTgopb3JyMq+/\n/jo5OTls2bKFrl270qdPnyJ5pk+fTm5uLu+88w5PPPEEs2bNCjkWERERERERNYKPQOnp6fTv399/\n7pzj+eefJzk5meTkZB566KFS74uMjKRp06bMnDmTxMTEIvni4uIYMGAAo0ePDvjcpk2b0rZt24Dl\nF7Z3715iYmLIz8+nefPmNGnSBPAO6R43bhwnn3wyCQkJXHvttezduxeA2NhYUlJSAMjLyyMiIoL1\n69f7yxw6dCinnnoqERERHH/88Vx22WV8+umnZcYiIhVD87lEAlP9EAlOdUTCSY3gI5SZFTlfuHAh\n69evZ/78+YwfP54PP/ww4L0RERFcdtllLFq0qEh5d999N6+//nqRZdWLP3PMmDFMnDiR3NzgC4JH\nRUWxY8cOnHOsXLmySJkvv/wyCxYsYP369Xz33XeMHTu2yL01a9akatWqDBkyhLvvvjvgMxYtWsTJ\nJ58cNA4RERERkaNNw4Z1MbOwHA0b1i07gCOcGsFHidGjR1OlShWaNWvGwIEDeeWVV4LmT0pKIicn\np8i12rVrc/311zNy5MiA9zVv3pxOnTod0Cba3uH4fxo8eDBJSUnExcVx9913l4h169atbNu2jSee\neIIWLVqUWuaoUaNwzjFw4MCQ4xCRQ0t7PIoEpvohEpzqSHAez2acIyyHxxPeqZqHg0oVHYD8dWZG\n/fr1/eepqamsWrUq6D1ZWVnEx8eXuD5s2DDS0tL49ttvA957zz330Lp1a2677bYi12NiYvw91GvW\nrCkSU2HFY920aVOJPNHR0fzrX/8iMTGRdevWUatWLX/aE088wYsvvsjixYs57rjjgr6niIiIiIhI\nYeoJPkpkZmb6/52RkUFSUlLAvM455s6dS4cOHUqkxcfHc+uttzJixIgSQ64LnHDCCfTo0YN77723\nSJ4dO3awfft2tm/fHrABXDxWj8cTMNa8vDx27txJVlaW/9rkyZOZMGECH374YdCNwEWk4mk+l0hg\nqh8iwakw8gBHAAAgAElEQVSOSDiV2Qg2s45mdqWZtQyQnmxmgcfPStg55xgzZgy7du1i9erVTJky\npciqygXDkfPy8li7di19+vRh8+bNJXpyC9x2220sWbKEtWvXBnzmyJEjmTJlSplzg0vz5JNPkpWV\nRU5ODvfdd58/1vfff59vvvmG/Px8tm/fzu233058fDxNmzYF4KWXXuLuu+9mwYIFpKamHvBzRURE\nREREAjaCzay6mS0BPgCmA1+Y2btmVrzbrj4wKowxVrjUunWwHwnbkVq3TkhxBOqZNTPOOecc0tLS\n6NSpE3feeSfnn3++P33WrFnExsYSFxdHt27dSExMZPny5dStW/qk95iYGO68884Sc4YLa9iwIf37\n9y9zu6TSYu7Xrx+dO3cmLS2NJk2a+Be/ys3NpW/fvsTFxdGkSRP+97//8e677xIVFQXAiBEjyMnJ\noVWrVsTExBAbG8uNN94Y9PkiUnE0n0skMNUPkeBURyScrPiiRf4Es/uAG4BbgC+AjkA6sBfo4pxb\n48vXGljinIs8FAGXFzNzpb27mZVYyEnKT6NGjXj++ec577zzKjqUcqfvjoiIiIgcCt7fO8NVdsmF\nbY9Uvt/PS/TKBRsO3QMY5Zyb7pxb55z7f0BLYDPwiZm1ClOsIiJyFNB8LpHAVD9EglMdkXAK1ghO\nAb4ufME5lwWcA6wE3jezjuELTY5GgYZ0i4iIiIiIHArBhkNvAP7tnCux4ayZVQFexztE+iHgbg2H\nlmOdvjsiIiIicihoOHRoAg2HDtYIfg3Y75zrEyC9EvAycDng1AiWY52+OyIiIiJyKKgRHJqDmRP8\nCpBqZgmlJTrn9gO9gaeBjHKJUkREjhqazyUSmOqHSHCqIxJOlQIlOOdexzvkOSBfV+oN5R2UiIiI\niIiISDgEHA59tNNwaClv+u6IiIiIyKGg4dChOZjh0CIiIiIiIiJHFTWC5ZD65Zdf6NChAzVq1OCO\nO+6o6HBEJIw0n0skMNUPkeBURySc1AgOQcPkuphZ2I6GyXXDGn/Hjh2Jjo4mNjaWmjVr0rFjR1at\nWuVPT09Pp3///mWWER8fz759+4pcz8rK4vLLLycxMZGaNWvSvHlzpk2bFrCcZ555htq1a7Nt2zYe\neOCBMmPv378/SUlJ1KhRgxNPPJHnn3/en/b555/TuXNnEhISqFOnDr179+bnn38us0wRERERETl2\nVUgj2MzOMbP8Uo6cYvnizOw5M/vVzH43swVm1qyU8iqb2QNmtsnMdprZEjM7u7zi9WzajLuIsB2e\nTZvLK9RSmRmTJk1i+/bt5OTkcM4555Ro9JqVGCr/5/t7PCxevJiIiAjmzJlTJK1///6kpqaSmZnJ\nb7/9xvTp06lTp07Qsk466aSQY//Pf/6Dx+Nh27ZtzJkzh+HDh/P1118DsHXrVv71r3/h8XjweDxU\nr16dgQMHhly2iIRXx44dKzoEkcOW6odIcKojEk4BV4c+BBwwGPiy0LX9xfL8F0gBbgJygf8AH5lZ\nC+fcpkL5JgMXAUOB/wE3A/PNrI1z7tswxV8hNm7cyJAhQ1i0aBHOOfr27UtCQgI//vgj06dPB7wN\nzUaNGrF//34iIrx/5yiY3G5m9OnTh/Hjx4f8zGnTptG2bVtat27NCy+8QM+ePf1pX3zxBRMnTqRK\nlSoAtGjRghYtWpRazsCBA3nppZcwMyZOnMibb77JokWLWLVqFZGRkcybN4/jjz+eyZMn07x5cwCa\nNm3qv985h5mxfv16TjvtNC688MIi5d988836P0wREREREQkq5J5gM4s1sz5mdqeZjSx2jDjI569z\nzi0rdHxV6HmXAW2Bq5xzs5xz7wF/98V8Z6F8LYC+wK3OucnOuY+AXnj3Lr7nIOM6LOXn53PppZfS\nqFEjPB4PWVlZ9OnTByjZkxuoZ3fv3r28+OKLtGnTJuTnTps2jauuuop+/foxf/58fv31V39a27Zt\nufHGG5k5cyaZmZlBy5kyZQpXXnklw4YNY/v27Zx33nkAzJkzh969e7N161b69u1Lt27dyMvL8993\n0003Ua1aNZo2bUpSUhIXX3xxqeV//PHHnHzyySG/l4iEl+ZziQSm+iESnOqIhFNIjWAzOwvYALwM\njANGl3IcqMDjb726Apucc58UXHDObQfmApcVyvd3YC8wq1C+PGAG0MXMjjuI2A5Ly5YtIzs7mwkT\nJhAdHU1UVBTt2rUL6d5bbrmF+Ph4YmNjmTRpEqNGjQrpvsWLF5ORkUGvXr1o2bIlaWlpvPzyy/70\nV199lQ4dOjB27FgaN25My5Yt+fLLL4OUWNLpp59O9+7diYyM5Pbbb2f37t189tln/vQnn3yS33//\nncWLF9OjRw8qV65cooxvv/2WMWPG8OCDDx7Qs0VERERE5NgSak/wRLyN4FZAFedcRLEj8iCf/5KZ\n7TezLWb2kpk1KJR2MrCqlHtWAylmVtV3fhLwP+fc7lLyRQFpBxnbYSczM5PU1FT/EOcD8dhjj5GT\nk8Pu3buZO3cuPXv2LLI4ViDTpk2jc+fO1KxZE4C+ffsydepUf3qNGjW47777WLlyJZs3b6ZFixZ0\n794dgBtuuIGYmBhiY2MZN25cwGc0aPDn/+xmRv369dm0aVORPGZGu3btyMzM5KmnniqS9uOPP3Lx\nxRfz+OOPh/xHAREJP01PEAlM9UMkONURCadQ5wQ3BXo555aX03O3AQ8CHwPbgdOAu4ElZnaac24L\nEI93fm9xBYtn1QR2+vJtDZIvvpxirnANGjQgIyOD/Pz8Ig3hatWqsXPnTv95dnZ20HLat29PWloa\n7733Hs2alVhnzG/37t3MmjWL/Px86tWrB3iHU+fm5rJy5UpOOeWUIvnj4+MZOnQo06ZNY+vWrTz1\n1FMlGqylKTyM2jnHxo0bSUpKKjXv/v37Wb9+vf/c4/HQqVMnRo0aRb9+/cp8loiIiIiIHNtC7VLM\nAEqOQT1IzrlvnHN3Oufeds4tcs49BlwI1AVuKa/nHG3OPPNM6tWrx1133cXOnTvZs2cPS5Ys4dRT\nT+WTTz4hMzOTbdu2Be11BVi6dClr164t0gDOy8tjz549/mPv3r3Mnj2bSpUqsXbtWlasWMGKFStY\nu3Yt7du392+DdNddd7F69Wry8vLYsWMHkyZNIi0tzd9zHIrly5fz5ptvkpeXxyOPPEKVKlVo06YN\nv/76KzNnzuSPP/4gPz+f+fPnM2PGDC644ALAuz3T+eefz+DBg/nnP/95EJ+oiIST5nOJBKb6IRKc\n6oiEU6g9wenAXWb2gW9ebrlzzn1tZt/jHXIN3t7d0lpS8YXSC/6bEiRfTilpAAwYMICGDRsCEBcX\nx6mnnlpqvtSkOtg74dvGKDUp8JZChUVERDB37lwGDx5MSkoKERER9OvXj4kTJ9KrVy+aN29OYmIi\nw4YNY+7cuUXuvfnmm7n11lsBqFu3Lvfeey+dO3f2p8+YMYMZM2b4z5OTk2nWrBmDBg0iOTm5RFlD\nhgxh/Pjx7Ny5k+7du/Pzzz8THR1N69atS2yjVFhpC3ZddtllzJw5k6uvvpomTZowe/ZsIiMjMTOe\neuopbrjhBvLz80lNTeXRRx/lkksuAeD555/nf//7H6NHj2b06NH+1aO3bw/LV/SAFPwfd8FQHp3r\n/Fg8L3C4xKNznR9O5wUOl3h0rnOdH4nn+M4p1/MCFf9+B37+zTffkJubC8CGDRsIxAq2zgnGzKYD\nZwMxwFJKNiydc+6aMgsq+zmrAY9z7mIzex7o5JxLKZZnCtDROdfIdz4C71DquMLzgs1sNDAMiHXO\n7SvlWa60dzczQvlMpHykp6ezfv16f8/ykUzfHRERERE5FLy/d4arbI6a32l9v5+X6IWLCPH+9nj3\n9d2Od8Gqs0s5/mqAZwAnAJ/7Ls0Bks3s7EJ5YvGuGv1WoVvn4l0A64pC+SLxbpM0v7QGsIiIiIiI\niBybQhoOXdDrWl7M7EW8i159BeQCLYG7gEzgcV+2OcBnwItmdqcv3799aQ8Uiu0bM5sJTDSzKF+5\nNwIN8e4fLCIiFWDhwoX+IUoiUpTqh0hwqiMSTqHOCS5vq4A+wM1AVeBn4DVgtHMuB7zjq83sEryr\nSD8JVAGW4B0KnVWsvAHAvcAYIA5YAXRxzq0I/6vIXxHqfsUiIiIiIiLlIaQ5wQBmVg24FugAJADX\nOed+MLM+wDfOuXXhC7P8aU6wlDd9d0RERETkUNCc4NAEmhMcUk+wmTUAFgL1gXVAM7yLZAGcC1wA\n/KNcIhUREREREREJk1AXxnoI2AMcD5wOFG5Nf0w5LIwlIiJHl4XF91oQET/VD5HgVEcknEKdE9wJ\n7/Bnj2/l5cKygORS7hERERERERE5rIS6T/AfQE/n3Lu+RvA+4Azn3Fdm9ndgmnMuLsyxlivNCZby\npu+OiIiIiBwKmhMcmr+6T/C3QM8AaRcByw82MDm2/PLLL3To0IEaNWpwxx13VHQ4IiIiIiJyjAm1\nEfwAcK2ZPYt3dWiAk8wsHe+K0Q8EvPMo0LB+XcwsbEfD+nXDGn/Hjh2Jjo4mNjaWmjVr0rFjR1at\nWuVPT09Pp3///mWWER8fz759+4pcz8rK4vLLLycxMZGaNWvSvHlzpk2bFrCcZ555htq1a7Nt2zYe\neKDsr82TTz5Jq1atqFKlCoMGDSqRvmvXLm688Ub/87WfnMjhQ/O5RAJT/RAJTnVEwimkOcHOuTfM\n7EZgHFDQEpkG7ABuds69G6b4DguerM24weEr3x7fHL7C8Q4DmDRpEgMHDsQ5x+jRo+nfvz9ff/11\nkTyBeDweFi9eTFxcHHPmzKFnzz8HBfTv35/TTjuNzMxMoqKiWLlyJT///HPQsk466aSQY09OTmbE\niBHMnz+fXbt2lUj/5z//SX5+Pt999x01a9bkm2++CblsERERERE59oS8TzD49wpuByQCvwFLnHM7\nwhRbWB3InGAzC3MjOPRx9xs3bmTIkCEsWrQI5xx9+/YlISGBH3/8kenTpwPehmajRo3Yv38/ERER\nnHvuufTv39/fk7p27VpOO+00du/eDXh7gtevXx+wB3fMmDG89957tG7dmu+++465c+f602JiYvj0\n009p3rx5mbEPHDiQl156CTOjcuXKvPnmmyxatIhVq1YRGRnJvHnzOP7445k8eXKJ8kaMGEFWVhaT\nJ0/2X/vuu+9o3bo1GzdupHr16iF9fuGkOcEiIiIicihoTnBo/uqcYACcc3845xY45152zs0/UhvA\nR6r8/HwuvfRSGjVqhMfjISsriz59+gAle3ID9ezu3buXF198kTZt2oT83GnTpnHVVVfRr18/5s+f\nz6+//upPa9u2LTfeeCMzZ84kMzMzaDlTpkzhyiuvZNiwYWzfvp3zzjsPgDlz5tC7d2+2bt1K3759\n6datG3l5eWXGtWzZMlJTUxk5ciSJiYm0aNGCN954I+T3EhERERGRY0/IjWAzSzazh83sSzP7ycya\n+a7famatwxeiFFi2bBnZ2dlMmDCB6OhooqKiaNeuXUj33nLLLcTHxxMbG8ukSZMYNWpUSPctXryY\njIwMevXqRcuWLUlLS+Pll1/2p7/66qt06NCBsWPH0rhxY1q2bMmXX355QO91+umn0717dyIjI7n9\n9tvZvXs3n332WZn3bdy4kZUrV1KzZk2ys7N5/PHHueaaa/juu+8O6PkiEh6azyUSmOqHSHCqIxJO\nITWCzexkYCXQH9gEpAJRvuRUYEhYopMiMjMzSU1NJSLigDrwAXjsscfIyclh9+7dzJ07l549exZZ\nHCuQadOm0blzZ2rWrAlA3759mTp1qj+9Ro0a3HfffaxcuZLNmzfTokULunfvDsANN9xATEwMsbGx\njBs3LuAzGjRo4P+3mVG/fn02bdpUZmwFfwgYPnw4lSpVokOHDpx77rm89957Zd4rIiIiIiLHppAW\nxgIeAtYCXYDdwN5CaUuA8eUcl5SiQYMGZGRkkJ+fX6QhXK1aNXbu3Ok/z87ODlpO+/btSUtL4733\n3qNZs2YB8+3evZtZs2aRn59PvXr1AO9w6tzcXFauXMkpp5xSJH98fDxDhw5l2rRpbN26laeeeoqn\nnnqqzPcqPIzaOcfGjRtJSkoq876CecPOOf/w72ALfInIoaXV2kUCU/0QCU51RMIp1C7F9sA459zv\nQPFZ0puB8O7xIwCceeaZ1KtXj7vuuoudO3eyZ88elixZwqmnnsonn3xCZmYm27ZtC9rrCrB06VLW\nrl1bpAGcl5fHnj17/MfevXuZPXs2lSpVYu3ataxYsYIVK1awdu1a2rdv719E66677mL16tXk5eWx\nY8cOJk2aRFpamr/nOBTLly/nzTffJC8vj0ceeYQqVar45yzn5eWxe/du8vLy2L9/P3v27PHPF+7Q\noQMpKSncf//95OXl8emnn7Jw4UK6dOlyoB+tiIiIiIgcI0LtCc4PklYLKLl3zVEkNblOWLcxSk2u\nE1K+iIgI5s6dy+DBg0lJSSEiIoJ+/foxceJEevXqRfPmzUlMTGTYsGFFVnAGuPnmm7n11lsBqFu3\nLvfeey+dO3f2p8+YMYMZM2b4z5OTk2nWrBmDBg0iOTm5RFlDhgxh/Pjx7Ny5k+7du/Pzzz8THR1N\n69atmTNnTsB3KK2n9rLLLmPmzJlcffXVNGnShNmzZxMZGQnA2LFjSU9P99/30ksvMWrUKEaOHEml\nSpV46623uPbaaxk3bhypqalMnz6d448/PqTPU0TCa+HChfpLvkgAqh8iwamOSDiFtEWSmb0PbHfO\n9TCzSGAfcIZz7iszmwFUdc79PcyxlqsD2SJJwqes7ZmOJPruiBSlX2BEAlP9EAlOdSQ4bZEUmkBb\nJIXaEzwGeN/M3gNexjsk+gIzGwJ0BzqUW6QiInJU0C8vIoGpfogEpzoi4RTSnGDn3MdAN6ARMBkw\nYBxwNtDNOfd52CIUERERERERKSchDYcucoNZGlAb+M05d8RuyKrh0FLe9N0RKUpD2UQCU/0QCU51\nJDgNhw7NXxoObWbVfStD45z7EfixnOMTERERERERCbtQF8baB3wJfOQ7FjvnjugVodUTLOVN3x0R\nERERORTUExyaQD3BoTaC/wmcC3TEuyfwXmAZ3gbxh8BS59ze8gw43NQIlvKm746IiIiIHApqBIcm\nUCM41IWxnnXO9XPOJQHNgNuBX4Ab8DaCt5ZnsCIicuRbuHBhRYcgcthS/RAJTnVEwimkRnAxHuAn\n4H/ARrwrRe8uz6BEREREREREwiHU4dDnAefhHRLdCm+jdxHeXuCPgK9LHVt8GNNw6ENn+PDhPP30\n0xx33HFs2rSposMJG313RERERORQ0HDo0Pyl4dDA+8CtwFK8ewPXdM5d4px7yDn31ZHWAD5QDRvU\nxczCdjRsUDes8Xfs2JHo6Ghq1KhBXFwcrVq1Yvz48ezd++c07tGjRxMREcFrr73mv5aXl0dERAQZ\nGRkADBgwgIiICL788kt/nvXr1xMREfhrlJmZycMPP8y6detCagDPnDmTE088kbi4OOrWrcvAgQP5\n/fffAdi7dy//+Mc/aNiwITVq1KBly5a8++67B/x5iIiIiIjIsSukLZKA2cA5wG14e4Q/MrMPgE8K\ntk46mnk2bsY9Fr7y7ZbN4Ssc719AJk2axMCBA9m1axdffPEFQ4YMYcGCBbz//vv+PAkJCYwaNYqe\nPXtiZv7rhctJSEhg+PDhRRqfhfMU5/F4qFWrFgkJCSHF2r59e5YsWUJ8fDw7d+7kuuuuY/jw4Uyc\nOJH9+/eTkpLCokWLaNCgAW+//Ta9evVi1apVpKSkHMxHIyJhpD0eRQJT/RAJTnVEwinUhbF6Oudq\nAacD04EmwMtAjpl9Zmb3hjFGKWTjxo307NmT2rVrk5iYyC233EJ6ejr9+/f35/F4PERERJCfn++/\nVtBZHx0dTYcOHZgzZw5Lly5l3rx5/jxdunQhKiqK6dOnl7ivwDXXXMO3337LokWLyoz1gw8+oHPn\nzmzatInY2FgGDRrkj+3ZZ58lOTmZ5ORkHnroIf89ycnJxMfHA5Cfn09kZCQ//ujdlrpq1aqMHDmS\nBg0aAHDJJZfQqFEjli9fHvLnJyIiIiIix7YDWhjLOfeNc+4R4AqgF/AxcCZwVxhik2Ly8/O59NJL\nadSoER6Ph6ysLPr06QOU7I0N1jsL0KBBA84444wijdmIiAjGjBlDeno6eXl5pd5XtWpV/vOf//Cf\n//ynzHjPP/983nnnHZKSkti+fTuTJ0/2py1cuJD169czf/58xo8fz4cffuhP+/TTT4mLiyM2NpY3\n3niD2267rdTyN2/ezA8//MDJJ59cZiwicujpL/gigal+iASnOiLhFFIj2MwqmVl7MxthZh8BucA7\nQHPgVeCmMMYoPsuWLSM7O5sJEyYQHR1NVFQU7dq1O+jykpKSyMnJKXLt0ksvJTExkeeeey7gfddd\ndx0ZGRnMnz//oJ89evRoqlSpQrNmzRg4cCCvvPKKP+2ss84iNzeXrKws7rjjjlKHOu/fv5+rrrqK\nAQMGcPzxxx90HCIiIiIicmwJtSd4G95e31vx7gl8J9DCOVfHOdfbOff/whWg/CkzM5PU1NSgC1Ed\niKysLP/Q48LGjh3Lvffey+7dpe98FRUVxYgRIxgxYkSR64sXLyYmJobY2FhOOeWUgM81M+rXr+8/\nT01NLXXRrHr16tGlSxd/b3cB5xxXXXUVlStX5vHHHw/6jiJScbTHo0hgqh8iwamOSDiF2poaAZwB\n1HLO9XDOPe6cWxXGuKQUDRo0ICMjo8hcX4Bq1aqxc+dO/3l2dnaZZWVmZrJ8+XI6dOhQIu2CCy4g\nLS2NSZMmBRxWPXDgQHJzc3njjTf819q3b8+OHTvYvn07K1euDPhs5xyZmZn+84yMDJKSkkrNu2/f\nPn766aci16699lq2bNnCG2+8QWRkZND3FBERERERKSzURvAWIKO0rZDMLN7Mri7fsKQ0Z555JvXq\n1eOuu+5i586d7NmzhyVLlnDqqafyySefkJmZybZt2xg3blzAMnbt2sXHH39Mt27daNOmDRdddFGp\n+caOHcuECRMClhMZGcno0aMZP378Qb3LmDFj2LVrF6tXr2bKlCn+3t6XX37Z30D2eDwMHz6cCy64\nwH/f9ddfz7p165gzZw5RUVEH9WwROTQ0n0skMNUPkeBURyScQt0iaQrQFvitlLRGvvRp5RXU4Sa1\nfp2wbmOUWr9OSPkiIiKYO3cugwcPJiUlhYiICPr168fEiRPp1asXzZs3JzExkWHDhjF37twi9958\n883+BabS0tK44ooruP322wM+q127dpx55plB5/327duX+++/n9zc3JDiL+ycc84hLS0N5xx33nkn\n559/PgBr1qxh2LBh5ObmUrNmTS655BLuu+8+wNtj/Mwzz1ClShXq1PF+ZmbG008/Td++fQ84BhER\nEREROfZYKZ27JTOZ5QNtnHPLSknrACxwzlUOQ3xhY2aldWxjZiW2BZLy4/F4aNy4Mfv27Su3uc2H\nC313RIrSHo8igal+iASnOhKc9/fOcJVdcpvUI5Xv9/MS8zsD9gSb2alAy0KXuppZs2LZooE+wA/l\nEqUcE46WSiUiIiIiIkeegD3BZjYKGOU7dUCgjWd/A651zs0p//DCRz3BFUM9wSIiIiIif416gkMT\nqCc4WCO4BhCHt/H7E9AD+LpYtj3A5lJbk4c5NYKlvOm7IyIiIiKHghrBoQnUCA7YFeec2+ac8zjn\nNuBd/Gqe77zw8fOR2AAWEZHw0x6PIoGpfogEpzoi4RTSeFTnnAfYZ2Z/N7MHzWyKmaUCmNk5Zlb6\nJq8iIiIiIiIih5FQV4euCcwDWgM7gOpAK+fcV2b2IpDjnLslrJGWMw2HlvKm746IiIiIHAoaDh2a\nAx4OXcwDQAPgLCCBootkvQ+c/5cjFBEREREREQmzUBvBlwF3O+eW4l0purAMvA1kERERP83nEglM\n9UMkONURCadQG8HVgawAaVUIvH2SCMOHDycxMZGkJE0dFxERERGRihVqI/g7oHOAtHOAleUTzuGp\nYUpdzCxsR8OUumGNv2PHjkRHR1OjRg3i4uJo1aoV48ePZ+/evf48o0ePJiIigtdee81/LS8vj4iI\nCDIyMgAYMGAAERERfPnll/4869evD7rfb2ZmJg8//DDr1q1j06ZNZca6evVqLrzwQhITE4mMjCw1\nz4wZMzjppJOoXr06TZo04dNPPy2zXBE59Dp27FjRIYgctlQ/RIJTHZFwqhRivknAE2a2DXjZdy3O\nzAYCNwPXhSO4w4UnczNuXvjKt4s3h69wvBPCJ02axMCBA9m1axdffPEFQ4YMYcGCBbz//vv+PAkJ\nCYwaNYqePXtiZv7rhctJSEhg+PDhvPvuu0WuB+LxeKhVqxYJCQkhxXrcccfRu3dvbrrpJrp161Yi\nfcGCBfz73/9m1qxZtGrViuzs7JDKFRERERERgdC3SHoGeBhIB370XV4APANMdM69FJ7wpLiNGzfS\ns2dPateuTWJiIrfccgvp6en079/fn8fj8RAREUF+fr7/WsEKb9HR0XTo0IE5c+awdOlS5s37s3Xf\npUsXoqKimD59eon7ClxzzTV8++23LFq0qMxYP/jgAzp37symTZuIjY1l0KD/z959x1dVZf0f/6wE\nIjWhSC8JKIgCikgRRUWq2ODHCIrz0HTQmREfER0LqKCC3cd5fMaOglgQHFEEC1goKlhAQUXFRodE\nFBABiUDW74+b3Em7l5tyQhK+79frvJJz9r7nrgNZcHfOXvtcEo7tiSeeoFGjRjRq1Ij7778//JqW\nLVsyYsQIjjvuuHzPOWHCBG655RY6duwIQIMGDWjQoMFBYxGRkqd6LpHIlB8i0SlHJEixTofG3W8A\njgL+CtwE/B04xt3HBRSb5JKRkcG5555Ls2bNWLduHZs2beKiiy4C8t6NjXZ3FqBJkyZ06NAhx2A2\nLnAP2KQAACAASURBVC6O22+/nVtvvZUDBw7k+7oqVaowduxYxo4de9B4e/TowRtvvEHDhg3ZuXMn\nTz31VLht4cKF/PDDD8ybN4+7776bd99996Dny8jIYNmyZfz000+0aNGCpk2bcuWVV5Kenn7Q14qI\niIiIiEABBsEA7r7O3Z9w9zvc/TF3/zGowCSvjz/+mC1btnDPPfdQuXJlEhISOOWUUwp9voYNG7Jt\n27Ycx84991zq1KnD5MmTI77usssuY/369cybN6/Q7z1hwgQqVapEmzZtGDFiBNOnTz/oa9LS0ti3\nbx8vvfQSH3zwAStWrOCzzz5j4sSJhY5DRIKjei6RyJQfItEpRyRIMQ+CzSzezEaY2eNm9lrm1+Fm\nlv/qRVLsNmzYQHJyctSFqApi06ZN1KpVK8/xiRMnMmnSJPbu3Zvv6xISErj55pu5+eabcxx///33\nqV69OomJibRt2zbi+5oZjRs3Du8nJyfHtGhW5cqVAfjv//5v6tatS61atRgzZkyOKd0iIiIiIiLR\nxDSaMrNkYBXwJHAWUDfz61PAl5ntErAmTZqwfv36HLW+AFWrVmXPnj3h/VgWi9qwYQPLly/n9NNP\nz9PWs2dPjj76aB5++OGI06pHjBjBjh07mDVrVvhY165d+e2339i5cydffBF5wXB3Z8OGDeH99evX\nx/T4pBo1auQYPMPBp32LyKGjei6RyJQfItEpRyRIsd5S/BeQCHR196bu3tHdmwKnAUnA/wUVoPxH\np06daNCgATfccAN79uwhPT2dJUuW0K5dOxYvXsyGDRv49ddfueuuuyKe4/fff2fRokX079+fk08+\nmb59++bbb+LEidxzzz0RzxMfH8+ECRO4++67C3Utt99+O7///jurVq1iypQp4dpmgPT0dNLT03F3\n0tPTczzKacSIEfzf//0fW7duZfv27TzwwAOcd955hYpBREREREQOP7E+Iqk78Hd3X5L9oLt/YGZj\nCQ2Sy63kJvUCfYxRcpN6MfWLi4tjzpw5XHnllTRt2pS4uDguvvhi/vnPfzJo0CCOP/546tSpw/XX\nX8+cOXNyvHbUqFFcffXVABx99NEMHDiQMWPGRHyvU045hU6dOkWt+x08eDB33nknO3bsiCn+7M44\n4wyOPvpo3J3rrruOHj16AKGVrZs1axZ+hnLlypVJSUnhxx9D5ec333wzP//8My1btqRy5cpceOGF\nMS3SJSIlT/VcIpEpP0SiU45IkCz3I3Dy7WSWBgx39zfyaTsbmOrudQOILzBm5vldu5nleSyQFJ91\n69bRvHlz9u3bV2y1zaWFfnZEREREpCSEPncGde68j0ktqzI/n+epn4x1FPIsoUcj5edyYFphA5PD\nT3lJKhGJTvVcIpEpP0SiU45IkCJOhzazS7LtfgcMNLMvgJeANKAecAFQHchzh1gkEi1mJSIiIiIi\nh0rE6dBmlpFvQ/7c3cvUo5I0HVqKm352RERERKQkaDp0bCJNh462MFazAOMRERERERERKXERa4Ld\nfV1BtpIMWkRESj/Vc4lEpvwQiU45IkEqX8vzioiIiIiIiEQR0yOSyiPVBEtx08+OiIiIiJQE1QTH\npqiPSBIREREREREp80rFINjM3jSzDDO7LdfxGmY22cy2mtkuM3vLzNrk8/ojzOxeM9tsZnvMbImZ\nnVZyVyBB+eKLL2jbti1JSUlMmTLlUIcjIgWgei6RyJQfItEpRyRIh3wQbGaDgeOB/O65zwV6A1cA\nA4CKwAIza5ir31PApcBNwDnAFmCemR1fHDGmJNfHzALbUpLrHzSG6tWrk5iYSGJiIvHx8VSpUiV8\nbPr06cVxmcWifv36VK1alcTERBo1asTIkSNJT08HoEePHuGYExISqFSpUnh/zJgx+Z5v0qRJ/OlP\nf+LXX39lxIgRUd/7jz/+oFu3btSpU4eaNWvSoUMH5s+fH26fNWsWp5xyCjVr1qRx48aMGjUqHJuI\niIiIiBweClQTbGZHAicDtYE57r7NzCoBf7h7QZ4rnHW+msBXwGhgOjDR3W/JbOsHzALOdPfFmccS\ngTXAM+4+OvPYCcBnwHB3n5Z5LB5YBXzj7v0jvHfMNcFmhn9X0KuLnbUo2Lz75s2b8+STT3LmmWcG\nF1QBHThwgPj4eBo0aMCsWbPo0qULW7ZsoWfPnlx88cWMGzcuR//BgwfTtm1bxo4dG/W8Xbp04eqr\nr2bQoEEHjSEjI4NvvvmGY489FjNj8eLFnHPOOWzatInExESeeeYZGjVqRNeuXdm1axcXXHABHTp0\n4J577inStWdRTbCIiIiIlATVBMemSDXBFnIvsBF4ldCd15TM5tnAuAgvPZi7gc/dfUY+becBm7MG\nwADuvhOYA/TL1u984A9gZrZ+B4AXgD5mVrGQsZVa7p7nBzMjI4Pbb7+do446irp16zJkyBB27twJ\nwOrVq6lYsSJTp06lSZMm1KtXj/vuuy/82iVLltC+fXuSkpJo2LBhjgHrSy+9ROvWralVqxa9e/fm\n+++/D7c1aNCA+++/nzZt2pCUlJQjvqz2nj17smLFikJdZ+fOnVm2bBnDhg0jMTGR1NRUBg4cyOjR\no+nWrRuJiYn06dOH1NRUAOLi4jjuuOMwC/2cmxnp6els3rwZgCFDhtC9e3cSEhKoVasWI0aM4IMP\nPihUbCIiIiIiUjbFOh36RmAUcBvQGcg+mp4DnFvQNzazrsB/EZrqnJ/WwJf5HF8FNDWzKpn7xwFr\n3H1vPv0SgKMLGltZdO+99/L222+zZMkSNm7cSMWKFRk9enS4/cCBAyxfvpwffviB1157jXHjxrF2\n7VoARo0axbhx4/j111/57rvv6N8/dPP8iy++YMSIETz66KP89NNPnH766fTr14+MjP/c9J85cybv\nvPMOv/zyS56Y1q9fz/z582nRokWhrumjjz6iXbt2TJs2jZ07d1K/fmja+LPPPst9993Hzz//TNOm\nTfNMk+7evTuVKlWiW7du9OvXj1atWuV7/sWLF9O6detCxSYiB6d6LpHIlB8i0SlHJEixDoL/Atzm\n7ncAn+Zq+x44qiBvmnl39lHgXnf/PkK3WsD2fI5vy/xaM8Z+tQoSW1n12GOPcdddd1GvXj0SEhK4\n+eabmTHjPzfYzYzbbruNhIQEOnToQKtWrfj8888BSEhI4Ntvv2Xbtm1UrVqVjh07AjBjxgwGDBjA\naaedRoUKFRg7dixbt25l2bJl4fOOGTOGevXqccQRR4SP9e3bl8TERFJSUmjWrFmeqdAFlfuu94AB\nA+jQoQMJCQnccccdzJs3jx07doTb3333XXbt2sUrr7wSccr47NmzmT17NjfffHORYhMRERERkbIl\n1kFwI+DDCG1/AFUL+L7XA5WAOwr4Oolgw4YNnH322dSqVYtatWrRvn17ALZtC/0uID4+npo1a4b7\nV6lShV27dgHw9NNPs3LlSlq2bEmXLl3Ci0lt3ryZ5OTk8Gvi4uJo1KgRmzZtCh9r3LhxnljefPNN\ndu7cyVtvvcWXX34ZjuFgzjjjjPBCWa+++mrEfk2aNAl/X6dOHSpVqsSWLVty9KlQoQLnnXce06dP\nz/ObxIULFzJy5EjmzJmT41wiUry6det2qEMQKbWUHyLRKUckSBVi7LcJaAMsyKftBEKLVcXEzJoA\nYwmt5lwpc2GtrOnVR5hZEvAbobu7NfM5Rdad3e3ZvjaN0i/iCGz48OGkpKQAUKNGDdq1axfrZZQ6\njRs3ZtasWZx44ol52rZu3Rr1tccccwwvvPAC7s706dMZMGAAO3bsoGHDhqxbty7cLyMjg02bNuUY\n+GbV32aXdee2R48eDBo0iOuuuy6mFawXLVp00D4QGvBn2bp1K+np6TRo0CDfvvv37+eHH34I/0O6\ndOlSLrzwQmbMmEHnzp1jer/CyBp4Z72v9rWvfe1rX/va1772tV+8+2TuU6z7WQ799RV8f8WKFeEZ\nolmln/nKWmQp2kZoAautwKlAPJABnAi0JLRY1i2xnCfzXGcABzK3jGzbgWxfjweeBNbn8/ophGqA\ns/ZvBvYClXL1mwD8DlSMEIfnJ7/jgPt3wW2RYokkJSXF33nnnRzH7rzzTu/Vq5dv2LDB3d3T0tJ8\nzpw57u7+zTffeIUKFXL0P/nkk/25555zd/dp06b5L7/84u7uc+bM8erVq/v+/fv9888/98TERH/v\nvfd83759PnHiRD/22GP9wIED7u5ev359/+CDD3KcN/exzZs3e+XKlf2bb77J0e+iiy7ySZMmHfRa\nO3To4DNmzAjvX3DBBV67dm1ftmyZ792710eOHOlnnXWWu7t//vnn/tZbb/nevXv9jz/+8Mcee8wr\nVarkq1evdnf3Tz75xI888kifO3fuQd+3MAr69yhS3i1YsOBQhyBSaik/RKJTjkQX4zCuUFt5+kyb\neS15LjLWO8ETgFOAxUDWrcEXgSbAEuCuGM8DoccZ5VeouRB4BphMqM74VWC4mZ3m7u9B+BFJ5wHP\nZnvdHOBWYGDm67MekTQImOfu+woQW76Sm9bDWqQV9TRRz18Q+d19vf7664mPj6d79+6kpaVRr149\nhgwZwrnnnpvva7Lvz507lzFjxvDHH3+QkpLCiy++SHx8PG3btuXJJ5/ksssuIzU1lfbt2zN79mzi\n4uIixpH7WIMGDRg8eDATJ07kmWeeiXoNsV7rkCFDuOaaa/j000/p3LkzTz/9NBBa/OvGG2/k22+/\npUKFCrRq1YpXXnmFli1bAnDPPfewY8cOBg8eHL5b3bp1az78MNJMfxERERERKW9ifk5w5sDyYqAP\nUBf4BXgTeM7d9xc5ELMMcj4n2ID3gcbAdcAOQqtUtwFOcPdN2V47Heid2W8N8HfgbKCLu6+M8H6e\n37XrWa+l28CBA+nYsSPXXXfdoQ4lD/3siIiIiEhJ0HOCYxPpOcGx3gnOevbuM5lbEDxzy3o/N7Nz\ngPuAhwgtpLUE6JZ9AJxpODAJuB2oAawE+kQaAIuIiIiIiMjhKS6WTmb2lJldG6GtuZk9VdRA3D3e\n3cfnOrbD3f/i7ke6ezV37+3ueZ4d7O7p7n6tuzd09yru3iVrCrWUL7FOoxaRQ29h7hU2RCRM+SES\nnXJEghTrneDhgJtZR2Cou6dna6sDDAMuKebYRPKYOXPmoQ5BRERERETKsJhqgjPrdccB1wLfAue7\n+9bMts7AEnePDzLQ4qaaYClu+tkRERERkZKgmuDYRKoJjmk6dKZ3gC6Enr/7sZkdV1zBiYiIiIiI\niJSEggyCcfdvgc7Aj8ASM+sTSFQiIlLmqZ5LJDLlh0h0yhEJUoEGwRBarIrQY5JmEnpG72XFHZSI\niIiIiIhIEApSE3yyu3+c6/jVwL2Z51FNsBzW9LMjIiIiIiVBNcGxKWpN8JnA17kPuvsDwOloZWgJ\nyBdffEHbtm1JSkpiypQphzocEREREREp42IaBLv7Inf/LULbEnd/unjDKl1SUupjZoFtKSn1DxpD\n9erVSUxMJDExkfj4eKpUqRI+Nn369BL4U4hN/fr1qVq1KomJiTRq1IiRI0eSnh56olaPHj3CMSck\nJFCpUqXw/pgxY/I936RJk/jTn/7Er7/+yogRIw76/tdddx1t2rShQoUK/M///E+e9tTUVAYNGkRS\nUhJHHnkkl19+edEuWEQiUj2XSGTKD5HolCMSpIjPCTazW4DJ7r458/to3N1vL97QSo9169ICm24A\nYJZ20D6//faf30E0b96cJ598kjPPPDO4oArowIEDxMfHY2a8/fbbdOnShS1bttCzZ0/uu+8+xo0b\nxzvvvBPuP3jwYNq2bcvYsWOjnnfdunUMGDAg5jiOPfZYevXqxX333Zdv+znnnMN5553H1KlTSUhI\n4Kuvvor53CIiIiIiUvZFuxM8AWic7fuDbVJC3D3PPP2MjAxuv/12jjrqKOrWrcuQIUPYuXMnAKtX\nr6ZixYpMnTqVJk2aUK9evRyDxCVLltC+fXuSkpJo2LAh48aNC7e99NJLtG7dmlq1atG7d2++//77\ncFuDBg24//77adOmDUlJSTniy2rv2bMnK1asKNR1du7cmWXLljFs2DASExNJTU1l4MCBjB49mm7d\nupGYmEifPn1ITU0Nv2bEiBH06tWLKlWq5Dnfyy+/THp6OhMmTKBKlSpUqFCB448/vlCxicjBdevW\n7VCHIFJqKT9EolOOSJAiDoLdPS5rIazM76NtZWpRrPLo3nvv5e2332bJkiVs3LiRihUrMnr06HD7\ngQMHWL58OT/88AOvvfYa48aNY+3atQCMGjWKcePG8euvv/Ldd9/Rv39/IFSPO2LECB599FF++ukn\nTj/9dPr160dGRkb4vDNnzuSdd97hl19+yRPT+vXrmT9/Pi1atCjUNX300Ue0a9eOadOmsXPnTurX\nD00bf/bZZ7nvvvv4+eefadq0aUzTpAE+/PBDjj76aAYOHMiRRx7JqaeeykcffVSo2EREREREpGwq\n8COSpHR67LHHuOuuu6hXrx4JCQncfPPNzJgxI9xuZtx2220kJCTQoUMHWrVqxeeffw5AQkIC3377\nLdu2baNq1ap07NgRgBkzZjBgwABOO+00KlSowNixY9m6dSvLli0Ln3fMmDHUq1ePI444Inysb9++\nJCYmkpKSQrNmzXLcWS6M3He9BwwYQIcOHUhISOCOO+5g3rx57Nix46Dn2bhxI3PnzmXQoEGkpaVx\n6aWXcv7557Nr164ixSci+VM9l0hkyg+R6JQjEqSYBsFm1tLMOmXbr2xmd5rZHDMbFVx4EqsNGzZw\n9tlnU6tWLWrVqkX79u0B2LZtGwDx8fHUrFkz3L9KlSrhwd/TTz/NypUradmyJV26dGH+/PkAbN68\nmeTk5PBr4uLiaNSoEZs2bQofa9y4Mbm9+eab7Ny5k7feeosvv/wyHMPBnHHGGeGFsl599dWI/Zo0\naRL+vk6dOlSqVIktW7Yc9PyVK1emdevWDBw4kPj4eC655BKqVq3KJ598ElN8IiIiIiJS9sV6J/hf\nwAXZ9icB1wANgQfM7IriDkwKpnHjxrz77rts27aNbdu2sX37dnbv3k2tWrUO+tpjjjmGF154ga1b\nt3LllVcyYMAA9u/fT8OGDVm3bl24X0ZGBps2bcox8DXL89it8J3bHj16MGjQIK677rqYrmHRokX8\n9ttv7Ny5k/PPPz9ivw0bNoS/37p1K+np6TRo0OCg5z/++OPzxJtf/CJSPFTPJRKZ8kMkOuWIBCnW\nQfAJwAcAZhYHDAWud/eTgInAZcGEJ7G6/PLLuf7669m4cSMAP/30E3Pnzg23R3vg9TPPPMO2bdsw\nMxITE4mLi8PMuPDCC3n55Zd5//332b9/P3feeSdHHnkkJ510UsxxXXPNNcyePZvVq1cX/uJymTVr\nFsuXLyc9PZ1x48bRu3dvatSoAcD+/fvZu3cvGRkZ7Nu3j/T09PC1Dxo0iB9//JFZs2aRkZHBtGnT\n2LNnT3j6t4iIiIiIlH+xDoKTgKyVj04EagL/ztxfCDQv3rBKl+TkepgR2JacXK9A8eR39/L666+n\nV69edO/enaSkJLp27cpnn30W8TXZ9+fOncsxxxxDUlIS48aN48UXXyQ+Pp62bdvy5JNPctlll1G3\nbl0WLFjA7NmziYuLixhH7mMNGjRg8ODBTJw48aDXEOu1DhkyhGuuuYY6deqwZs0annzyyXDb4MGD\nqVKlCnPnzmXs2LFUqVKFl19+GQg9w/jll1/mpptuombNmjz66KPMnTuXatWqxRSLiBSM6rlEIlN+\niESnHJEgWbQ7hOFOZuuA8e4+1cxuBEa4e8vMtnOAZ929ZtSTlDJm5vldu5lFvWsqh9bAgQPp2LFj\nzFOsS5J+dkRyWrhwoaaziUSg/BCJTjkSXehzZ1Dnjj6LtCzJ/Hye565ahRhf/ypwp5m1AYYDj2Vr\nawv8WOQIRUSkXNGHF5HIlB8i0SlHJEixDoJvACoBfQgNiCdlazsfmF/McYnkSwtZiYiIiIhIUcQ0\nHbo80nRoKW762RHJSVPZRCJTfohEpxyJTtOhYxNpOnSsC2OJiIiIiIiIlHm6E5z3eLn5zYeULP3s\niIiIiEhJ0J3g2OhOsIiIiIiIiBz2NAjOJTk5GTPTpq3AW3Jy8qH+8RUpVfSMR5HIlB8i0SlHJEix\nrg592Fi7du2hDkHKCC3YICIiIiJS9qgmWEREREREpAwxU01wLEJ/TnlrgmO+E2xmZwCDgaaEnhmc\nnbt7j6KFKCIiIiIiIhKsmGqCzexyYAFwAVADsFybaovlsKNaFZHolCMikSk/RKJTjkiQYr0TfA3w\nPHCJu/8RYDwiIiIiIiIigYmpJtjMdgPnu/s7wYdUMlQTLCIiIiIiZZFqgmMTqSY41mnMy4HmxRuS\niJR3Dz/8MCeccAJJSUkkJSVxyimn8Prrr4fbb7nlFo499liqVatGrVq16NmzJ0uXLs1zno8//pje\nvXtTvXp1EhMT6dq1K9u2bSvJSxERERGRciLWQfB/A6PN7PQggxEpS1SrcnBNmjThnnvu4bPPPmP5\n8uV0796d/v378+WXXwLQqlUrHn74Yb788ks++OADmjVrxllnncXWrVvD5/joo4/o06cP3bt35+OP\nP+bTTz/l2muvpWLFiofqsiRGyhGRyJQfItEpRyRIEadDm9kGIHtjElAN2ANsz9Xd3T05kAgDounQ\nUlR6TnDh1K5dm7vuuouRI0fmafvtt99ISkpi3rx59OrVC4BTTz2VHj16cNttt5V0qFJEyhGRyJQf\nItEpR6LTdOjYFOYRSe+QcxAsItnoH+aCycjIYObMmezevZtTTjklT/u+fft47LHHSEpKol27dgBs\n3bqVpUuXcvHFF3Paaafx7bffcswxxzBhwgS6d+9e0pcgBaQcEYlM+SESnXJEghRxEOzuw0swDhEp\np7788ku6dOnC3r17qV69Oi+//DKtW7cOt7/22mtcdNFF7Nmzh4YNG/LWW29Rp04dAH788UcAJkyY\nwH333Ue7du2YOXMmffr04dNPP6Vt27aH5JpEREREpOzS831FCkm1KrFp1aoVK1eu5OOPP+Zvf/sb\nQ4cO5auvvgq3d+/enZUrV7J06VLOOussBg4cSFpaGhC6ewzw17/+lWHDhnHCCScwadIkOnbsyKOP\nPnpIrkdipxwRiUz5IRKdckSCFNMg2MweMLNnIrQ9Y2b3FW9YIlJeVKhQgebNm3PiiScyadIk2rVr\nxwMPPBBur1y5Ms2bN6dTp0488cQTVKxYkcmTJwPQoEEDAI499tgc5zzuuONYv359yV2EiIiIiJQb\nsd4JPh+YH6FtHtCveMIRKTtUq1I4GRkZpKenx9SekpJCw4YNWb16dY4+3377LcnJZWotvsOSckQk\nMuWHSHTKEQlStIWxsmsERLrtsjGzXUQkhxtvvJFzzjmHJk2a8Ntvv/Hcc8+xaNEiXn/9dX777Tfu\nuecezjvvPBo0aMDWrVv517/+xaZNmxg0aFD4HP/4xz+YMGECbdu25cQTT2TGjBl89NFHPPzww4fw\nykRERESkrIp1ELwdOBpYlE/b0cCuYotIpIzQ0v0Hl5qaypAhQ0hNTSUpKYnjjz+eN998k549e/L7\n77+zatUqpkyZwi+//ELt2rXp2LEj7733Hm3atAmf46qrruKPP/7g2muv5ZdffqF169a8+eabOfpI\n6aQcEYlM+SESnXJEghTrIPht4CYzm+vuaVkHzaweMBZ4K4jgRKRsmzJlSsS2ypUrM2vWrJjO849/\n/IN//OMfxRWWiIiIiBzGLJYHIZtZCvAJcAQwl/9MgT4X2Auc7O5rAosyAGbm5eUh0CKlUUpKfdat\nSzt4x0JITq7H2rWpgZxbREREpLQzM4IayphBeRknhf6c3PIcj/UCMwfCtwG9gNrAz4QWyxrv7uuK\nLdISokGwSLD0j7OIiIhIMPQ5KzaRBsExPyfY3de6+1B3b+DuCe7e0N2Hl8UBsEhx0PPrRKJTjohE\npvwQiU45IkGK9TnB75pZqwhtLc3s3eINS0RERERERKT4xVoTnEGo7vfjfNpOAj529/gA4guMpkOL\nBEvTdERERESCoc9ZsSnydGgg0p/EUegRSSIiIiIiIlIGRBwEm9kIM1tsZosJDYAfz9rPtn0CPA28\nV1IBi5QWqlURiU45IhKZ8kMkOuWIBCnaneAM4EDmZrn2s7ZfgEeAS4MNU0RERERERKToYq0JXgD8\nzd2/CT6kkqGaYJFgqVZFREREJBj6nBWbIj8nuLzRIFgkWPrHWURERCQY+pwVm+JYGAszO8HMBpnZ\n0Nxb8YUqUjaoVkUkOuWISGTKD5HolCMSpAqxdDKzGsBrwMlZhzK/Zv8VwbRijEtERERERESk2MVa\nE/ww0J3QAljvAf8P+BW4BOgCXOTuywOMs9hpOrRIsDRNR0RERCQY+pwVm6JOh+4D3AF8mLm/0d0X\nuvtQ4G3gquIJU0RERERERCQ4sQ6CGwA/uvsBYC9QPVvbLOCc4g5MpLRTrYpIdMoRkciUHyLRKUck\nSLEOglOBGpnfryM0BTrL0cUakYiIiIiIiEhAYq0Jngasd/ebzGwscAvwNLAfGAa86u4XBxppMVNN\nsEiwVKsiIiIiEgx9zopNpJrgmFaHBm4FGmZ+fy9QG7gQqAK8ClxZHEGKiIiIiIiIBCmm6dDu/oO7\nv5f5/T53v8bdG7t7LXe/2N1/CTZMkdJHtSoi0SlHRCJTfohEpxyRIMVaE5yDmdUp7kBERERERERE\nghZTTTCAmZ0B3AZ0AhKAP4CPgFvcfXFgEQZENcEiwVKtioiIiEgw9DkrNkV6TrCZDQTeBeoSqgn+\nb+A+oB7wrpldUMBgepvZO2a2xcz2mtkGM5thZsfm6tfYzP5tZjvM7Fcze8nMmuRzvhpmNtnMtprZ\nLjN7y8zaFCQmERERERERKf9inQ59G/Aa0Nrdb3H3h9z9ZqA18CZwewHftxawDLgC6AXckHmu+UF8\n6AAAIABJREFUpVmDXDOrDCwAWgJDgP8CWhAadFfOdb65QO/M8w0AKgILzKwhIgFRrYpIdMoRkciU\nHyLRKUckSLGuDt0MGOPuGdkPunuGmT0MvFSQN3X3F4AXsh8zs0+Ab4ALgAeAy4AUoKW7r8ns8wXw\nHXA58M/MY/0IPbf4zKxp2Wb2IbAGuA4YXZDYREREREREpPyK9TnBXwD3uvu0fNqGAde6e9siBWJ2\nJPAT8N/u/i8zexs4wt1Py9VvIeDufmbm/mSgj7s3ydVvKnCGuzeL8H6qCRYJkGpVRERERIKhz1mx\nKVJNMDAOuNXMOuY6aWdgAnBjIYOKM7OKZtYCeAzYDEzPbG4NfJnPy1YBx2Xbj9avqZlVKUxsIiIi\nIiIiUv5EHASb2eKsDbgWqAR8aGZrzewjM1sLLMk8/o9Cvv9HQDqwGmgD9Mj2zOFawPZ8XrMNqJlt\nP1o/cvUVKTaqVRGJTjkiEpnyQyQ65YgEKVpNcAaQ/T74N5lbljWZW1H8F5AINCc00H7bzE519/VF\nPK+IiIiIiIhIHhEHwe7eLeg3d/fVmd9+YmZvAmsJrRT9d0J3d/O7i5v7zm+0fpD/XWIAhg8fTkpK\nCgA1atSgXbt2dOvWDfjPb5+0r/1o+1lKSzylbT9L1m5mc5H3s97jUF+f9rWvfe1rX/vaD2a/W7du\npSqe0rlP5j7Fup/l0F9fwfdXrFjBjh07AFi7di2RxLQwVknJXCF6u7v3NrN3gIrufnquPgsAsi2M\n9STQy92b5uo3BeimhbFEDg0t2CAiIiISDH3Oik1RF8YKnJnVA1oB32ceehU42cxSsvVJAU4FZmd7\n6atAIzM7LVu/ROC8XP1EitXC3L8uE5EclCMikSk/RKIrDzly55130qlTJ5KSkqhbty7nn38+q1at\nytFn9+7dXHnllTRp0oQqVarQqlUr/vnPf+bo88QTT9C9e3dq1qxJXFwc69evL8nLKJcOySDYzGaZ\n2U1mdr6ZdTOzy4GFwB/A/2R2e4LQ9OjZmf3OB14B1gGPZzvdq8CHwLNmdqGZ9ck8BnBv8FcjIiIi\nIiKS0+LFixk1ahRLly5lwYIFVKhQgZ49e4an6wJcffXVvPHGGzz33HN888033HTTTdxwww0899xz\n4T579uyhT58+3HrrrZjluakphXBIpkOb2T+AQcBRQAKwAVgA3JV9USwzaww8APQCDHgbuDr3wllm\nVgO4D+hPaLXqJcAYd8/v0UlZr9F0aJEAaZqOiIiIyH/s3r2bpKQkZs+ezTnnnANA27ZtueCCCxg/\nfny4X7du3Tj++ON58MEHc7x++fLldOrUiTVr1pCcnKzPWTEoVdOh3f1ed+/o7rXcvZq7H+vuf889\nuHX3je4+0N1ruHuSu/8pv5Wj3X2Hu//F3Y/MPF/vaANgERERERGRkrRz504yMjKoWfM/a/p27dqV\nOXPmsHHjRgCWLFnCypUr6du376EK87BQamqCRcqa8lCrIhIk5YhIZMoPkejKY45cddVVtG/fni5d\nuoSPPfjggxx//PE0bdqUhIQEzjzzTO6++24NggMW7TnBeZhZfaApoSnHObj74uIKSkREREREpLwY\nM2YMS5Ys4YMPPshR1/vggw+ydOlS5s6dS9OmTVm8eDHXXHMNKSkp9O7d+xBGXL7FVBNsZo2AZ4Az\n8msG3N3jizm2QKkmWCRYqgkWERERCS1+NXPmTBYuXEiLFi3Cx/fu3UtSUhIvvfQS5557bvj4yJEj\nWbduHfPnz89xHtUEF1xRa4IfAdoC1wF9ge7ZtjMzv4qIiIiIFItYHi8TFxdHfHw8cXFxObYrr7wS\ngP3793P99ddzwgknUK1aNRo2bMif//xnNmzYcCguSQ5DV111FTNmzGDBggU5BsAA+/btY9++fcTF\n5RySxcfHk5GRUZJhHnZiHQSfRmi15fvdfb67L8q9BRmkSGlUHmtVRIqTckQkMuXHwcXyeJnU1FS2\nbNlCamoqqampzJkzBzPjwgsvBEKPllmxYgU333wzn332Ga+++iobNmygb9++GmSUcuUhR6644gqm\nTp3K888/T1JSEmlpaaSlpbF7924AqlevzhlnnMENN9zAokWLWLt2LVOnTmXatGkMGDAgfJ60tDRW\nrlzJ6tWrcffwL4O2bz8kl1UuxDodejMwwt3nBR9SydB0aCmqhQsX0q1bt0MdRqml6dCiHBGJTPlR\ncPk9Xia3kSNH8v777/P1119HPM/XX39N69at+eKLL2jdunVQ4UoRlYcciYuLy/e5vuPHj+eWW24B\n4KeffuLGG29k/vz5bNu2jeTkZEaOHMnVV18d7n/rrbfmeUZwRkYGTz8NQ4cWf9zl6XNWpOnQsQ6C\nbwWOcvf/CiK4Q0GDYJFgaRAsIiLFacuWLTRq1Ij333+fU045JU/77t27adCgAbfeemuOAURuH374\nIaeeeiobNmygYcOGQYYsEhh9zopNpEFwrKtDbwKGmNk7wBvAttwd3P2pooUoIiIiIpK//B4vk91z\nzz3Hvn37GBrl1ti+ffu45pprOP/88zUAFjmMxVoT/CiQQmgRrHuAybm2J4IITqQ0Kw+1KiJBUo6I\nRKb8KJisx8u89NJL+U4vBZg8eTL9+/endu3a+bYfOHCAP//5z+zcuZOnntK9m9KurOdISnJ9zCyw\nTYom1jvBzQKNQkREREQkH9kfL5OcnJxvnxUrVrBs2TLuuuuufNsPHDjARRddxKpVq1i0aBE1a9YM\nMmQR1q1Pw78L7vzW4uB9JLKYaoLLI9UEiwRLtSoiIlJUV111FS+++CILFy6kZcuWEfv9/e9/Z/78\n+Xz//fd52vbv38+FF17IV199xaJFi6hbt26QIYsAmZ+DAh4E63PWwRW1JjjrJMcDpwO1gcfcPdXM\njgbS3P234glVRERERA53V1xxBc8++yyzZ88OP14GoFq1alStWjXc7/fff+f555/nhhtuyHOOAwcO\ncMEFF7B8+XLmzJmDu4fPk5SURKVKlUrmYkSkVImpJtjMjjCzF4HPgAeBW4Cs1QTuAcYFE55I6VXW\na1VEgqYcEYlM+XFwjzzyCLt27aJHjx40bNgwvN1///05+s2YMYM9e/YwfPjwPOfYuHEjc+bMYfPm\nzZx00kk5zjNz5swSuhIpDOWIBCnWO8GTgJ7AEOAtIC1b2xvA34G8v34TERERESmEjIyMmPoNHz48\n3wEwQHJyMgcOHCjGqESkPIj1OcGbgDvc/SEziwf2AR3c/VMz6wm86O5laoUB1QSLBEs1wSIiUlgp\nKfVZty7t4B0LKTm5HmvXpgZ2fhHVBJcORa0Jrg18HaEtDjiisIGJiIiIiGS3bl1aYB/wAcyCG2CL\nSOkX63OC1wD5P5kcOgGriycckbJDtSoi0SlHRCJTfohEpxyRIMU6CJ4G3GBmfwYqZh5zMzsTuBrQ\nE8dFRERERESk1Iu1JjgeeA4YBKQTmv78O1AJeMHd/xxkkEFQTbBIsFQTLCIihRXk/yGh8+v/EQmW\naoJLh0g1wTENgrOd5DSgD1AX+AV4090XFVuUJUiDYJFgaRAsIiKFpUGwlHUaBJcOkQbBsU6HBsDd\n33P3m9z9Mne/sawOgEWKg2pVRKJTjohEpvwQiU45IkGKaRBsZp+Z2Wgzqxd0QCIiIiIiIiJBibUm\n+HWgF+DAO8DTwCvuvjfY8IKj6dAiwdJ0aBERKSxNh5ayTtOhS4ciTYd297OBRsB1QB3geSDNzJ7K\nXCFaREREREREpNSLuSbY3X9y93+6ewegNfAQ0B1428zWBRWgSGmlWhWR6JQjIpEpP0SiU45IkAq0\nMFYWd/8auA0YB2wGGhdnUCIiIiIiIiJBKNAjkgDMrDswBBgAVAM+Bp5x94eLP7zgqCZYJFiqCRYR\nkcJSTbCUdaoJLh0i1QRXiPHFbYD/Ai4mdNd3LfC/hAa/Af71ioiIiIiIiBSfWKdDfw5cDrwJnO7u\nzd39Fg2A5XCmWhWR6JQjIpEpP0SiU45IkGK6EwwMAua4e3qQwYiIiIiIiIgEqTA1wdWA2sBmd98X\nSFQlQDXBIsFSTbCIiBSWaoKlrFNNcOlQpOcEZ57gXDP7FPgV+AFom3l8spldXGyRioiIiIiIiAQk\npkGwmfUHZgM/A9fnet0aYFjxhyZSuqlWRSQ65YhIZMoPkeiUIxKkWO8EjwemuHtv4J+52r4E2hRr\nVCIiIiIiIiIBiKkm2Mz2Aue5+1tmFg/sAzq4+6dmdjow390rBRxrsVJNsEiwVBMsIiKFpZpgKetU\nE1w6FLUmeCdwZIS2FGBrIeMSERERERERKTGxDoLfAm40sxrZjrmZHQGMAt4o9shESjnVqohEpxwR\niUz5IRKdckSCFOtzgscBHwOrgdcBB24AjgeSgP6BRCciIiIiIiJSjGJ+TrCZNQZuBfoAdYFfgDeB\nW9x9Q2ARBkQ1wSLBUk2wiIgUlmqCpaxTTXDpEKkmOOZBcHmjQbBIsDQIFhGRwtIgWMo6DYJLh6Iu\njCUiuahWRSQ65YhIZMoPkeiUIxIkDYJFRERERETksKHp0CISCE2HFhGRwtJ0aCnrNB26dNB0aBER\nERERETnsaRAsUkiqVRGJTjkiEpnyQyQ65YgEKaZBsJk9ZWbNIrQlm9lTxRuWiIiIiIiISPGLqSbY\nzDKAk93943zaTgI+dvf4AOILjGqCRYKlmmARESks1QRLWaea4NKhOGqCI/1J1Ad+L1RUIiIiIiIi\nIiUo4iDYzP6fmU0zs2mZh27N2s+2vQg8CSwvkWhFShHVqohEpxwRiUz5IRKdckSCVCFKW1PgtMzv\nHWgHpOfqkw4sAW4s/tBEREREREREilesNcFrgP7uvjL4kEqGaoJFgqWaYBERKSzVBEtZp5rg0iFS\nTXC0O8Fh7p7vytAiIiIiIiIiZUmsj0jqZ2Yjsu0nm9lSM/vNzP5tZtWCC1GkdFKtikh0yhGRyJQf\nItEpRyRIsa4OfRNQJ9v+/wCNgceB04EJxRuWiIiIiIiISPGLtSZ4G3Cxu79pZpWBbcBQd3/RzP4C\n3OjuRwUca7FSTbBIsFQTLCIihaWaYCnrVBNcOhT1OcGV+M+zgE8hVEs8P3N/NdCwyBGKiIiIiIiI\nBCzWQfBaoGvm9/2A5e7+a+Z+XeDX/F4kUp6pVkUkOuWISGTKD5HolCMSpJhWhwYeA+4zs/9H6HnB\nf8vW1gX4qrgDExERERERESluMd0Jdvf/BYYDS4FL3P2JbM3VganFHplIKdetW7dDHYJIqaYcEYlM\n+SESXUnkyJ133kmnTp1ISkqibt26nH/++axatSpPvwkTJtCoUSOqVKnCmWeeyVdf5bz/t2PHDoYM\nGUKNGjWoUaMGQ4cODTx2KZpYp0Pj7s+5+5XuPi3X8ctzHxOR4vPee+/Rr18/GjduTFxcHNOm5Uy3\nn376ieHDh9OoUSOqVq3K2Wefzffffx9uX7duHXFxccTHxxMXF5dju//++0v6ckRERERKhcWLFzNq\n1CiWLl3KggULqFChAj179mTHjh3hPnfffTcPPPAADz30EMuWLaNu3br06tWL3bt3h/sMHjyYFStW\nMH/+fObNm8enn356KC5HCiDmQbCFnG9m95nZFDNLzjx+hplpYSw57JRUrcquXbto27YtDz74IFWq\nVMnT3q9fP3744QdeffVVVqxYQdOmTenZsye//x5ay65p06akpqayZcsWUlNTSU1N5eGHHyYuLo6B\nAweWyDXI4Un1XCKRKT9EoiuJHHnjjTcYOnQoxx13HK1bt+aZZ55h69atfPDBB+E+//u//8uNN95I\n//79Oe6443j66af57bffeP755wH4+uuvmTdvHk888QSdOnWic+fOPPbYYwB8tzbwS5BCimkQbGY1\ngSXAK8BIYChQO7N5JHBDrG9oZn8ys3+b2Voz22Nm35jZHWZWLVe/GmY22cy2mtkuM3vLzNrkc74j\nzOxeM9uceb4lZnZarPGIlHZ9+/Zl4sSJDBgwALOcK7x/9913fPTRRzzyyCOcdNJJtGjRgkceeYTf\nf/+d6dOnA6Gl4evWrZtje+mll+jZsydNmzY9FJckIiIiUurs3LmTjIwMatasCcCaNWtITU2lV69e\n4T6VKlXi9NNPZ8mSJQAsXbqU6tWrc/LJJ4f7nHrqqQAs0Q3hUivWO8H3Ak2AUwkNfrN/En8b6FGA\n97wW2A/cCPQBHia00Nb8XP3mAr2BK4ABQEVgQT53nZ8CLgVuAs4BtgDzzOz4AsQkUmCloZ4rPT0d\nM+OII44IH8vaf//99/N9zY8//si7777L5ZdfXlJhymGqNOSISGml/BCJ7lDkyFVXXUX79u3p0qUL\nAKmpqZgZ9erVy9GvXr16pKamApCWlkadOnXyPV/q1mDjlcKLdXXofsC17r7UzOJzta0nNECO1bnu\n/ku2/ffMbDsw1cy6uftCM+tHaNXpM919MYCZfQisAa4DRmceOwEYDAzPqks2s8XAKuA2oH8B4hIp\nc1q1akWTJk0YO3Ysjz/+OFWrVuWBBx5g48aNbNmyJd/XTJ48Obz4g4iIiIjAmDFjWLJkCR988EGe\nmXdS/sR6J7gasClCWyVy3hmOKtcAOMsnmedolLl/HrA5awCc+bqdwBxCA/Is5wN/ADOz9TsAvAD0\nMbOKscYlUlCloZ6rQoUKvPzyy/zwww/Url2batWqsWjRIs4++2zi4vKm94EDB5g6dSrDhw8nPj73\n77NEildpyJHSrqgL3wE88cQTdO/enZo1axIXF8f69etL8hKkkJQfItGVZI5cffXVzJgxgwULFpCc\nnBw+Xr9+fdydtLS0HP3T0tKoX79+uM/Wrfnf8q2f/w1iKQViHQSvJjQ1OT9nAF8UMY5ugPOf5w23\nBr7Mp98qoKmZZa0OdBywxt335tMvATi6iHGJlHonnngin376Kb/++itbtmzh9ddf5+eff6Z58+Z5\n+r766qukpaVx6aWXHoJIRSS3oi58B7Bnzx769OnDrbfeqrsXIiIFdNVVV4UHwC1atMjR1qxZM+rX\nr89bb70VPrZ3717ee++9cN1vly5d2LVrFx9++GG4T1a98CntS+ACpFBinQ79MPAvM/sVeD7zWA0z\nGwGMAi4rbABm1gi4FXjL3T/LPFyL0NTn3LZlfq0J7Mnstz1Kv1qFjUvkYEpbPVf16tWB0GJZy5Yt\nY9KkSXn6TJ48mTPOOIOjj9bvhyR4pS1HSqO+ffvSt29fAIYNG5ajLWvhu88//5w2bULrQj7yyCPU\nr1+f6dOnc8kllwChD3AAy5cvL8HIpaiUHyLRlUSOXHHFFTz77LPMnj2bpKSk8B3fatWqUbVqVQBG\njx7NnXfeyTHHHEOLFi2YOHEi1atXZ/DgwUCoNK1Pnz5cfvnlPPbYY7g7f/3rXwFokRL4JUghxTQI\ndvfHzaw5ocHqbZmH3wIygHvc/bnCvLmZVQVmE5rSfElhziFS3u3evZvvv/8edycjI4P169ezcuVK\natWqRZMmTfj3v//NkUceSXJyMp9//jmjR49mwIAB9OiRc7269evXM2/ePJ599tlDdCUiUhAHW/gu\naxAsIiKF88gjj2BmeT4zjR8/nltuuQWA6667jr179zJq1Ci2b99O586dmT9/fniQDDB9+nSuvPJK\nzjrrLCA0i+eLL4o6UVaCFOudYNz9BjN7hNC06DrAL4Tu3v5YmDc2s0qEVoBOAU53983ZmrcTutub\nW61s7Vlf83vGS1a/bfm0hQ0fPpyUlBQAatSoQbt27cK/dcqqQ9C+9iPtr1ixgtGjRwf+fsuWLePM\nM88MT3McP34848ePp3fv3rzxxhts2bKFK664gu3bt9OoUSOGDRvGaaedxsKFC3Ocb+rUqdSsWZMB\nAwaU2J9XlqzdrF/qFnU/6z1K08+D9vP/++/WrVupiae072fJ2u/atStNmjRh5MiRXHPNNfTp0ye8\n8N2qVavy9M+aDbJ06VJ+/PHHQ3492o++n3WstMRT2vazFPf/H7lOX2quV/t593PnSnGfP6VpqN7X\n3ckt67NWfhYuXEjr1q3zbcuStb7Dwo8y4+9MMPsLM/e7Fe9+ltL08xDr/ooVK9ixYwcAa9euJRLL\n7y8+aGZWgdAd4K5AT3f/JFf7k0Avd2+a6/gUoJu7N8vcvxkYB9TIXhdsZhOA64FEd98XIQY/FNcu\n5cfChQvDSSd5mRlBpZgZ+f6nJaWLcqRgqlevzkMPPcTQoUPDxz777DMuvfRSVqxYQYUKFejZsydx\ncXG4O6+99lqO1y9fvpxOnTqxZs0aPQO8DFB+RBfk/yGh8+v/kdIu6BwxM/z1wE6PnQ3+XYDnb4E+\nZ8Ug9G+J51kwI64AJ4g3sxFm9riZvZb5dXg+j0w62HmMUF1xN6Bf7gFwpleBRmZ2WrbXJRJaNXp2\ntn5zCC2ANTB7nMAgYF6kAbBIcQj6w0tK0/qYWWBbStP6gcYvog/4RVeQhe+kbFF+iESnHJEgxTQd\n2sySgXlAS2AjkAa0Bf4CXG9mZ7n7uhjf82HgAmAi8LuZdc7WttHdNxEaBH8IPGtm1wE7gBsz+9yb\n1dndV5jZDOCfZpZAaDGtvxOaYj04xnhESqV1G9IC/g1l2sE7iUipEMvCdyIiIhKbWO8E/wtIBLq6\ne1N375g5Vfk0IAn4vwK851mEHoc0DliSa7sUIHOe8jmEFt96CHiJ0OJZ3TIHydkNB6YAtxOqMW4E\n9HH3lQWISaTActctiUhOypGD2717NytXrmTFihU5Fr7bsGEDAP/+979ZuHAha9asYfbs2fTu3TvP\nwndpaWmsXLmS1atX4+6sWrWKlStXsn17fg9PkNJC+SESnXJEghTrILg7cKO7L8l+0N0/AMZmtsfE\n3Zu5e3yE7bZs/Xa4+1/c/Uh3r+buvd09z7OD3T3d3a9194buXsXdu7j7e7HGIyIicqgsW7aME088\nkZNOOom9e/cyfvx42rdvH16QZcuWLQwdOpRjjz2W0aNHM2zYMJ5//vkc53j00Uc58cQTGTJkCGbG\nueeeS/v27ZkzZ86huCQREZFSL6aFscwsDRju7m/k03Y2MNXd6wYQX2C0MJaUdiWyYEOAOaCFsUQi\nS0mpz7p1wZUkJCfXY+3a1MDOLxI0LYwlQdPCWFHOXY7yo6gLYz0L/DVC2+XAtMIGJiKHznvvvUe/\nfv1o3LgxcXFx4SX9s8TFxREfH09cXFyO7corrzxoHxGJbN26NNwJbAtygC0iIlLWRfykamaXZG3A\nd8CJZvaFmU0ws79lfv0SaAd8X1IBi5QW5aFWZdeuXbRt25YHH3yQKlWq5GlPTU1ly5YtpKamkpqa\nypw5czAzLrzwwoP2ESkPOSISFOWHSHTKEQlStNWhJ+dzrDGQ39OhHwIeLZaIRKTE9O3bl759+wIw\nbNiwPO116+ascnjllVdo2bIlXbt2PWifb775JoCIRURERESKJtqcxWYF2PTAQjnsHG7Pr9u9ezcz\nZszgsssuK1IfOXwcbjkiUhDKD5HolCMSpIh3ggvw3F8ROQw899xz7Nu3j6FDh8bUZ8yYMSUYnYiI\niIhIbLR6jUghHW61KpMnT6Z///7Url27SH3k8HG45YhIQSg/RKJTjkiQNAgWkYNasWIFy5YtY+TI\nkUXqIyIiIiJyqGkQLFJIh1OtyuOPP07z5s3p3r17kfrI4eVwyhGRglJ+iESnHJEgaRAschjbvXs3\nK1euZMWKFWRkZLB+/XpWrlzJhg0bwn1+//13nn/+ef7yl79EPE8sfURERERESgMNgkUKqTzUqixb\ntowTTzyRk046ib179zJ+/Hjat2/P+PHjw31mzJjBnj17GD58eMTzxNJHDj/lIUdEgqL8EIlOOSJB\nivac4DzM7EjgZKA2MMfdt5lZJeAPd88IIkARCcYRCf+ZauTuOdqmTJnClClTchxr0KDBQc8ZSx8R\nERERkUPJcn/4zbeTmQH3AFcCCYADHd39UzObB7zv7rcHGmkxMzOP5dpFDhUzw18P8Pxng38X4Plb\nQFApZpZ34C5SlphZYPkROr9yRMo25YgETZ+zopy7HOVH6N8St9zHY50OfSMwCrgN6AxkP9Ec4Nwi\nRygiIiIiIiISsFgHwX8BbnP3O4BPc7V9DxxVrFGJlAGqVRGJTjkiEpnyQyQ65YgEKdZBcCPgwwht\nfwBViyccERERERERkeDEOgjeBLSJ0HYCsKZ4whEpO/T8OpHolCMikSk/RKJTjkiQYh0EvwjcYman\nZjvmZtYSuAZ4odgjE/n/7N17nI51/sfx12eQQw4hjPOwKYzNko46oMQoZUsS5dBBvw5bpGUlRpuK\nstlVUqmUDtRWWkrRrmZoUyJGDokcIxOychox8/39cd0z7rnnnpl7uO+55/B+Ph7Xw1zX93t97889\n7u/c1/e6vgcREREREZEwC7URPAb4DlgIZM5z9k/gW9/+uLBHJlLEaayKSN5UR0Ryp/ohkjfVEYmk\nkNYJds4dNrMOQB+gC95kWHuAR4E3nXPHIhahiIiIiIiISJiEtE5wSaR1gqWo0/p1eZRdgtavk9JJ\na6CK5E11RCJN11l5lF2C6sfJrhMsIiIiIiIiUuyF1Ag2s1PMLNHMvjOzQ2aWHrCpO7SUOhqrIpI3\n1RGR3Kl+iORNdUQiKaQxwcBTwD3Ax8D7wJGIRSQiIiIiIiISISGNCTaz7cBzzrnHIh9S4dCYYCnq\nNFYlj7JL0FgVKZ003lEkb6ojEmm6zsqj7BJUP052THBlYHF4QxIREREREREpXKE2gucAl0YyEJHi\nRmNVRPKmOiKSO9UPkbypjkgk5Tom2Mya+u0+A0w3swxgLvBLYH7n3MbwhyciIiIiIiISPrmOCfY1\neP0TM/tSBz3BOVcmvKFFlsYES1GnsSp5lF2CxqpI6aTxjiJ5Ux2RSNN1Vh5ll6D6kduY4Lxmhx4Y\nwXhERERERERECl2ujWDn3GuFGYhIcZOUlESHDh2iHYZIkaU6IpI71Q+RvKmOSCSFNDGE2g9CAAAg\nAElEQVSWmS0ws+a5pJ1pZgvCG5aIiIiIiIhI+IU6O3QHoGouaVWAy8ISjUgxknl3ctGiRVx77bU0\naNCAmJgYpk+fni3f6NGjadGiBZUrV6ZGjRpcccUVLF68OEdZMTExWVuZMsVqiL1IULqDL5I71Q+R\nvKmOSCSF2giGXCbEAn4HHAhDLCLF0oEDB/j973/PpEmTqFSpUo705s2b89xzz7Fq1Sr++9//0qRJ\nE7p27cquXbuy8pgZt956K6mpqezcuZOffvqpMN+CiIjICQnHjWB/CQkJxMQU5PJURKTgcv0rY2YD\nzWyhmS3EawC/mLnvt30NvAYsKqyARYqKzPXrEhISGDt2LNdddx1mOSafo0+fPnTs2JG4uDhatGjB\n008/zf79+1mxYkW2fJUqVaJWrVrUrl2b2rVrF8ZbEIkorfEokruSUj/CcSM404QJEyhbtmzQ71Ip\nfUpKHZGiKa/ZoTOAdN/PFrCfaQ8wBRgf/tBESp6jR4/ywgsvUK1aNf7whz9kS5s5cyYzZsygTp06\nJCQkRClCERGR0CUkJGR9Z/Xv3z9Hep8+fbLtP/3007z88susWLGCzp07Zx3/+uuveeaZZ1i2bJlu\nBItIxOX6JNg595pzrqNzriOQDPTN3PfbujrnHnDOpRZeyCJFQ0HGqnz00UdUqVKFChUq8I9//INP\nP/2UWrVqZaX37duXN998k6SkJEaPHs17770XgYhFCpfGc4nkrjTWj9xuBO/fv5++ffsydepUTj/9\n9ChGKEVJOOdemTp1Kp06daJ69erExMSwdevWwnobUkSFNOjC1+D9LtLBiJRUnTp1IiUlhcWLF9O1\na1duuOEGUlOP3zu6/fbb6dy5M/Hx8fTq1Yu3334bgBU/RCtiERGR8MjvRvBdd91Ft27duPLKK6MY\npRRV4ehyf+jQIbp06cIjjzyi7vYCFGxiLBHxU5CxKhUrVqRp06acd955TJ06lXLlyvHSSy/lmr9d\nu3YArN9xslGKRI/Gc4nkrjTVj7xuBL/++uukpKTw5JNPRjlKKWrCOffK/fffz/Dhw2nfvn1hhS9F\nnBrBIlGQkZHBkSNHck1fuXIlAHVrFFZEIiIikZHXjeAFCxawdu1aTj31VMqVK0e5cuWyzrv00mhF\nLMVVXnOviPjLa2IsEclD5liVgwcPsmHDBpxzZGRksHXrVlJSUqhRowannXYaTz75JN27d6du3brs\n2rWLZ599lu3bt9OrVy8ANm7cyJtvvkm3bt04/fTTWb16NQ8++CAA7VtG692JnLzSOOZRJFSluX74\n3wh+/PHH+fOf/5wtvVWrVgAEDP2UUqagc6/07t2bQ4cOUa9evRxd7kUCqREscpKWLl1Kx44ds7ro\nJCYmkpiYSP/+/Zk8eTKrV69m2rRp7Nmzh5o1a3LuueeyaNGirC/5U045hf/85z9MmjSJAwcO0LBh\nQ66++mrWrl2Lhq2IiEhRdrI3guvWrUvdunWDlh0XV4hvRIq1zC73u3fvZurUqdxwww18+eWX1KlT\nJ9qhSRFlzrloxxAVZuZK63uX8IitVYPU3Xsj+hpubuTKtm7g1kew/GYQqSpmBqq/RV9SUlKpftqV\nFzOLWP3wylcdKepKSv1ITk7OdiM4U+aN4L59+7JkyZJsN4JHjhyZNfdFMGXKlCEjI0N1pJQLVkeq\nVKnC5MmT6devX57nnnnmmfTv35+RI0dmO75s2TLOO+88Nm3aROPGjXWdlVvZJah+eN+3LsdjpZCf\nBJtZDHAe0AioEJjunFOnFSlVUnfvxU2KXPl2X+TKFhERORlxjWPZsvX4KgeBF8zTpk1j2rRp2Y5t\n376d7du388EHHxRKjFI4Fi1axIQJE1i2bBk7duzg1VdfzWqkHjt2jJEjR/LJJ5/www8/ULVqVTp2\n7Mi4ceNo2LBhVhlTp05lxowZLF++nH379rF58+aTiim/uVdEQmoEm1lL4APgd0CwDpoOUCNYRESy\nlISnXCKRUtzrx5atqRF/yiXFQ+YSRv3798/xhPbQoUOsWLGCUaNG0bp1a/bt28cDDzxAQkICK1eu\nJCYmJitfly5d6NGjB0OGDAHCN/cKQGpqKjt37mTdunU451i9ejUAe/dD9SqF8EuSIiek7tBmloT3\nBPjPwLdAjlsrzrkt4Q4uktQdWk6WmUX8SbC66eRSdgnqpiOlk7pDS3FnZsX2OwRURyIllO7Ka9eu\nJT4+nm+//Zb4+Phsaf7dlRs1agSEp8v9I488kmON4IyMDF4bCv0uD8c7z0nXWUVDbt2hQ10iqS3w\noHPuPefc9865LYFbeMMVCZ9FixZx7bXX0qBBA2JiYpgeMN3krFmz6Nq1K7Vr1yYmJoaFCxdmS9+y\nZQsxMTGUKVOGmJiYrA3gbwsK7W2IFDulaR1UkYJS/ZDSat++fZgZ1atXzzNfbK0amBkdOnTIegrs\nv02bNo1KlSoxa9Ystm/fTlpaWlZ3+3PPPRczy9rGjBmTowyIXANYir5QxwTvBn6LZCAikZJXNx3w\nutm0b9+eW265JWh6o0aN2LlzZ7Zj77//PnfddRc3aAk6ERERkZAcPXqUoUOHcs0111CvXr0882ru\nFYmkUBvBE4F7zOxj51x6JAMSCbeEhAQSEhIAr+tMoJtvvhmAPXv2BO36YWbUrl0727H33nsPgEY1\nwh2tSMlR3Mc8ikSS6oeUNunp6fTt25dff/2VDz/8MNrhSCmXayPYzP4acKg5sMbMPgV+CUhzzrnE\ncAcnUhRt3LiRBQvUD1pEREQkFOnp6fTu3ZvVq1eTnJycb1dokUjLa0zwwwFbY6AZcHeQtIcjG6ZI\n0fHSSy/leDIsIjlpzKNI7gqrfpzsvBjgLV/TqVMnqlevTkxMDFu3bi2U2KVkOHbsGL169WLVqlUk\nJSVRq1ataIckknsj2DkXU4CtTGEGLRIt6enpvPrqqwwYMCDaoYiIiOQrc16MSZMmUalSpRzpmfNi\nTJw4Mcfsu5kyl68JnF1XBLzPUEpKCitWrMi2hNG2bdtIT0+nZ8+eLFmyhBkzZuCcIzU1ldTUVNLS\n0rLKSE1NJSUlJecSRoei9a6kpAt1neBGwE/OuaNB0soC9Zxzui0oJd7s2bNJTU3ltttuY9y4cdEO\nR6RI05hHkdwVVv042XkxAO6//37AW75GJNDSpUuzLWGUmJhIYmIi/fv3JzExkdmzZ2NmnHPOOdnO\nmzZtWtaEpM8//3zWTRYz4+qrrwZgzirod17hvh8pHUKdGGsTcCGwJEhaa99xPQ2WEu+ll17isssu\n44wzzoh2KCIiIiJRE9cgli3bU7P2A2+iTJs2jWnTpgVNA++mTOCNmcB8agBLpITaCM6r70s5ICMM\nsYhExMGDB9mwYUPW+nCZ3XRq1KhBw4YN2bt3L1u3bmXv3r0ArF+/nmrVqhEbG0udOnWyytm6dSvz\n5s3jjTfeiNZbESlWkpKS9DRYJBeqH1LcbdmeivtT5Mq3ZyJXtkiuY4LN7DQza2pmTX2H6mfu+23x\nQH9gZ27liETb0qVLadOmDeeccw5paWkkJibStm1bEhO9Cc1nz55NmzZtuPzyyzEzBg0aRNu2bXnh\nhReylfPKK69QvXp1rrvuumi8DRERERERCYO8ngTfDyQCzre9m0s+8+UTKVIK0k0nWHrmmJZA5cuX\nD3OkIiWTnnKJ5E71Q0QkevJqBH8AbMZr5L4CjAV+CMhzBFjjnFsZkehEToK66YiIiEhpt2jRIiZM\nmMCyZcvYsWMHr776ataEVJnGjBnD1KlT2bt3L+effz6TJ0+mZcuWWemPP/44c+fOZcWKFRw+fJj0\n9PTCfhsiYZXXEkkpzrnXnHOvAgOBSb59/22mGsAiIhKM1gkWyV1h1Y+8lq8B2Lt3LykpKXz77beA\nNy9GSkoKqanHe1LlunzNvkJ5C3KS8lsma/z48UycOJHJkyezdOlSateuTefOnTl48GBWnt9++43r\nr7+ewYMHF2boIhGTayPYn6/BuzvSwUjpdODAAQYPHkxcXByVKlXi4osvZunSpVnpP//8MwMGDKB+\n/fqceuqpdOvWjQ0bNkQxYhERkeIhHPNiPP/887Rp04Zbbrkl+/I1C6LylqSAEhISGDt2LNddd13Q\ndZ7/8Y9/MGLECHr06EHLli157bXX2L9/P2+99VZWnjFjxjBkyBDatGlTmKGLREyu3aHNbAFwt3Pu\nO9/PeXHOucvDG5qUFrfddhurVq3i9ddfp379+rz++utcccUVrF27lrp163LttddStmxZZs+eTdWq\nVfnb3/6WlV6xYsVohy8iudCYR5HcRbp+xDWKZcu28M6LkWP5mj+GK1qJlk2bNrFz5046d+6cdaxC\nhQpceumlfPHFF9xxxx1RjE4kcvIaE+x/qygGb3KsUPKKhCwtLY3333+fWbNmcckllwDeF++cOXOY\nMmUKt9xyC1999RUrV66kVatWAEyZMoXY2FhmzJjBrbfeGs3wRUREiqQt21JxcyNXvnWLXNlSeHbu\n3ImZZVsSEqBOnTrs2LEjSlGJRF5eY4I7Oue+8/3cwbef61aQFzWz+mb2jJl9YWYHzSzDzBoFyVfe\nzJ4ysx1mdsiX/5Ig+czMRpjZJjM7bGYrzEzr2BQDx44dIz09PceMyxUrVuTzzz/nyJEjQPYZmc2M\n8uXL8/nnnxdqrCJSMBoTLJI71Q8RkegJaUxwBJwB9AR+ARaS+1PmV4DbgIeBq4CfgHlmdnZAvrHA\naGAS0BVYDPzTzLqGP3QJp8qVK3PhhRcyduxYduzYQUZGBm+88QaLFy/mp59+okWLFjRq1IiHHnqI\nvXv38ttvvzF+/Hh+/PFHfvrpp2iHLyIiInJS8psbxd+dd95JTEwMTz/9dFheOzY2FudctonQwJsM\nLTY2NiyvIVIUhdQINrOdZjbDzAaZWbOTfVHnXLJzrq5z7mpyWX/YzFoDNwGDnXOvOOc+A3oBW4G/\n+uWrBQwFnnDOTfSVfRfwGTDuZGOVyHvjjTeIiYmhQYMGVKhQgWeffZY+ffoQExNDmTJleP/99/nh\nhx+oWbMmlStXJjk5mW7duhETE617OCISipIwJjivi9Njx44xfPhwWrduTeXKlalXrx59+/bNmnVX\nJC8loX5IeNx22218+umnvP7666xatYrOnTtzxRVX5LjZ/+677/L1119Tv379sL12kyZNiI2N5dNP\nP806lpaWxqJFi2jfvn3YXkekqAm1FfEy0Bh4FvjOzLaZ2XQzG2hmjSMU2zXAb8A7mQecc+nATKCL\nmZXzHe4KlAPeDDj/DeD3EYxPwqRJkyZ89tlnHDx4kG3btvHll1/y22+/0bRpUwDatm3LN998w759\n+/jpp5+YO3cuu3fvzkoXEYmUvC5ODx06xIoVKxg1ahTLly9n9uzZbNu2jYSEBDIyMqIduogUA5lz\no4wfP55LLrmEpk2bkpiYyBlnnMGUKVOy8m3ZsoUhQ4YwY8YMypbNa0qfnPJbJmvw4MGMHz+eWbNm\nsWrVKgYMGECVKlW46aabssrYtm0bKSkpbNq0CYCUlBSv7KMn+xsQiY5Ql0ga6Zy7CKgOdAdmAC2B\nF4GNZvZDBGJrCWxyzqUFHF8NnILXpToz3xHnXGAMq/Em7GqJFAsVK1akTp067N27l3nz5tGjR49s\n6VWqVKFmzZqsX7+epUuX5kgXkaKlMMY8htKNcMyYMdSvX59KlSrRsWNH1qxZE1LZ+V2cVq1alXnz\n5tGzZ0+aNWtGu3bteOGFF1izZg1r166NxNuVEkRjggXynxsFID09nT59+jBq1CjOOuusAr9Gfstk\nDRs2jCFDhnDvvfdy3nnnkZqayvz58zn11FOzyhg9ejRt27Zl+PDhgPeAAmDZzyf0tkWirkC3kpxz\nB4G5ZrYR2AJcB3TEe0ocbjWAvUGO/+KXnvnv/0LIJ0XU/PnzycjIoHnz5qxfv55hw4bRsmVLBgwY\nAHjdf04//XQaN27MypUrGTx4MNdddx2XX65VuURKu/yWWBs/fjwTJ07ktdde48wzz+SRRx6hc+fO\nfP/999ku8IIJ5eI00L59+zAzqlevHrb3KCIll//cKPHx8cTGxvLWW2+xePFimjXzRiCOHj2a2rVr\nM2jQoAKVHVc/li07Ql8mK1NSUhLx8fEhvcal4euZLVKoQmoEm1lToBNeg7cjUAdYizfudrLv32Jn\nwIABxMXFAXDaaafxhz/8IWuMTuYdWu1Hfn/fvn0MHjyY3bt3c/rpp9OzZ0+6dOnCokWL6NChAz/9\n9BP33HMPe/fupX79+vTv359LLrmEpKSkfMvPlPSj92+HBuHdzyp/vS+9WXj3s8pf6Us/O7z7WeV/\n5Us/P7z7WeUn+dI7hGffO5b//7/2S/b+BRdcwPvvv89f//pX0tPTs57UvvXWWwwfPpzp06fzj3/8\ngxtvvJHTTjuNli1b8tprr1GzZk0SExOZMGFCvq934YUX8uCDDzJq1Ciuu+463nrrLb744gsaNPD9\nMfDL3759e4YOHcpFF13E999/T7169fIs//j5+NLDux8YX7T/v7RfuPuZ9P0RfP94+Um+9A5R27/3\n3nt58cUXadCgAWXKlKFZs2b06dOHZcuW8fe//50XX3yR7777Lit/WtrxTpJ5lb9lRyqfned7/zV9\n+feEb98+JmLXV1n7Ebq+ytqPUP3I2o9Q/cjaT/LtdwjvfqaiUD8Kur9ixQr+9z/v+ejmzZvJjQXe\nFQqaySwDOITXDfo/wGfOudS8zwqNmd2G1626iXNuq9/xmUBr51yLgPw34I0LbuWcW2tm44D7nHOV\nAvKdC3wFXOWc+zjI67pQ3rtEVuBdynBzf4pY0dgz4CZFsPz7iPgaj259/vlOuPxmEKkqZpbzjraU\nPgcOHMjqkty5c+es45dccgnlypXj5Zdf5ne/+x1ff/0155xzTlb61VdfTa1atYI+AQm0adMmbr31\nVpKTkylbtixt27blzDPPZNmyZaxevTorX3p6OjfddBNr165l4cKF+T4JNrOI1Q+vfNWR0s7M9B2S\nV/lFsI4cPnyYX3/9lTp16tC7d28OHjxIu3btePTRRzGzrHzp6enExMRQr149tm7dmmt5ZoZLiFy8\n9rGus/IsvxjXkaJYP06U933rLPB4qN2hlwOt8bo/1wRqmdl/nHOhDaw6MauBHmZWIWBccDzehFkb\n/PKVN7OmzrmNAfkcEMkY5SRt2ZEasT/QluPWh4gUpiS/p/WRkF83wp07d2Jm1KlTJ9t5derUYceO\nHSG9RubEfYEXp/4T86Wnp9O7d29Wr15NcnKyukJLSCJdP6T4qVixIhUrVsyaG2XChAlce+213HDD\nDdnyXXnllfTp04c77rgjSpGKFH8xoWRyzp0DnA7cCmwD7gC+9S2dNNPMIlEL5+BNgJVV882sDN4y\nSfOcc5nz0X0CHAP6Bpx/M7DKObclArGJiEgRkNcSaxC+O9m5Tdx37NgxevXqxapVq0hKSqJWrVph\neT0RKT3mz5/PJ598wubNm/n000/p1KlT1twop59+Oi1btsy2lStXjtjY2KwxwyJScCFPjOWc+x/w\nL9+GmV2It15vL7yG6tSCvLCZXe/7sR3eLM7dzGwXsMs5t9A5t8LM3gb+bmanAJuAu4E4vPWDM+Pa\nZWZPAyPM7ADwDdAb6IA3k7WIiERBYTzlyutJbWxsLACpqanZxvCmpqZmpeUnr4n70tPT6dmzJ8uW\nLWPOnDk450hN9YZ3VKtWjQoVKoT/DUuJoafAkmnfvn2MGDGC7du3U6NGDXr27MnYsWMpU6ZM0Pz+\nXaNF5MSE3Ag2swZ4k2J18v3bEMgAvubEJsb6J153ZXz/Tvb9nOx7DYABwGPAo8BpQArQxTmXElDW\nQ8B+4D4gFlgH3BBsLLCIiJQ8wboRNmnShNjYWD799NOsMcFpaWksWrSIv/3tbyGVm9fF6ZYtW5gz\nZw5AtjHH4M262q9fv/C+SREpceLqxrJl5/G5UXbs2MGkSZOYNCnvwbBDhw5l6NChkQ5PpMQKdXbo\n9UDmAKgU4D28hm+yc27/ibywcy7frtjOuSPAg74tr3wOeNy3iYhIEVAYYx7zW2Jt8ODBPPHEE5x1\n1lk0a9aMsWPHUqVKFW666aY8y41rHMuWrdkn7Qv14hSgf//+9O/f/4Tfl5R8GhMsAFt2puLOiEzZ\ntiH/PCKlVahPgj/Ga/QmOeeCrd0rIiJS6PLrRjhs2DDS0tK499572bt3L+effz7z58/Pd43gLVtT\nIz6rpxQPTZo0YcuWnNOLXHXVVcyZM4eMjAwSExN58803+emnn6hbty59+/blkUceyRqbLiIiRUtI\njWDn3H2RDkREREqWSD/limsUy5ZtBetGmJSURHx8fETjkpJl6dKlpKenZ+3v2LGDc845hxtvvBGA\ncePGMWXKFKZPn06rVq1YuXIl/fr1o0KFCowcOTLXcjPrR36N7PzSRUSk4HJtBJtZXefcTwUt0Mxi\nnXM7Ty4sERGRvG3ZlhqxNR6tW2TKleKnZs2a2fanTp1KtWrVspatWbx4Md27d6dbN+9D06hRI665\n5hq++uqrkMrPr5GdX7qIiBRcXv10NpjZP8yseX6FmFlFM+tjZiuA28MXnoiIFFdJSUnRDkEk7F55\n5RVuueUWypcvD8DFF1/MZ599xrp16wBYs2YNCxYs4KqrrsqznMz6UbNmTWrXrp21ffTRR9ka2fml\ni4hIweXVHfpS4ElgtZmtBBbhTYq1CzgCVMebLOs8vNmcM3z5n45kwCIiIiLRMH/+fDZv3swdd9yR\ndWz48OHs37+fli1bUqZMGdLT0xk5ciR33nnnCb1GYCO7oOkiIpK/XBvBzrllwOVmdg7e092rgXsD\nsqUBXwHDgDdPdKZoEREpeTTzrZQ0U6dO5dxzz6VVq1ZZx2bOnMnrr7/OzJkzadmyJStWrOC+++6j\nSZMmDBw4MNeygtWPYI3sgqSLiEho8p0Yy9cYXgZgZrWBekAFYA+w2Tl3NKIRioiIiETZrl27mD17\nNlOmTMl2fNiwYQwbNiyre3J8fDybN2/miSeeyLMRHEywRnZB0kVEJDQFmrvfOfezc26Fc+5L59x6\nNYBLj507dzJgwABq165NxYoVadWqFYsWLcpKP3jwIH/6059o2LAhlSpVonnz5vz973+PYsQiEm0a\nEywlybRp06hQoQK9e/fOdvzQoUM5lkKKiYkhIyMjz/IC60dmI3vQoEFB8+eXLiIioQt1nWApxfbt\n20f79u259NJL+fjjjzn99NPZuHEjtWvXzsozZMgQFixYwJtvvklcXBwLFy7k9ttvp1atWvTt2zeK\n0YuIiJy8l19+mZtuuolKlSplO969e3fGjRtHXFwc8fHxfPPNN0ycOJEBAwYUqPzcGtmhpouISOjU\nCJZ8jR8/nnr16jFt2rSsY40bN86WZ/Hixdxyyy1ceumlANx888289NJLfPXVV2oEi5RSGhMsJUVS\nUhIbNmzgrbfeypH27LPPMmrUKO655x5+/vln6taty5133smoUaPyLDOwfuTWyA41XUREQleg7tBS\nOv3rX//i/PPPp3fv3tSpU4c2bdowefLkbHkuvvhi5syZw48//gjAF198QUpKCgkJCdEIWURE5KTF\nNYjFzOjYsSMZGRm0a9cOM8u2Va5cmYkTJ7J582YOHTrEDz/8wNixYylfvnyOvP5bXIPYrNfJbGTn\nNuFVfukiIlIwehIs+dq4cSPPPfccQ4YMYcSIEaxYsYJ7770XM+Puu+8GYNKkSdx55500atSIsmXL\nYmY888wzagSLlGJJSUl6GizF2pbtqbg/RabsUyanYmbZjrVr1y7Pc/JLFxGR0KgRLPnKyMjgvPPO\n47HHHgOgdevWfP/990yePDlbI3jx4sV8+OGHNGrUiIULFzJ06FDi4uK48soroxm+iIiUcDt37uQv\nf/kLc+fOZf/+/fzud79jypQpXHLJJRw7doyRI0fyySef8MMPP1C1alU6duzIuHHjaNiwYdRiPpoB\nblLkyrf7Ile2iEhxF1J3aDOLMbOyAce6mNlQM2sTmdCkqKhbty4tWrTIdqxFixZs3boVgLS0NB56\n6CGeeuopunXrRqtWrbj77rvp3bs3EyZMiEbIIlIE6CmwFIbMyRvNjI8//pjvvvuOZ555JmvyxkOH\nDrFixQpGjRrF8uXLmT17Ntu2bSMhISHfGZxFRKRkCvVJ8AzgCNAPwMz+D3jOl3bUzK5yzv07AvFJ\nEdC+fXvWrVuX7di6deuyJsc6evQoR48ezbFERJkyZXSBISIiEZXf5I1Vq1Zl3rx52c554YUXiI+P\nZ+3atcTHxxdarCIiUjSEOjHWBcBcv/0/Ay8B1YD3gZFhjkuKkCFDhvDll1/y+OOP88MPP/DPf/6T\nZ555hnvvvReAKlWqcNlll/GXv/yF5ORkNm/ezKuvvsr06dO57rrrohy9iERLUlISjzzyCDExMdm2\nevXqZeXRGuNyskKZvDHQvn37MDOqV69eSFGKiEhREuqT4NrAdgAzOwNoAjzrnNtvZtOAnGsGSInR\nrl07PvjgA0aMGMHYsWNp1KgRjz32GP/3f/+Xleftt99mxIgR3Hzzzfzyyy80btyYxx57LGvMsIiU\nXs2bNyc5ORnnHOD1EsmkNcblZIUyeaO/o0ePMnToUK655ppsN2RERKT0CLUR/CtQ0/dzB2C3c26l\nbz8dqBDmuKSIiKsby5adqdmOrVu3jnvvvTfrSXAwa9eu5YEHHuCBBx6IdIgiUkR16NCB5ORkypYt\nS61atYLm0RrjcrJCmbwxU3p6On379uXXX3/lww8/jEa4IiJSBITaCP4C+IuZHQMGk71r9BnAj+EO\nTIqGLTtTcWdErnzbELmyRaRo2LhxI/Xr16d8+fKcf/75PP744zRp0gQ4vsb4bbfdRoMGDbLWGB8+\nfHiUo5biIrfJGydNyj71cnp6Or1792b16tUkJyerK7SISCkW6pjgYXhPgmfjPdLG5wMAACAASURB\nVPUd45d2I7A4vGGJiEhxl5SUxAUXXMCrr77KvHnzeOmll9i5cycXXXQRe/fuBbzl1c4++2waNWrE\nKaecQseOHRk/frzWGJeQ5Td5I8CxY8fo1asXq1atIikpKdeeCSIiUjqE1Ah2zq13zjUDajnnznDO\nbfZLvh+vkSwiIpJNly5d6NmzJ61ataJTp058+OGHZGRk8NprrwHZ1xj/5ptvmDhxIkOHDmX+/PlR\njlzCJb/J0WbNmkXXrl2pXbs2MTExLFy4sEDl5zd5Y3p6Oj179mTJkiXMmDED5xypqamkpqaSlpYW\n1vcqIiLFQ67doc1sHfAB8IFzbjGAc25PYD7n3LeRC09ERIqrYOsEn3rqqcTHx7N+/fqsNcbfe+89\nunXrBkCrVq1Yvnw5EyZM4MorryzkiCVS8poc7eDBg7Rv355bbrmFfv36FajcuPqxbNnhzVsxcuRI\nRo48vljFXXfdxV133ZUtf5s2bU70LYiISAmS15jgacC1wINm9jMwB5gF/Mc591thBCciIiVLWloa\n3333HZdffrnWGC9F8poc7eabbwZgz549WY3kUG3ZkYqLYM95+zhyZYuISPTk2h3aOTfOOXchUB94\nBGiI1wjeZWbvmNlNZla1kOIUEZFiJikpiT//+c8sXLiQzZs389VXX9GzZ08OHTpE//79tcZ4KZI5\nOVrTpk256aab2LRpU7RDEhGRUizfMcHOuZ3OueedcwlALeBOvGWRpuA1iOeb2V1mpsX2RESKuSee\neIKYmBjuu+++bMe///57rr/+eqpXr86pp55Ku3btckxGFKj3DdcxYcIELrvsMpo0acIFF1zARx99\nxL59+2jYsCFmRlJSEt9++y0dOnSgSZMmDBw4kMOHD3PPPfdgZnluUjzkNzmaiIhIYQt1iSQAnHP7\ngZnATDMrB1yO12X6YeBZoEwep4uISBH25ZdfMnXqVFq3bp3t+ObNm7n44osZMGAAo0ePplq1anz3\n3XdUrlw5z/JSd+/FTcozy0mx+/LPI9HXpUuXbPvnn38+TZs25bXXXmPw4MFRikpEREqzAjWC/Tnn\njgKf+La7zOz8sEUlIiKFat++fdx8881MmzaNMWPGZEsbOXIkXbp04cknn8w6FhcXV7gBSonhPzma\niIhINIS0RJKZXWRmV/vt1zSzGWb2rZlNMLMyzrmvIhemiIhAzu7Kx44dY/jw4bRu3ZrKlStTr149\n+vbty7Zt2wpU7qBBg+jVqxeXXXZZtuPOOebMmUPLli1JSEigdu3anHfeebzzzjthe09SumROjla3\nbt1ohyIiIqVUSI1gYBxwjt/+U0A34HvgLuChMMclIiIBgnVXPnToECtWrGDUqFEsX76c2bNns23b\nNhISEkKeYXnq1Kls3LiRsWPH5kj7+eefOXDgAI8//jhdu3bl3//+NzfddBN9+/bl4481da7kL6/J\n0QD27t1LSkoK337rrbi4fv16UlJSSE1NjWbYIiJSgoXaHboFMB7ANxa4JzDYOfeKmQ3Gmyzr0ciE\nKCIiuXVXrlq1KvPmzcuW94UXXiA+Pp61a9cSHx+fZ7nff/89I0eO5L///W+OpYqArIZ0jx49uP/+\n+wE4++yzWbp0Kc8++ywJCRFcn0ZKhB9//JE+ffqwe/duatWqxQUXXMCXX35Jw4YNAZg9ezYDBw7M\nmvBs0KBBACQmJjJ69Ohohi4iIiVUqI3gysCvvp/PA04FPvTtfwM0CnNcIiLiJ7fuysHs27cPM6N6\n9er55l28eDF79uyhZcuWWcfS09NZuHAhzz//PAcOHKBs2bK0aNEi23ktWrTg7bffLvgbkVIlrm4s\nW3Yef6L7448/8u677/Luu+/myBu4RnBiYiKJiYkRj1FEREqfUBvB24HWwCIgAVjlnPvZl1YdOBSB\n2EREhOPdlWfMmJFv3qNHjzJ06FCuueYa6tXLf+W6P/7xj5x77rnZjg0YMIAzzzyTkSNHcsopp3Du\nuefmWA7p+++/p3HjxgV7I1LqbNmZijsjcuXbhsiVLSIiJVeojeAZwONm1gFvLLD/rdm2gKZ4FBGJ\ngPy6K/tLT0+nb9++/Prrr3z44Yd55s1UtWrVbE+BwZu9t0aNGllPf4cNG8aNN97IxRdfTKdOnViw\nYAFvv/02//rXv07sTYmIiIhEUaiN4DFAGnAB3iRZE/3SWgP/DG9YIiIC+XdXPnjwIOXKlSM9PZ3e\nvXuzevVqkpOTQ+oKnRszy7Z/7bXX8uKLL/LYY48xePBgmjVrxuuvv07Xrl1P+DVEREREoiWkRrBz\nLh14LJe0HmGNSEREsuTXXblcuXIcO3aMG2+8kTVr1pCcnEytWrVCKjuuQSxbtgefgfezzz7jmWee\nCZqWkpLCjTfeyI033liwNyMiIiJSBIT6JBgAMzsbuBSoCbzgnNtpZmcAqc65/ZEIUESkNMuvu3J6\nejo9e/Zk2bJlzJkzB+dc1tIy1apVo0KFCrmWvWV7Ku5PkYvdgrehRURERKIqpHWCzay8mf0TWA5M\nAkYDmTOuPAmMjEx4IiJF23PPPUfr1q2pVq0a1apV46KLLmLu3LlZ6bNmzaJr167Url2bmJgYFi5c\neNKv6d9d+ccff2TOnDns2LGDc845h3r16mVt77zzzkm/loiIiEhJE1IjGK8r9BXALUAdwH/A2MdA\nlzDHJSJSLDRs2JAnn3yS5cuXs2zZMjp16kSPHj1YtWoVAAcPHqR9+/ZMnDgxx1jb/MTVj81aO9V/\ny+yqbGbExcWRkZERdOvfv3/Q8zM3ERERkdIo1O7QNwEPO+feMrMyAWmbgLiwRiUiUkx079492/7Y\nsWOZMmUKixcvplWrVtx8880A7NmzJ8c6qPnZsiMVlxC2UHOwjyNXtoiIiEhRFWojuCawNpe0GKB8\neMIRESm+MjIyeOeddzh48CAXXXRRtMMRERERkSBCbQRvAi4EFgRJOw9YF7aIRESKmVWrVnHhhReS\nlpZGlSpVmDVrFvHx8dEOS0RERESCCHVM8HTgL2bWFyjnO+bMrCMwBHglEsGJiBQHzZs3JyUlhSVL\nlnDXXXfRr18/1qxZE+2wRERERCSIUJ8EPwm0Bl4HXvId+xyoAMx0zmkhDBEptcqWLUvTpk0BaNOm\nDUuWLGHixIlMnTo1ypGJiIiISKCQGsHOuXSgt5lNxpsJujawB/jEOZccwfhERIqdjIwMjhw5Eu0w\nRERERCSIUJ8EA+CcWwQsilAsIiLFzogRI7jqqqto2LAh+/fv58033yQ5OTlrreC9e/eydetW9u7d\nC8D69eupVq0asbGx1KlTJ5qhi4iIiJRKBWoEi4jIcXF1Y9myM5Vx48blSOvSJfjy6bfffnukwxIR\nERGRPITUCDazDCC3BS4dsA/4BnjKOTc/TLGJiBRpW3am4s6IXPm2IXJli4iIiJRWoc4O/SiwDdgF\nvAqMB17z7f+IN2FWLeBjM7s6/GGKiIiIiBS+RYsWce2119KgQQNiYmKYPn16tvSBAwcSExOTbdNa\n8SJFW6jdodPw1gpOcM6lZR40s4rAx3iN4bbAR8BDwIdhjlNEREREpNAdOHCA3//+9/Tv359+/foF\nzdO5c2feeOMNnPM6Tp5yyimFGaKIFFCojeD/A/7k3wAGcM4dNrOJwLPOucfM7CW8J8QiIiIiIsVe\nQkICCQkJAPTv3z9onvLly1OrVq3CDEtETkKo3aFrAeVySTsFqOn7eTdgJxuUiIiIiEhx8fnnn1On\nTh3OOussBg0axK5du6IdkojkIdRG8DJgjJnV9T9oZvWARGCp71BjYEf4whMRERERKboSEhKYPn06\nCxYs4Omnn2bJkiVcfvnlHD16NNqhiUguQu0OfT/wH2CjmX0J/AzUBi4EDgE3+/KdAbwV7iBFRERE\nRIqiXr16Zf0cHx9P27Ztady4MR999BE9evSIYmQikpuQngQ7577Ba+BOBDKA3/v+/RvQzDm3wpdv\ntHMuMUKxSi4yMjIYNWoUTZs2pWLFijRt2pRRo0aRkZER7dBERKSI03eISHjVrVuXBg0asH79+miH\nIiK5CPVJMM65PXgzP0sRM27cOKZMmcL06dNp1aoVK1eupF+/flSoUIGRI0dGOzwRESnC9B0iEl67\nd+9m+/bt1K1bN//MIhIVITeCpehavHgx3bt3p1u3bgA0atSIa665hq+++irKkYmISFGn7xCRvB08\neJANGzbgnCMjI4OtW7eSkpJCjRo1qFGjBmPGjOH666+nbt26bNq0iYceeojY2Fj++Mc/Rjt0EclF\nqBNjYWbxZjbRzOaa2YKA7T+RDFLydvHFF/PZZ5+xbt06ANasWcOCBQu46qqrohyZiIgUdfoOEcnb\n0qVLadOmDeeccw5paWkkJibStm1bEhMTKVOmDN9++y09evTgrLPOYuDAgbRo0YIvvviCU089Ndqh\ni0guQnoSbGbnA8nAZqAZsBKoDjQCfgQ2RCg+CcHw4cPZv38/LVu2pEyZMqSnpzNy5EjuvPPOaIcm\nUiQ899xzvPDCC2zevBnwJi55+OGHs558iZRm+g4RyV1cbCxbUlMBcM5lS5s2bRrTpk3Ldmzz5s28\n8sorvPLKK4UWo4gUXKhPgh8H3gfi8dYBvs05FwdcAZQBxkYkOgnJzJkzef3115k5cybLly9n+vTp\nTJ48OccfZpHSqmHDhjz55JMsX76cZcuW0alTJ3r06MGqVauiHZpI1Ok7RCR3W1JTcRCxTUSiI9Qx\nwWcD/TleX8sAOOcWmNlY4Ang/PCHJ6EYNmwYw4YN44YbbgC8p1ybN2/miSeeYODAgVGOTiT6unfv\nnm1/7NixTJkyhcWLF9OqVasoRSVSNOg7RERESptQG8GnAAedcxlm9gvgP93dOkBXkVF06NAhYmKy\nP9SPiYnR8hYiQWRkZPDOO+9w8OBBLrroomiHIxJ1+g4REZHSJtRG8Aagvu/nlcCtZvahb38gsDPc\ngRWUmTUA/o7XRduAfwODnXPbohpYIejevTvjxo0jLi6O+Ph4vvnmGyZOnMiAAQOiHZpIkbFq1Sou\nvPBC0tLSqFKlCrNmzSI+Pj7aYYlEnb5DRESktAm1ETwH6AC8hTc++CPgVyAdqAzcF4ngQmVmFYHP\ngMPALb7DjwELzOxs59zhqAUXYf4TNgTO5PnUU0/x1FNPRSMskSKnefPmpKSksG/fPt5991369etH\ncnIyLVu2jHZoIlGj7xARESmNQmoEO+fG+P38bzO7AOgJVAQ+cc7Nj0x4IRsExAFnOuc2AZjZt8B6\n4E68J8QlUuaEDZFiESxbpDCVLVuWpk2bAtCmTRuWLFnCxIkTmTp1apQjE4kefYeIiEhpFOqT4Gyc\nc8uB5WGO5WR0B77MbAADOOc2m9l/gWspwY1gETkxGRkZHDlyJNphiIiIiEghO6FGsJnlWFrJORfN\nGTTigQ+CHF+N98RaREqxESNGcNVVV9GwYUP279/Pm2++SXJyMnPnzo12aCIiIiJSyEJqBPvG3CYC\nNwANgpznQi0rQmoAe4Mc/wWoXsixiEgR4T/ecdy4cTnSu3TpUtghiYiIiEiUhdpwfQ7oizdB1kzg\nt4hFJCISJhrvKCIiIiKBzLn8LxHNbA/wiHNuUuRDKjgz2wnMcs7dFXB8MtDTOVcnyDmRvDYWERER\nERGRKHPO5XhuEeqT4CPA2vCGE1ar8cYFB2oJrAl2QrBfhoiIiIiIiJRsOSa4ysWrQO8IxnGyZgMX\nmFlc5gHfz+2Bf0UlIhERERERESlyQu0OXQaYgrcW7zyCTELlnHsl3MGFyswqASuAw8Ao3+G/AqcC\nrZ1zh6IVm4iIiIiIiBQdoTaCz8N72lo7lyzOOVcmnIEVlJk1ACYCnfHmq/k3MMQ5tzWacYmIiIiI\niEjREWp36OeBPcC1wFlAk4CtaUSiKwDn3I/OuRucc6c556o5565XA7j0MbOpZpZhZn8LkjbGzCK2\nnrWZJfpee72v94R/2u98af1OoNz+ZjYwfJFKSWJmF5rZDDPbZmZHzGyfmS0xs7+aWWyUYxvg+9w3\nOoFzN/vOHRMkbeyJ1GUzq+arp38o6LkSWWbWw8ySzSzVzA75/v9nmVmpXMfMzJJ8n/9Xg6TdfhL1\nKtHMOoSYt76ZPWNmX5jZwYK+pnlGmNkmMztsZivM7LqCxiyqH4FKSP14wMxmm9kO37mjCxqvnJxQ\nG8HNgT875+Y459Y757YEbpEMUiQUZlYBby1rB/Qxs8DPt/NtkdYUuC2M5Q0A1AiWHMxsKPA5cDow\nErgcuBH4BLgDeDl60QEnV+cyzx1sZjXCVO5peGvetz3BmCQCzOw+4H1gHXAr0A14FO//uGMUQ4um\nzM94XzNrnkvaiUgEOoWY9wygJ/ALsPAEXnMsMBqYBHQFFgP/NLOuBSynVFP9CKok1I/bgVrArBM4\nV8Ig1Nmh1+GNrxUpyv4IVAU+wvuS6ArMjUIc84GHzexV55zW1JaIMLOOwJPAROfcgwHJn5jZE3g3\nhYqzhcD5wAjgz2EoT6sCFE1Dgfedc4P8jiUR/Zs40bYCqIfX4Cn0uuycSwbqApjZbcCVoZ5rZrXw\n/l8fd85N9B1ONrNmwDi8G3USGtWP4Ipt/fCd39J3bhngrnyySwSE+iT4L3gX9Y0jGYzISeqPd0du\nAJDm28+TmZXxdddaa2ZpZrbdzCaYWXm/PH/1dTM9x+9YJTNbZ2b/DXji7PCeyNUH7gnh9Vv7usP8\n4uvi9LmZXeyX/hlwGdDe110mw8wW5PubkNJgOLAL7+9zDs65w8656Zn7ZhZrZtPNbJfvs55iZn0D\nzzOz88zs32a238wO+H4+N0i+wX7dHL80r1v2JjPLd5JEMxvk6xp52BfPS2ZWPUjWbXiTMt5tZnVP\nplzf99dGvDr6kq8updsJDFGQsKsBpIaS0cwuN7Nlvv/j9b7/81fNbJNfnst8/7+XBpwbtHt+KJ/H\nEL8rNvn9nfbf0gPKus7MFvu6UO41s3fMrGGQt3sQeBy43szahPC7ybNc84YQOLzruczPf6S6YHYF\nygFvBhx/A/i9ricLRPWj5NUPKQJCbQQ/jDcp1vdm9q2ZLQzYkiMYo0i+fBfIlwMznXN7gA+A7mZW\nLZ9T3wQewvti7ob3B/U2336mR4ClwFvmzUQO8BxenbjJORc4NnEF8E/gL2aWaw8KM2sL/Bevi+bt\nwHV4Y+//7fcH/S5gObAS74nYBcDd+bwnKeHMu3N8KfCpc+5YCPkr4T1V7YLXaL4W7zP1upnd7pfv\nbLwnDNWAfsAteL0rks3s9375bgeexuv1cA3eMnpv+c7LL5ZxwLO+c7sDD+LrtWFmwZ7UPg6kc3zm\n/xMt9ye8OmbAY3h16UK8niMSXUuAAWb2oHlPCoMysxZ4/18HgV54f7sH43VfDOxOGKx7YY5ukgX4\nPIbyXdED73OVuXXGu1G11u/1/g94F1gFXA8MAloBSbl8XzwPbMX7zOYqxHIvwPv8T+P45/+lvMo9\nCS2BI865HwKOr/bF0DJCr1sSqX6UvPohRYFzLt8N76Los7y2UMrRpi1SGzAM70L5PN/+lUAGMMgv\nTyKQ7rd/iS9P34Cy+vjKOtvvWGO8pcGm4a2ZnQH0Cjgv0XdeDNAMOAo87Ev7ne+cfn75/4P3B7mM\n3zED1uB1fco89hmwMNq/Y21FZ8O7AZMBPBYkrYz/5jt2r++zeUlA3k+BnRxfKeBdvN4UVfzyVMG7\nOfOub9/wLjrmBJT1R19Mr/gd6+973Ua+/cbAMWBkwLkX+s69xu/YJmC67+e/4vXuaOLbfzSgLodU\nri9fBnBrtP8PtWX7f2qGd/Mw3ff/swvvpkrngHxvAj8DFfyONQCOABv9jl3mK+vSgPNP6PNIAb4r\n/NIMb1WNVKCx79ipwP+AqQF5G/vew31+x7L+7uONA00H2vv2bwt4HwUpNwP46wn8H2V7zRDyvwDs\nCHI887uwb0FjKK2b6kfJqx8B55bxve7oaH/WStsW0pNg51wH51zHvLZQyhGJoH7A9865Jb79fwM7\nyLtLdBe8P4Dv+brylPE9YfsU7w90Vlch503+dqevvGnAq865d3Ir2Dm3HngNGGpmpwWmmzeJ16V4\njQ78XruML/ZLA88RyY+Z1cG7+fJb5r/mdde/BNjunFsUcMobeBNzZD6VuQT40Dm3PzOD7+fZeBdO\n4F1UNcD32fXzL7wLprxkLmH3VkCd+xrYT+6f+wnAAbxeGeEsV4oA39/LNnifsbF4vV96APPMbKRf\n1guAuc65NL9zf8TrUXMiQv3cdCXE7wo/TwJXANe645OHXoh3Uynw9bYD3+VSDng9LdbjPV0L5kTL\nlWJA9UP1QyIj1O7QIkWWmbXDu4ifZd4SKNXwunC+D1xgZmfkcmptoDxwCK/BkLml4nUJqhmQfy7e\nE7FTgL+HENojQAW8sZuBauA1eEcFvPZveE/tcjScRfzswXsyGrgcw26gHXAuMNXveA287sCBdvql\n55cvcwxY5tjcn/0zOG9YwO584q6Nd1H0Azk/95XJWecyy/4V76LpJl+Xv7CUK0WH83zunBvtnLsS\nb5b9b4HRfsNa6hJ8bGRI4yWDCPVzU4sCfFeYN0nOEGCgc+7LIK/3nyCv1yqwnEy+ujUauNiCL4lz\nQuVG2F6Cf49l/q35pRBjKfZUP0pc/ZAiINfZoc2sQGv/Ouc2nnw4Iick82nvcLJPEpQ5tqUf3h/I\nQHuAw8DFBJ81dkfA/nN4N45+AF40s4tczvHAx1/cuW1m9gJeo/bDgOT/4XV/eRbvibFmrZWQOefS\nzWwh0NnMyjrfuGDnXDrwDYCZ+X9+fwHODFJU5jrCe/zyBVtbOBbvohaON5Jr+2fwPXE+PZ/Q9+DV\ny854dSBYem6ewRvf9hjeuMJwlStFkHNup5m9hHfDsRnevAw/AXWCZA88lob3N/WUgOOBF7uhfm5C\n/q4ws8vwvisecc69nUt5/fCGvQTaH+QYAM65d8zsL3hPAl8IV7kRtBoob2ZNA64P4/F+58HilBCp\nfmRXDOuHFAF5LZG0gYKtW1XmJGMRKTAzK4c3RvdLgs+S+3e8yX2CNYI/wRtLfJpz7rN8XqcP0Bdv\nGv7NeOsdPoo3E3ReHsMbK/IwfvXJOXfIzBYBrZ1zy/Mp4wi6Uyk5PYk3WcmTwANB0v0vRpKBnmZ2\noXNusd/xvnhPdNf65etmZqc65w4CmFkVvAlRMmcl/9G33YB3AyfTH8l/2b1P8W7+NHbOFWiWc+fc\nYTMbi3fjqFJAcqjlHvH9W7Egry2RZWYNfN02A2U+9c/ssbAY7/NZ0Tl32HduQ6A9XtfGTJndK1vh\nDS/JdHVA+aF+bkL6rvD1OnoPeNs592iQLF/gXXA3c869ESQ9Pw/j3VC9/iTK/Y3C+fx/gjc8oi/e\nd2Wmm4FVfl1gJR+qHyErTvVDioC8LlgGFloUIifuarwG4hDn3MLARN+T2OfMrENgmnMu2cxmAu+a\n2US8GRgzgCZAAjDMObfBzJrg3bl8yTn3vq/ckcA4M/vUOZeUW3DOuV1m9g+8WRMDbyo9gDfr7ny8\n9f5+wnuS1haIcc495Mu3BrjLzHrhPYXe75z7PoTfjZRgzrkFZjYCeMI3q/N0vMmkKuA99b0Rbxyt\nwxszdT/wvpk9jNeIvRlvRvVBzrnMz+ajwFXAAjMb7zs2HO+i4FHf6zozewSYamZT8WZC/50vX2YP\nh9xi3mhmTwLPmllzvEZ3ZrfuK/AmLslrtYGpeLOTXkn2m0qhlpuK91Sgt5l9izeL6ibnnLpmRtcq\n85aDm4X3Ga6K9zm8E++CObMBMBbv5sunZvYUXhfMRI43AoCsp2TJwAgz24N3o+dmvL/t/vlC+tyE\n+l2BdwF+EG8JrvMDXusr59x+M/uz7/VqAx8D+/CW1LsMb5LRmbn9kpxzc83sv3jzWfh//gtS7hrg\nKjObh9e7Y4dzLtgQCADMLLNB0Q7vxlo3M9sF7PL/zjWzY8A059wdvph2mdnTvv+DA3g9VHoDHfBu\nqknoVD9KWP3wHTsHiOP4Q8SWfuV95D+2WyLEFYHZubRpO9EN70thL36zIQakV8VrCLxCwOzQfnn+\nhDfRxCFfWcuBcXgTKZTBu4u4BqgYcN48vFlyq/v2E/HufMcE5KuGN1byGH6zQ/vSzsKb5XEnXnei\nrXjLO3X1y1MH78tjH97sgwui/XvXVnQ2vEk/ZuKtqZuG1xD9yvd5rOOXrw7ek9uffZ+1FXhLfAWW\ndy7eE+Zf8e6ezwfOCZLvPrwLskN4Fz3t8bpT/80vT7bZRv2O9+X43flf8bpOTgLq+eXZCLwW5HX7\n+co8FiQtlHKvwZuV/YivnH7Bfq/aCvUzPMj3dy/z87QfWAYMBcoG5O3kSzuM12PtDrzJCjcG5KuH\nN1nbL3jdMcdyfBbZAn8effly/a7wpafntgWU0xVvfOL/8L6f1uEtxdLcL89nQHKQ39WlmZ//IO8j\nlHIvxJvY6JCvnDxnpMVrzAR7TwsC8qUDLwccM7wbwJs4/jfnj9H+vBW3TfWjxNaPaXn8Tgo8y7S2\ngm+Zy2KIiIicMN8EdUuAm51zb0U7Hik9zGwacJlzrkBzmYiUBqofIsHlN35LREQkGzOLA+4BFuE9\nGWgJjMDrrv9+1AITERERCYEawSIiUlCH8SZVuQVv6aS9eJOojHAaxyTRoW5tIrlT/RAJoO7QIiIi\nIiIiUmrERDsAERERERERkcKiRrCIiIiIiIiUGmoEi4iIiIiISKmhRrCIiIiIiIiUGmoEi4iIlHJm\nNsDMMsys0Qmcm2FmoyMRl4iISCSoESwiIiIOLaMiIiKlhBrBIiIiIiIiUmqoESwiIlJEmNkYX/fi\ns8zsEzM7YGZbzGyAL/0WM1trZvvNbIGZNfU7t6yZjTWzTWZ2xPfvo2ZWNuA1mpjZR2Z20MxSzezv\nQPlc4hlkZivM7LCZ7TKzl8yseiR/ByIiIpFWNv8sIiIiUkgyuyS/A0wFnFiKDgAAAqxJREFUngLu\nhv9v725CbArjOI5//4lsWFAaNIaFEnthwYYlC0kWChsv2UlSo0YkspJGKaXIjJ2FLBQWlJea2DAS\nISmvDUpKxN/iPJdxu2Qzurfz/dTpOed/n+c55y5/PeeFkxExF1gG7AImAEeBAWBxGXMaWAMcAK4D\nS4A9wBxgPUBEjAcuU4XebcBbYAuwuvlCIuIQsAM4AuwEZpa5F0TEksz09mlJUkcyBEuS1F4SOJyZ\nAwARcRtYBWwGZmfmp1KfARyJiG5gMrAO6MvM/WWeyxHxDdgXEYcy8x6wEZgNLMrMoTLPReAuVcil\n1Hqogm9fZh4YVX9IFbBXAufH5u9LkjS2vB1akqT2c7Gxk5kfgDfArUYALh6UthtYShWeB5rmOQME\n1QoywCLgeSMAl/mTauV5tBVl3GBEjGtswBDwsZxPkqSO5EqwJEnt533T8Zc/1AAmAlPK/sumPq9K\n2/h9OvC6xfmaa9OoQvDjFn0TmNqiLklSRzAES5LU+d6Vtgt4OqreVdqR0r4E5rcY39V0PEIVdlcA\nH1r0H2lRkySpI3g7tCRJne8a1crtuqb6eqowe7Uc3wS6I2Jho0NEBLC2adwl4DvQk5l3WmzPxuRf\nSJL0H7gSLElS5wqAzByOiLPA3vIG6Bv8ejv0YGYOl/6ngN3AuYjopXrWeCswafSkmfkkIg4D/REx\njypEfwZmAcuBE5l5FUmSOpAhWJKk9tLq00P5l3rDBqpneDcBvcAL4CCw72fnzK8RsRzoB44Bn4BB\n4AJw/LeJM3sj4j6wneozTQk8B64Aj/7h2iRJakvhZ/4kSZIkSXXhM8GSJEmSpNowBEuSJEmSasMQ\nLEmSJEmqDUOwJEmSJKk2DMGSJEmSpNowBEuSJEmSasMQLEmSJEmqDUOwJEmSJKk2fgDr7DEn3OnZ\nFwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xaa2bc9d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_max_num_images_per_second(df_mean_time_per_image, libs_to_drop=[], fontsize=14)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "model                     lib              \n",
       "bvlc-alexnet              cpu                  16\n",
       "                          libdnn-cuda          16\n",
       "                          nvidia-cuda          16\n",
       "                          nvidia-cudnn         16\n",
       "                          nvidia-fp16-cuda     12\n",
       "                          nvidia-fp16-cudnn    16\n",
       "                          tensorrt-fp16        16\n",
       "                          tensorrt-fp32        14\n",
       "bvlc-googlenet            cpu                  16\n",
       "                          libdnn-cuda          16\n",
       "                          nvidia-cuda          16\n",
       "                          nvidia-cudnn         16\n",
       "                          nvidia-fp16-cuda     16\n",
       "                          nvidia-fp16-cudnn    16\n",
       "                          tensorrt-fp16        10\n",
       "                          tensorrt-fp32        10\n",
       "deepscale-squeezenet-1.0  cpu                  12\n",
       "                          libdnn-cuda          14\n",
       "                          nvidia-cuda          16\n",
       "                          nvidia-cudnn         16\n",
       "                          nvidia-fp16-cuda     16\n",
       "                          nvidia-fp16-cudnn    16\n",
       "                          tensorrt-fp16        16\n",
       "                          tensorrt-fp32        14\n",
       "deepscale-squeezenet-1.1  cpu                  14\n",
       "                          libdnn-cuda          16\n",
       "                          nvidia-cuda          16\n",
       "                          nvidia-cudnn         16\n",
       "                          nvidia-fp16-cuda     14\n",
       "                          nvidia-fp16-cudnn    16\n",
       "                          tensorrt-fp16        14\n",
       "                          tensorrt-fp32        14\n",
       "dtype: int64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# What is the batch size that gives the minimum time per image (or the maximum number of images per second)?\n",
    "df_mean_time_per_image.idxmin(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "model\n",
       "bvlc-alexnet                12\n",
       "bvlc-googlenet              16\n",
       "deepscale-squeezenet-1.0    16\n",
       "deepscale-squeezenet-1.1    14\n",
       "dtype: int64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Focus on e.g. nvidia-fp16-cuda, for which the batch size of 16 is not always the best.\n",
    "df_mean_time_per_image.idxmin(axis=1).reorder_levels(['lib', 'model']).loc['nvidia-fp16-cuda']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# # Is the same answer as via .min(axis=1).values?\n",
    "# df_mean_time_per_image.lookup(df_mean_time_per_image.index, df_mean_time_per_image.idxmin(axis=1)) \\\n",
    "#     == df_mean_time_per_image.min(axis=1).values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df_time_per_image = df_time / (batch_sizes*(len(df_time.columns)/len(batch_sizes)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df_min_time_per_image_index = pd.DataFrame(df_mean_time_per_image.idxmin(axis=1)).set_index(0, append=True).index.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df_model_lib = df_time_per_image[df_min_time_per_image_index] \\\n",
    "    .stack(['model', 'lib']).reorder_levels(['model','lib','repetition']).sum(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df_model_lib_mean = df_model_lib.groupby(level=['model', 'lib']).mean()\n",
    "df_model_lib_std  = df_model_lib.groupby(level=['model', 'lib']).std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "zero_positive_infinity = df_model_lib_mean > 1e5\n",
    "df_model_lib_mean[zero_positive_infinity] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# exclude_positive_infinity = df_model_lib_mean < 1e6\n",
    "# df_model_lib_mean = df_model_lib_mean[exclude_positive_infinity]\n",
    "# df_model_lib_std = df_model_lib_std[exclude_positive_infinity]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"plot_models\"></a>\n",
    "## Plot by Caffe models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"plot_models_all\"></a>\n",
    "### All libs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xabb69f70>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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lpgbloo0xzeswZKkHEhISnA5BpNFS+xNxhtqeiG/ExcUxf/58evXqRVBQEElJ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\nkiRJkqSR8f8BG7k6jp/+NqoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa7e55230>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mean = df_model_lib_mean.unstack('lib')\n",
    "std  = df_model_lib_std.unstack('lib')\n",
    "plot(mean, std)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"plot_models_gpu\"></a>\n",
    "### Only GPU libs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xa7c7ed50>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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FRBpAY25FRESq0phb8SaNuRUREREREWmC/Px8XC5XrQn74sWLSU5OblDZ+nz2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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa7cb8290>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mean = df_model_lib_mean.unstack('lib').drop('cpu', axis=1)\n",
    "std  = df_model_lib_std.unstack('lib').drop('cpu', axis=1)\n",
    "plot(mean, std)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"plot_models_cuda\"></a>\n",
    "### Only GPU libs with CUDA-level fp32 performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cuda_level_performance = ['nvidia-cuda', 'nvidia-cudnn', 'libdnn-cuda']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xa5e8d970>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Y3EqSJEmSGp49t5KkzNhzK0kaDPbcNj97biVJkiRJQ5LFreqGfUdSPsw9KT/m\nnyRl5xV5ByBJkiRJ1Whpaan6uq9qLC0tLVU/x55bSVJm7LmVJEm1ZM+tJEmSJKmpWdyqbth3JOXD\n3JPyY/5J+TD3mpPFrSRJkiSp4dlzK0nKjD23kiSpluy5lSRJkiQ1NYtb1Q17H6R8mHtSfsw/KR/m\nXnOyuJUkSZIkNTx7biVJmbHnVpIk1ZI9t5IkSZKkpmZxq7ph74OUD3NPyo/5J+XD3GtOFreSJEmS\npIZnz60kKTP23EqSpFqy51aSJEmS1NQsblU37H2Q8mHuSfkx/6R8mHvNyeJWkiRJktTw7LmVJGXG\nnltJklRL9txKkiRJkpqaxa3qhr0PUj7MPSk/5p+UD3OvOVncSpIkSZIanj23kqTM2HMrSZJqyZ5b\nSZIkSVJTs7hV3bD3QcqHuSflx/yT8mHuNSeLW0mSJElSw7PnVpKUGXtuJUlSLdlzK0mSJElqaha3\nqhv2Pkj5MPek/Jh/Uj7MveZkcStJkiRJanj23EqSMmPPrSRJqiV7biVJkiRJTc3iVnXD3gcpH+ae\nlB/zT8qHudecBr24jYjjIuJnEbE4ItZGxEMR8ZWI2KHLPi0RsaGH28sRMXKwY5YkSZIk1bdB77mN\niLuBduBa4DHgDcAc4M8ppYPL+7QAjwJfBhZ2O8R9fTXV2nMrSfmx51aSJNVSXz23rxjsYIBjUkpP\ndxnfERErgcsiopBSKnbZ9mhK6d7BDU+SJEmS1GgGfVlyt8K2w31AABMHORzVEXsfpHyYe1J+zD8p\nH+Zec6qXE0oVgAT8udvjX42IlyLi2Yi4NiL2GfzQJEmSJEn1Lvfr3EbEROD3wB9SSu8sPzYe+AKw\nCHgSeB3wOWAscFBK6S99HM+eW0nKiT23kiSplvrquc21uI2I7YHbgHHAG1NKy/rYd1fgQeDalNIp\nfexncStJObG4lSRJtVRvJ5QCICKGA9cDrcBb+ipsAVJKj0XEncCBWzr2rFmzaG1tBWD06NFMnTqV\nQqEAbFxf77j+xl17H+ohHseOh8q447Esj1cadxw73/HGeCqL37HjwRx3PFYv8Th2PFTGbW1tnHXW\nWXUTj+Pex3PnzqWtra2zvutLLjO3EfEKSpcCOgR4R0rpvgqf9ytgSkpprz72cea2QRWLxc5fYkmD\nJ8vcc+ZWqo6ffVI+zL3GVVfLkiMigJ8C7wLe1e3SP309bzLwR+AXKaUP9bGfxa0k5cTiVpIk1VK9\nLUv+PnA5XJepAAAgAElEQVQ88CXghYh4Y5dtj6WUHo+Ib1A6k/PdbDyh1HnAP4CvDHK8kiRJkqQ6\nNyyH13wnpcv+fA64q9vttPI+DwJvBv4N+DWlMyffAbwppfTwYAeswdGxvl7S4DL3pPyYf1I+zL3m\nNOgztymlKRXsMw+YNwjhSJIkSZKaQO7Xuc2aPbeSlB97biVJUi311XObx7JkSZIkSZIyZXGrumHv\ng5QPc0/Kj/kn5cPca04Wt5IkSZKkhmfPrSTVWLFY7PwLcdeLxhcKhaa7gLw9t5IkqZb66rm1uJWk\nQVT+BznvMGrG4laSJNWSJ5RSQ7D3QfWsdfx4ImLANyCT40QErePHZ/LezD0pP+aflA9zrzkN+nVu\nJakRta9YQX/n/orlW8f9Qvl+ocv9/ogVKwbwbEmSpObismRJqkBE9Lu4rZWg/pbbuixZkiTVksuS\nJUmSJElNzeJWdcPeBykf5p6UH/NPyoe515wsbiVJkiRJDc+eW0mqgD23lbHnVpIk1ZI9t5IkSZKk\npmZxq7ph74OUD3NPyo/5J+XD3GtOFreSJEmSpIZnz60kVcCe28rYcytJkmqpr57bV1RxkInA4cCb\ngF2AVwJPAX8BbgNuSyltGHi4kiRJkiRVZ4vLkiPirRFxPbAY+BFwNDCRUnG7H/AZ4GZgaUTMjoiR\ntQtXzczeBykf5p6UH/NPyoe515z6LG4j4lfAjcDzwPuBnVNKk1NK/5RSOiSltBcwEpgKfB84AXgk\nIo6scdySJEmSJHXqs+c2Ir4JfC2l9ETFB4x4NzA8pXRVBvFVzZ5bSbVgz21l7LmVJEm11FfPrSeU\nkqQKWNxWxuJWkiTVUl/F7YAvBRQRYwd6DAnsfZDyYu5J+TH/pHyYe82p4uI2Ij4SEf/cZfz6iHgM\n+HtE3B8R42sSoSRJkiRJW1DxsuSI+G/g0pTSd8vj3wATgH8HzgBuTSl9tFaBVsplyZJqwWXJlXFZ\nsiRJqqVMrnMLtAAPlQ84Cngr8J6U0g0R8TTw1QFHKkmSJElSP1TTczsM2FC+fwiQgGJ5vBTYObuw\nNBTZ+yDlw9yT8mP+Sfkw95pTNcXtw8C7yvdPAu5KKa0tj3cBnskyMEmSJEmSKlVNz+1M4MfASmAM\ncEJK6Rflbf8GtKSUjqpVoJWy51ZSLdhzWxl7biVJUi1l0nObUloQEUuANwL3pZRu77J5BXDdwMKU\nJEmSJKl/qrrObUrpzpTSxd0KW1JKF6SUbsg2NA019j5I+TD3pPyYf1I+zL3mVM3ZkgGIiEnAJGB4\n920ppVuyCEqSJEmSpGpU03O7G3AFcFDHQ+X/Jjpbv9JWmUdYJXtuJdWCPbeVsedWkiTVUlbXuf0B\nMBk4i9L1btdnEJskSZIkSQNWTc/tgcAZKaXvpJR+k1K6rfutVkFqaLD3QcqHuSflx/yT8mHuNadq\nitvHcLZWkiRJklSHqum5PRn4GHBkSun5mkY1APbcSqoFe24rY8+tJEmqpayuc/vjiHgdsDgi7gFW\nbr5LOnUAcUqSJEmS1C8VL0uOiFnAZ4HRwP7AoT3cpH6z90HKh7kn5cf8k/Jh7jWnas6WPAf4JXBa\nSunZGsUjSZIkSVLVqum5fQ44NqV0c21DGhh7biXVgj23lbHnVpIk1VJfPbfVnC35TmDPbEKSJEmS\nJCk71RS3ZwIfiYgPRMTYiBjW/VarIDU02Psg5cPck/Jj/kn5MPeaUzU9t38u//fyXranKo8nSZIk\nSVImqum5nQ19t5yllOZkENOA2HMrqRbsua2MPbeSJKmW+uq5rbi4bRQWt5JqweK2Mha3kiSplrI6\noZRUU/Y+SPkw96T8mH9SPsy95tRncRsR/zsihldzwIjYPyLeObCwJEmSJEmqXJ/LkiPiD8B4YD5w\nZUrpgV72GwMcA5wMHALMSildlX24W+ayZEm14LLkyrgsWZIk1VK/e24jIigVrJ8GXg+sBv4IPAms\nA8YAuwG7l8c/BeaklBZnGH9VLG4l1YLFbWUsbiVJUi31u+c2lVyeUtoPmAZ8E1hDqaB9AzACuAP4\nMLBLSulDeRa2amz2Pkj5MPek/Jh/Uj7MveZU8XVpU0r/CfxnDWORJEmSJKlfBv1SQBFxHDADOADY\nGVgC/AL4SkrpuS77jQa+ARwLvBK4Gzg7pfQ/Wzi+y5IlZc5lyZVxWbIkSaqlurrObUTcDbQD1wKP\nUVrePAf4c0rp4C773QlMBj4DPAv8H2BvYL+U0rI+jm9xKylzFreVsbiVJEm1VG/XuT0mpXRSSunK\nlNIdKaVvA2cAb4yIAkBEHEupx/eDKaWrUkqLgHeX4z0nh5g1COx9kPJh7kn5Mf+kfJh7zWnQi9uU\n0tM9PHwfpUmIieXxdGBZSun2Ls9bDSyktExZkiRJkqROg74succgIj4OfA84IKX0h/LS5WdTSkd1\n2++fgYuAESmltb0cy2XJkjLnsuTKuCxZkiTVUr0tS95EREyk1HP7m5TSH8oP7wis7GH3Z8r/HTMY\nsUmSJEmSGkNVxW1EbB8RZ0TEzyLi1oh4dfnxkyLiddW+eERsT+nEUuspXStXQ5i9D1I+zD0pP+af\nlA9zrzlVfJ3biJgEFIFdgYeAfYAR5c1vA94B/H9VHG84cD3QCryl2xmQV9Lz7OyOXbb3atasWbS2\ntgIwevRopk6dSqFQADb+Ijt27Nhx1WNKCuX/5j3uiHGg76/rsfrz/N6PV463kO94YzyVxe/Y8WCO\nO9RLPI4dD5VxW1tbXcXjuPfx3LlzaWtr66zv+lJxz21EXEWpoD0KeJzSbOsBKaXfR8RM4IKU0msr\nPNYrKM3YHgK8I6V0X7ftPwQOTylN7vb4PKCQUprSx7HtuZWUOXtuK2PPrSRJqqWsem4Pp1TAtsNm\n3/EeZ+OZjrcUTAALgAJwbPfCtuw6YGJEHNrleSMpnUX52ipiliRJkiQNARUvSwa2Adb0sm0U8I8K\nj/N94HjgS8ALEfHGLtseSyk9Tqm4vQf4j4g4B3gW+Gx5n3+pImY1kGKx2Ln8QNLgMfek/NRL/hWL\nxc6lgF1jKhQKdRGflLV6yT1lq5ri9r+B44Cbeth2FPBfFR7nnZRmfj9XvnU1B/hiSilFxLuAb1C6\nRNBw4C5KS5IfryJmSZIkbUHXIjYiOgtdSWok1fTcvg/4GfBDSsuKbwZOAV5NaVb13SmlngrfQWXP\nraRasOe2MvbcSo2v3M+WdxiS1KO+em4rLm7LB/o4cBGlsyR3HHAN8M8ppUsHGmgWLG4l1YLFbWUs\nbqV8tLaOp719Rd5hbKKlZRyLFz+RdxiSmkxWJ5QipfRvlE4cdSTwQUrLkXetl8JWjc0lUFI+zD0p\nP1nlX3v7ClIikxtkc5x6K7alrvzsa07V9NwCkFJ6HvhtDWKRJEmSJKlfql2W/ApgGjCJ0kmeNpFS\n+lF2ofWPy5Il1YLLkivjsmQpHwPNvWKxdOu433ES2UJh4/3qYzL3JGUvk57biNgf+CWwKxv7bbtK\nKaWt+h1lRixuJdWCxW1lLG6lfJh7koaKrHpu/w14DngP8FpgSrfbbgOMU0OcvQ9SPsw9KT/mn5QP\nc685VdNzuxfw/pTSDbUKRpIkSZKk/qhmWfLvga+mlK6ubUgD47JkSbXgsuTKuDRSyoe5J2moyGpZ\n8v8Bzo+IydmEJUmSJElSNioublNKNwE3Ag9HxB8j4vZut9tqF6aGAnsfpHyYe1J+zD8pH+Zec6q4\n5zYizgPOAZ4EVgMv1yooSZIkSZKqUU3P7ROULgX0v1JKdVvY2nMrqRbsua2MfX9SPsw9SUNFVj23\n2wFX13NhK0mSJEkamqopbm8EptUqEMneBykf5p6UH/NPyoe515yquc7tXOCyiAC4CVjZfYeU0v/L\nKC5JkiRJkipWTc/thi7DHp+UUtoqi6AGwp5bSbVgz21l7PuT8mHuSRoq+uq5rWbm9sP0UtRKkiRJ\nkpSnimduG4Uzt42rWCxSKBTyDkPqUTPP3GaZe84eSdXJKv/MPak6fu9sXFmdLVmSJEmSpLrU58xt\nRPwIuDCl9Gj5fl9SSum0TKPrB2duJdVCM8/cZsnZIykf5p6koWIgPbdvA75Vvn8Yfffc+q+XJEmS\nJCkXfS5LTilNSSk9UL7fWh73dtttcEJWs/J6Y1I+zD0pP+aflA9zrzlV3HMbEW+JiB162bZ9RLwl\nu7AkSZIkSapcNde5fRmYllK6t4dt/wTc63VuJTUre24rY9+flA9zT9JQkdXZkns8QNm2wMtVRSVJ\nkiRJUkb6LG4jojUiDouIw8oPHdAx7nJ7F/BpYEnNo1VTs/dByoe5J+XH/JPyYe41py2dLflU4AJK\nZ0JOwHfYdAY3lcf/AE6vRYCSJEmSJG3Jlq5z2wK0Uipgb6FUwP6p227rgL+mlJ6pUYxVsedWUi3Y\nc1sZ+/6kfJh7koaKvnpuqzmh1FuB/0opPZdlcFmzuJVUCxa3lfELtpQPc0/SUJHJCaVSSrfVe2Gr\nxmbvg5QPc0/Kj/kn5cPca05b6rmVJDWhYrHY+cF+zTXX8J73vAeAQqFAoVDILzBJkqR+qnhZcqNw\nWbKkWqjHZcnDt4J1dXgRtnr7J9ilkRoKXJYsaajoa1myM7eS1KDWvQxpdv+eW3wUiovL9xdDobV0\nv9AKhSn9jyn6GY8kSdJAWdyqbhSLRZdDSoOkMGVjEVt8dGAFraT+87NPyoe515wqPqGUJEmSJEn1\nqqqe24g4FZgBTAaGd9ucUkq7Zxhbv9hzK6kW6rHnNuj/suRaidn23Ep5sOdW0lCRSc9tRHwemAP8\nD9AGrMsmPEmSJEmSBqaaZcmnAd9KKe2bUpqZUvpQ91utgtTQ4PXGpHwUH807Amno8rNPyoe515yq\nKW7HAgtrFYgkSZIkSf1Vcc9tRFwP3JxS+mZtQxoYe24l1YI9t5Wx51bKhz23koaKrK5zexbwi4h4\nGrgBeKb7DimlDf0LUZIkSZKk/qtmWfJfgX2AecAK4KVut/WZR6chxd4HKR/23Er58bNPyoe515yq\nmbn9ItTdqjxJkiRJkqq7zm0jsOdWUi3Yc1sZe26lfNhzK2moyKrntusBdwDGACtTSs8NJDhJkqRa\nKBaLnUsPi8UihUIBgEKh0HlfktQ8qpq5jYgjgS8DUylPGgC/Bz6XUvpNTSKskjO3javrFw+p3jTz\nzG3xUShMGfhxwJlbZa91/HjaV6zIO4xNtIwbx+InnsjkWFl99jlzK1XH752NK5OZ23Jh+yvgb8CF\nwBPABOBE4IaIOLpeClxJktQc2les6PcflorlW8f9Qvl+ocv9/og6K7YlSSXVXOf2bmAlcEzXS/5E\nxDDgemB0SungmkRZBWduJdVCM8/cZsmZW2WtbnOvzn6nnLmVNFT0NXNbzaWA9gO+1/1atuXx9ykt\nVZYkSZIkadBVU9yuA0b2sm1EebvUb15vTMqH17mV8uNnn5QPc685VVPcFoELI2KT045ExGRgNnBr\ndmFJkiRJklS5anpuXwP8DhgF3AMsB8YDbwKeBQ5JKT1cozgrZs+tpFqo276/2XlHsSl7bpW1us29\nOvudsudW0lCRSc9tSumvwL7At4Ftgf2B4cC3gKnVFLYRMTEivhMRd0XE8xGxoTwD3H2/DT3cXo6I\nfSt9LUmSJElS86tmWTIppeUppc+klN6YUnp1+b/npJSWV/m6ewDHA88At0Off5T9EaXZ4Y7bNOCv\nVb6eGoC9D1I+7LmV8uNnn5QPc685VXyd2yyllG6jdI1cIuI04Ig+dl+WUrp3UAKTJEmSJDWkPovb\niLgF+GRK6aHy/b6klNLbswtNQ02hUMg7BGlIKkzZ8j6SasPPPikf5l5z2tKy5K6NusPK495uVS1x\nrsInIuLFcm/uzRFxSI1eR5IkSZLUoPosSFNKb0spPVS+XyiPe73VIL4fA58E3g58BNgRuCUi3lKD\n11LO7H2Q8mHPrZQfP/ukfJh7zani2daIOCUixvaybceIOCW7sEpSSqemlK5OKf0upbQAOBRYBnwp\n69eSJEmSJDWuak4oNY/SmYqf7mHblPL2y7MIqjcppeci4lfAh/vab9asWbS2tgIwevRopk6d2rmu\nvuOvNI7rb1woFOoqHseONxtTUij/N+8xlGZdO3pmO2Zg8x53xlYOuKOtKa/xxniK5e0Fxw007tAx\nKtTLuE5+Ph3j0mP555v557iRxh3qJR7HPY/nzp1LW1tbZ33Xl6j04toRsQF4U09nLi4vE/5NSmnb\nig626XNPAy4FpqSUllSw//eAD6WUtutle/KC4ZKyFhF9XrMsDwGk2XlHsamYDfX2T3AE+LnQuOo2\n9+rsdyoizD1JQ0Lp37sUPW0btoUnTo2ID0fEh8sPTe8Yd7mdDnwZeDjjuHuKZyRwDPCftX4tDb7u\nf0WTNDjsuZXy42eflA9zrzltaVnyscAF5fsJ+Fwv+z0NnFbNC0fEceW7B1D6I+jREfEk8GRK6faI\n+DTwWuBWSn22rcCngXHAjGpeS6pGsVjs/AevWCx2LokoFAqd9yVJkiTVlz6XJUfEKGA0peLz/wHv\nA/7Qbbd1wIpq1wKXlzn39JzbUkqHRcQxwLmUCtxRwGrgTuBLKaX/6uO4LktWZsrLHvIOQ3WgbpdG\nzs47ik25LFlZq9vcq7PfKZclSxoq+lqW3OfMbUppFbCqfJApwPKU0vosgkop9bkkOqV0PXB9Fq+l\noae1dTzt7SsyOVZEj7lTtZaWcSxe/EQmx5IkSZK0qYrPlpxSaq9lIFLXJcAD1d6+ot9/wS4WN57l\nseuZJwuFjff7IyKbYlvKWtczLksaXFl+9kmqnLnXnCoubvtYRtwppbTVgCOScjbQIlaSJEnS4Kvm\nUkCz2by4HQscAWwLXJZSmpNpdP1gz63A3iNlr277/mbnHcWm7LlV1uo29+rsd8rPPUlDRb97brtK\nqeevUBGxFbCQcm+uJEmSJEmDrc+TOlUipfQy8H3grIGHo6HM641J+fA6t1J+/OyT8mHuNacBF7dl\n2wI7ZnQsSZIkSZKqUk3P7eQeHt4G2Af4F2BpSumwDGPrF3tuBfYeKXt12/c3O+8oNmXPrbJWt7lX\nZ79Tfu5JGioy6bkFFtPz2ZIDeAQ4vfrQJEmSJEkauGqWJX+4h9tM4GDgdSmlP2cfnoYSex+kfNhz\nK+XHzz4pH+Zec6rmbMmX1TAOSZIkSZL6rZqe29cAE1JKt/Ww7S3A8pTSwxnHVzV7bgX2Hil7ddv3\nNzvvKDZlz62yVre5V2e/U37uSRoq+uq5rWZZ8lxgei/bjgG+WW1gkiRJkiRloZri9gDg9l623Q4c\nOPBwNJTZ+yDlw55bKT9+9kn5MPeaUzXF7QjgxV62vQSMGng4kiRJkiRVr5qe2weB61NK5/aw7WvA\nsSml12UcX9XsuRXYe6Ts1W3f3+y8o9iUPbfKWt3mXp39Tvm5J2moyKrn9nLg7Ig4PSK2LR9424g4\nHTgLmD/wUCVJkiRJql41xe03gOuA7wDPR8TfgefL4+uAr2UfnoYSex+kfNhzK+XHzz4pH+Zec6rm\nOrcvA8dHxGHAEcCOwFPAopRSsTbhSZIkNZdisdj5xfqaa67hPe95DwCFQoFCoZBfYJLU4CruuW0U\n9twK7D1S9uq272923lFsyp5bZa1ucy+j36ly71hGx8kgoAyZe5JqIaueW6Lk3RHxjYiYFxEt5cff\nGhG7ZBGsJEmSJEnVqri4jYgxwF3ANcBHgFOAseXNHwHOyzw6DSn2Pkj5sOdWqs62W5VmDrK4QTbH\nklQdv3c2p4p7boF/ASYBbwbuA9Z32fZb4J8zjEuSJKkurXs5u5aAmJ3NsSKDY0hSo6umuD0W+ExK\n6e6I2KrbtiWUCl+p3zyJhpSPwpS8I5CGluKjUFxcuv/WFph9a+l+odV8lAaL3zubUzXF7Q7A471s\nG07p/AqSJEnqQ2GKRawk1UI1J5T6C6VLAPXkrcAfBx6OhjJ7H6R82HMr5cf8k/Lh987mVM3M7feB\n70bEKmBB+bHREfEh4H8BH806OEmSJEmSKlHVdW4j4iLgM5SWIAeQgA3A11NKn6tJhFXyOrcCr/en\n7NXttTZn5x3FprzOrbJm7lXG3JM0VPR1ndtqZm5JKZ0XEZcAhwM7A08Dv0kp/b+BhylJkiRJUv9U\n03MLQEqpPaX0g5TSV1JK/25hq6zY+yDlw54/KT/mn5QPv3c2p4qL24j4WkT0ONMbETtFxPXZhSVJ\nkiRJUuUq7rmNiLWUzog8o+tsbUQcDlwObEgpTaxJlFWw51Zgz62yZ99fZez7U9bMvcqYe5KGir56\nbqtZlvxGSte6/UNEnBIRW0fEvwI3AfcD+w08VEmSJEmSqldxcZtS+iPwT8BPgXnAEuDjwFkppekp\npadqE6KGCnsfpHzY8yflx/yT8uH3zuZU1QmlUkovAncB64FxwMPAwhrEJUmSJElSxao5odSIiFgA\n/JDSzO3BwDZAW0TMqFF8GkIKhULeIUhDUmFK3hFIQ5f5J+XD753NqZrr3P43MAI4LqV0DUBE7A98\nG7giIt6ZUjq1BjFKkiRJktSnapYlLwb26yhsAVJKL6SUPgKcAByTcWwaYux9kPJhz5+UH/NPyoff\nO5tTNTO3h/V2jZ2U0s8j4j8zikmSJEmSpKpUc7bkXi9UFhHDgLWZRKQhy94HKR/2/En5Mf+kfPi9\nszn1WdxGxDPlvtqOcUTEdRGxW7ddDwSerEWAkiRJkiRtyZZmbkez6dLlYZR6a0fXLCINWfY+SPmw\n50/Kj/kn5cPvnc2pquvcSpIkSZJUjyxuVTfsfZDyYc+flB/zT8qH3zubk8WtJEmSJKnhVVLcToyI\n3conkdqt+2Plx3etXYgaKux9kPJhz5+UH/NPyoffO5tTJde5/VkPj13TbRxAr5cKkiRJkiSplrZU\n3H5oUKKQsPdByos9f1J+zD8pH37vbE59FrcppfmDFYgkSZIkDSXFYrFziXSxWOwsuguFggV4P0RK\nzbWaOCJSs72noaJrQg9URFBvvwYR4O9m44qIuuu9CCDNHvhxio9mN3sUszH3lKlmzj3ILv/MPak6\nWX7vzErp+6s5syXln1P0tM2zJUuSJEmSGp4zt2pKztwqa80+e5QVZ4+UNXOvMuae1Picua2MM7eS\nJEmSlLHW1vFERCY3ILNjtbaOz/knk49cituImBgR34mIuyLi+YjYEBGTe9hv24j4l4hYFhFry/sf\nmkfMqj2vNyblw+tsSvkx/6R8ZPW9s719BSmRyQ2yOU5KpbiGorxmbvcAjgeeAW6n92vk/gg4DTgf\neBewHPh1ROw7GEFKkiRJkhpD7j23EXEacCkwJaW0pMvj+wF/AGallC4vP7YV8CDwUErpPb0cz55b\n2XOrzNn3Vxn7/pQ1c68y5p6Uj4F+5ywWS7eO+x0ncC4UNt7vX1zNm3999dz2eZ3bnL0bWA9c1fFA\nSunliPgJcG5EbJ1Seim36CRJkiRpAAZaxGpT9Vzc7gU8mlJ6sdvjDwLbUFra/OdBj6qB1ftFouvx\nemPSUJDldW4lVcf8k/Lh987mVM/F7Y7Ayh4ef6bLdlWhaxEbEZ7ASZIkSVLT8FJADaR1fJ2eanx8\nNqca969nUj6cNZLyY/5J+fB7Z3Oq55nblcBmlwdi44ztMz1sA2DWrFm0trYCMHr0aKZOndr5C9wx\nW9mI4/YVK7i1/B4L5f8WqxgXgcvK48XlxxYDU4Gz+nG8jvHbVmw81Xi9/Lw2xlOOt1Av4/69H8d1\nMqakUP5v3mPYdEljxyVF8h53xlYOOO/82xhPsby94LiBxh06RoV6GddJvnUtjovF/PPN/HPcMW5r\na+PZZ58F4JprrmHq1Km0trZ27pN3fNmOO94PdTXukP/PZ2DjuXPn0tbW1lnf9aWez5b8eeBzwOiu\nfbcRMRs4FxjZ0wmlmvlsyfV4xkgonzUyg595sVjc5B+8gfBsycpaPeZfVmdszbLnzzO2KmvNnHuQ\nXf6Ze6pn5bPb5h3GJrL63lmP3zmhufOvr7MlDxvsYKqwkNKJo07oeKB8KaD3A7/2TMmSJElSbdRj\nO1xWrXBqXrktS46I48p3D6D0R9CjI+JJ4MmU0u0ppbaI+CkwNyK2AR4FPgm0AjPyiFm1ldWsraTq\n2PMn5cf8U71qX7FiQKsmimxczl9k45L+Qpf71YourXAD5ffO5pRnz+3V0JkzCfhe+f5twGHl+7OA\nLwMXAqOBB4AjU0oPDF6YkiRJkqpRoP9FrNRfuS1LTikNSylt1cPtsC77rEspfSaltEtKabuU0rSU\n0h15xazaKnbvfpc0KLqfCErS4DH/pHz4vbM51XPPrSRJkiRJFbG4Vd2w90HKhz1/Un7MPykffu9s\nTha3kiRJkqSGZ3GrumHvg5QPe/6k/Jh/Uj783tmcLG4lSZIkSQ3P4lZ1w94HKR/2/En5Mf+kfPi9\nszlZ3EqSJEmSGt7/396dh0tSlYcf/74soriETcAFGXcDKuCCoImMskgiiD8VQ6IsWZQYt2gSAyLM\niEsIEiAKPiqoIQoaRURxQYRhABUQl1EWUQRmQBZZh2VYZ+b8/jinZ2pq+t7bl7l3TnXP9/M8/dxb\n1adPne6qt7reqnOqTW7VGY59kOpwzJ9Uj/En1eFx52gyuZUkSZIkDT2TW3WGYx+kOhzzJ9Vj/El1\neNw5mkxuJUmSJElDz+RWneHYB6kOx/xJ9Rh/Uh0ed44mk1tJkiRJ0tAzuVVnOPZBqsMxf1I9xp9U\nh8edo8nkVpIkSZI09Exu1RmOfZDqcMyfVI/xJ9XhcedoMrmVJEmSJA09k1t1hmMfpDoc8yfVY/xJ\ndXjcOZpMbiVJkiRJQ2+d2g2QeubOnetZNKmCudd69UiqxfiTVp+5c+cuG2t7+umn8/rXvx7IV3E9\nBh0NJreSJEmSRl4zif3whz/MvHnz6jZIU85uyeoMz5hJdXjVSKrH+JOkqWNyK0mSJEkaeia36gx/\nb2P+VakAAB09SURBVEyqw9/ZlOox/iRp6jjmVpIkSVLnrbc2RMSU1TeVdakbTG7VGY65lepwzJ9U\nj/EnDe7BJZBmT01dMXtq6oopqENTx27JkiRJkqShZ3KrVdbrItKlh6TBOeZPqsf4k6SpY7dkrbJV\n6SIy91qYOz//f/qV8Prn5f9nzli1rlp2EZEkSZLWLCa3qmrm05cnsbNfVbct0prKMX9SPcaftPo0\nL6rstCXMPjf/v6oXVdQdJreSJEmSRl7zoopGk2Nu1RmOO5LqMPakeow/qQ5jbzSZ3EqSJEmShp7J\nrTrDbiJSHcaeVI/xJ9Vh7I0mk1tJkiRJ0tAzuVVnOPZBqsPYk+ox/qQ6jL3RZHIrSZIkSRp6Jrfq\nDMc+SHUYe1I9xp9Uh7E3mkxuJUmSJElDz+RWneHYB6kOY0+qx/iT6jD2RpPJrSRJkiRp6JncqjMc\n+yDVYexJ9Rh/Uh3G3mgyuZUkSZIkDT2TW3WGYx+kOow9qR7jT6rD2BtNJreSJEmSpKFncqvOcOyD\nVIexJ9Vj/El1GHujyeRWkiRJkjT0TG7VGY59kOow9qR6jD+pDmNvNJncSpIkSZKGnsmtOsOxD1Id\nxp5Uj/En1WHsjSaTW0mSJEnS0DO5VWc49kGqw9iT6jH+pDqMvdHU6eQ2InaKiKV9HnfUbpskSZIk\nqTvWqd2AASTg3cDPGvMWV2qLppFjH6Q6jD2pHuNPqsPYG03DkNwCXJlS+mntRkiSJEmSuqnT3ZKL\nqN0ArR6OfZDqMPakeow/qQ5jbzQNQ3ILcHJELI6I2yLi5IjYonaDJEmSJEnd0fVuyXcBRwHnAXcD\n2wGHAD+JiO1SSrfVbJymlmMfpDqMPake40+qw9gbTZ1OblNK84B5jVkXRMQFwE+B9wCHVWmYJEmS\nJKlTOp3c9pNS+mVE/A546VhlDjjgAGbMmAHABhtswLbbbsvMmTMBmDt3LsDwTpf3OLP87cp0T2/8\nQu9s2GSmm2MfHsnrm9PL2lMaWD6+DkzPLdMznR7GabKZ5W/tacjb/FTESzMGRy3+lrdnbnl+ptND\nNN3Tm5rZlekpjJepiD/I23zteDP+Rmu6pzc1s/L0svZMQfzNuxn+eccpqq80sHa8jWr8HXvsscyb\nN29ZfjeeSClNWKhrIuJyYEFK6S/7PJeG8T0NIiLo4jsLIM1e9XqaB+mrKmZD1zaDCBjVbXNN0MX4\nM/YGY+wNt1GOPZi6+DP2NNWMvcF0MfZgtOMvIkgp9b3p8FqruzGrKiJeAjwXuLh2WzS1HPsg1WHs\nSfUYf1Idxt5o6nS35Ij4MnAt8AtgIfAi4CDgeuBTFZsmSZIkSeqQrl+5vQzYE/gCcCb5JlKnAjuk\nlO6o2TBNPX9vTKrD2JPqMf6kOoy90dTpK7cppSOAI2q3Q5IkSZLUbV2/cqs1iGMfpDqMPake40+q\nw9gbTSa3kiRJkqShZ3KrznDsg1SHsSfVY/xJdRh7o8nkVpIkSZI09Exu1RmOfZDqMPakeow/qQ5j\nbzSZ3EqSJEmShp7JrTrDsQ9SHcaeVI/xJ9Vh7I0mk1tJkiRJ0tAzuVVnOPZBqsPYk+ox/qQ6jL3R\nZHIrSZIkSRp6JrfqDMc+SHUYe1I9xp9Uh7E3mkxuJUmSJElDz+RWneHYB6kOY0+qx/iT6jD2RpPJ\nrSRJkiRp6JncqjMc+yDVYexJ9Rh/Uh3G3mgyuZUkSZIkDT2TW3WGYx+kOow9qR7jT6rD2BtNJreS\nJEmSpKFncqvOcOyDVIexJ9Vj/El1GHujyeRWkiRJkjT0TG7VGY59kOow9qR6jD+pDmNvNJncSpIk\nSZKGnsmtOsOxD1Idxp5Uj/En1WHsjSaTW0mSJEnS0DO5VWc49kGqw9iT6jH+pDqMvdFkcitJkiRJ\nGnomt+oMxz5IdRh7Uj3Gn1SHsTeaTG4lSZIkSUPP5Fad4dgHqQ5jT6rH+JPqMPZGk8mtJEmSJGno\nmdyqMxz7INVh7En1GH9SHcbeaDK5lSRJkiQNPZNbdYZjH6Q6jD2pHuNPqsPYG00mt5IkSZKkoWdy\nq85w7INUh7En1WP8SXUYe6PJ5FaSJEmSNPRMbtUZjn2Q6jD2pHqMP6kOY280mdxKkiRJkoaeya06\nw7EPUh3GnlSP8SfVYeyNJpNbSZIkSdLQM7lVZzj2QarD2JPqMf6kOoy90WRyK0mSJEkaeia36gzH\nPkh1GHtSPcafVIexN5pMbiVJkiRJQ8/kVp3h2AepDmNPqsf4k+ow9kaTya0kSZIkaeiZ3KozHPsg\n1WHsSfUYf1Idxt5oMrmVJEmSJA09k1t1hmMfpDqMPake40+qw9gbTSa3kiRJkqShZ3KrznDsg1SH\nsSfVY/xJdRh7o8nkVpIkSZI09Dqd3EbEUyPi1IhYGBF3RcQ3ImKL2u3S9HDsg1SHsSfVY/xJdRh7\no6mzyW1EPAY4F3gOsC/wVuDZwJzynCRJkiRJQIeTW+DtwAxgr5TSGSmlM4DXlXkHVmyXpoljH6Q6\njD2pHuNPqsPYG01dTm73BC5KKS3b9FJK84EfA3vVapSmz7yba7dAWjMZe1I9xp9Uh7E3mrqc3G4N\nXNZn/uXAVqu5LVoNFj5QuwXSmsnYk+ox/qQ6jL3R1OXkdiPgzj7z7wA2XM1tkSRJkiR1WJeTW61h\n5i+s3QJpzWTsSfUYf1Idxt5oipRS7Tb0FRE3A99MKb2jNf944E0ppc3GeF0335AkSZIkaZWllKLf\n/HVWd0Mm4XLyuNu2rYArxnrRWG9UkiRJkjS6utwt+dvADhExozej/P8K4FtVWiRJkiRJ6qQud0te\nH5gH3A8cWmYfDjwW2CaldF+ttkmSJEmSuqWzV25L8vpq4HfA/wJfAq4GdjaxlSRJkiQ1dTa5BUgp\n/SGltHdKaYOU0p+klN6YUrqudru6KiJmR8TSiFjl9RoRcyNizlS0axLLnB8RX1idy1wVETErImbW\nboemz7DHVC1dea8RsU2J0w1qt6WLurKeVpeIOKDE89Nqt0Xji4idImLWJMo/LiKOiohzI+Kusp5f\nOcllvi0ifhMRD0TElRFx4ORbPvCyjD110uqOvYjYLyJOLTnA0qnIAzqd3GrSUnlMVV2rWzf7yI9t\nFrl3gUbXsMdULV15r9uS43Sj2g3pqK6sp9VlKuNZ02smcNgkTixuDBwAPAycxSTXc0S8DfgM8HXg\nNcDXgE9PY4K7pm2Hxt7wmMlqjD3grcAzymvvmuRr++ry3ZIlSVoVgQdU0jCK1t9xpZQWAJsARMTO\nwBsGXlDE2sBHgZNSSoeV2edFxFOAj0TEiSmlJQO3XBpuqy32yut3W7bgiL+YzGvH4pXb0bRVRMyJ\niEURcWNEfBggIjaLiIcj4l3tF0TEByLioYjYeKxKI2KTiPh0RFxXuu1cFxEnRcS64zUmInaNiO+W\ntiyKiEsj4v2DnBWKiBkRcXJE3FKW+cuIeH3j+fVLN6KLyxdUb/5uEbEkIt7RmLc0Ig6PiHdHxDUR\ncXfpGrRVn+W+ISIuLO29MyK+FhFbNOsiHzR/qNS7JCIOa9ejkdG1mForIj7aiKmzI+K5ZVs8rFV2\n94j4SUTcFxELI+KbEfGcPnW+r3TFe7DU+6mIeHyf9n6ldD26IyI+HxF7DtINqbz2MxHxh/Jef1Ou\nljTL9LquvSwivlyWc0NE/HdEPKpV9jER8Z8llh8sfz/YeH5/oNe96feNOF0ju8VFxD6xvMvlpc39\naKPMhOuolBt3v1zK9Lr0P79f7DTKPbZsawtKXX+MiLOa22hErB0R/x4Rl0fE/WW53+uViYj1IuLo\n8r7uiYibIuLbEfHcAT+bt0fEvFL3rRFxYkRsOMDrBmn7JhFxStmW74yI/4mIvdoxE2MMyxkjprcp\n7++OEtc/iog/6/Pancq+4e6IuDcizoyIrRvPzyr193vs1yg3mfX9rIj4TlkP8yPiUFom2s4id4ns\nveeHe7E70fpYBTuSD85Pbs3/Evmq1Eqf7WQYe+N+Nsbemh170yOl5GNEHuTud0uBq4CDgV2AT5R5\nh5Uy3wcu6vPaXwOnN6bPBeY0pjco9d4KvAd4FfBXwCnAYydo14HA+8hdfXYC/oXc9eDjrXLXAl9o\nTD8VuKW07a+BXYETgSXAHo1y2wIP9OoDNgNuAk5r1b8UuKZ8BnuQzy5dQ75p2VqNcv9Yyp4A7A7s\nTf5t5at77xXYvpT5fPl/e+DJtbcBH1P76HBMfbTEwceBnYEPAL8t8w5rlNsdWAycCbwW2Kcs84/A\nkxrlPl7e03+XOHsvcA9wXmu5FwB3lBjZldyNb35Z7ivHea+PL+2bD/wduTv/f5a2vbNRbv/Sjt8C\ns0u5Q0q5WY1ya5e23Aq8u3x2B5Pvrv+JUmYT8h32lwD/rxGn69beripsx7uUz+F04C+A/YAFwI29\n9TSJdTTofnnC2CnlTiDvrw8gJxF7AUcC2zfKnAo8VNqzG/A64Chgp/L8E4DPkffVf17q+EHZVjdt\nbV9LgKc15h1R6j6ytHF/4A/AhZRflBjncx2k7RcAC4F3ND6r61g5Zlb4/mvMb39eLwLuBc4nf4ft\nTv55xAeA7RrlXkvuJngasGd5/Lh8Jk8pZZ7ciIve4+vl89jhEa7vX5O/718NHFPm7T+ZfUFp1wll\nGTv02jaJ7X3n9uc7QfkDS/nNWvOfWNr/DmPP2MPYm/LY6/P66/uti0nXs6oV+OjOo2zgS4B/a83/\nHDmZfALwN6XMsxvPb1uC4I2Nee2D08NLsL5wCtq5NvBB4PbW/HZy+3nyQfgGrXJnAb9ozfvn0r6d\nyQfy1wMbtcr0DprXbsx7Yy+Iy/RjyTvDE1qv3RJ4EHhPq77Da693H9P36GJMkZPie4BPtea/j5W/\njH9WtvnmyZsZ5C/Qo8r0huQv58+36ntLqW+PMr1b+z2V+d9qf5n1ea+HAvcBz+jzOd7Sax/Lk9vD\nWuXOAK5sTO9blvmKVrkPlveySaO+Je3lrmkP8oHVZa15Lyufde8Ae9B1NNB+eZDYKdOX9rbFMdr+\n6tLOdw7yXstr1gIeA9wNvLcxf4UDbPJ+fTFwSOv1O5Zlvm6C5UzU9l1LPXu35n+vT8wMeoB9DnAZ\nK36PBfkE7GmNeVcBZ7Xqehz5hNDRY7R379KuZkI12fW9X6vcr4EzG9ODbme9+tbq19YJ1stkk9uD\nS/lHteavXT7/QybbhkYdxl4y9oy9gV8/Jcmt3ZJH09db018lB9bzgW8Ci8gHhz37khO6M8apc1fg\nkpTSr8cqULqvLHs05m8eEZ8t3SQeIh/QfxTYICI2HWeZryHviO5p1LsOObC3iYjH9QqmlI4t879D\nPgO4b0rpjj51/jCtOHbmUvLOqddVcUfy2a1TWu/lBuBKYFJ3X9TI6FJMvQBYn3xGvenU1mvXB7YD\n/i+ltLQ3P6U0n3zAtVOZtSOwLit3yfsq+eCjV26HMn36eMsdw2uAi4EFrfdzFvkKa3NoQCLHfdOl\nLI/RXn0LgIta9f0QeFRpq8hd2IGX0FpPKaWLyWfwewZdRwPvl4vxYgfgEuCAiDg4Il4cKw9X6R2k\nnjjB+3xzRFwUEXeSt9NF5JOV43WP3JW8/2/v7y8hn0B6Zal7rVYs9saiTdT2Xsyc1uczmLSIeHRp\n06llutfetYGzG+19FvDMPu/rAfJVsZW+xyLiJcD/AMellI5vPDXZ9d2O3ctYOXYH3Rf0+wzWipX3\niZ1k7Bl7xl4dJrej6Y99poPcHeJ+4BvkqzK9ne8+wNdSSg+NU+fG5O4ifUXETuSk9aHGX8qO6Azg\nL8lXql5F3tl/rLz00eMsc1NyF56HG49eF5ZU2tT0JWA94Fcppblj1NlOeB9stWNT8md1Tp/lPr/P\nMrVm6ExMAU8qf2+ZoI0bljbe1Kf6m1l+B+He+KYVypWTQLc3ym0O3JlWvrFKe7n9bEr+Un+49fga\n/WO5X5yu16pvRp/6Lh6jvjXZJuSTF/3WU3PeoOtovP0yrPzZjxk7ZfpdwGeBvwV+CtwSeQxfb5+8\nMXBHSulBxhARe5IPWi8nd9/bnvw9cxsTf8cEechJ+/08juXb/jmt53pj2d49QdufxCOPmX42Ih9M\nH9qnve8i9+rovS/IV37a5V5L6+7hEfFUcg+MOeReUE2TXd/9Yre5Dia7L2hrrquHojE+cRXcWf62\nx3r2Pqd+J8oHYeyNzdjD2Jsu3i15NG3GimcFNyt/byh/vwTsFxGvIJ9d27zMG89tLN8h9vMz8g6t\n7ZnAi4G3pJS+0psZEXtNsDzIB9bnk8dl9Ltr242N+jYnjxf8ObBdRLwnpfTJAZbRb5mQdyhX9Hn+\nnkdQp4Zfl2LqJnI8bAr8pk+beu4kf2Ft3qeOzVn+RXhHqW/zZn3lzOzGjXI3ARtGxNqtA4b2cvu5\nnXxQ8R76x/JvB6ijXd815K5c/eqbP8n6Rtlt5IORfuupuV0Puo4G3i/3WUZvOlFiJ6V0H3lc9SGR\nb9r3JvI4sAfJ3UVvAzaKiPXGOcj+K+CqlNLf92aUKxwT/QTU7aUtu5J7WvR7HuDt5B49PTeWti+a\noO2TiZkHyL0OlomIdvsXkq+kHQecxNh3M+21+2DyVaW2ZSfdIvfwOIN8smyfVPoGtuqazPqeyKru\nC/ZgxRNd105y+f1cXtqyNSsmP70rWf2OBQZh7I3N2DP2po3J7Wh6M8vP7EA+m3YPuWsf5PFwN5AT\nuMcA81NKP56gzrPIO5EXpJQubT9ZdjS/6PO69cvfxb0Zke8E+5YB3seZ5K4lV4x35rA4iXwzmV3I\nd3o7IiLmpJQuG2A5TT8hf1bPTil9eYKyD5E/P42+LsXUpeRuX3sD57Xa2Hz9fRHxc2DviJjd++KM\niC2Bl5NPBgFcRN6W9ynvo2cf8pnqcxvl1iHfnKnZzW6F5Y7hTPLZ7etTSrcNUH6Q+t4ALEop/W6c\ncr39xhobpymlpRFxCfngb3ZvfkS8jHz1e36ZNeg6msx+GfrHzr0sj51mW68HjomIt7K86+RZwEHA\nPwDHt19TrE/jO6bYj7z9jueH5APWLVNKc8YqlFK6aoJ6xmr7heSYeSP56kjPX/epYkHjdT17tJZx\nX0RcAGyTUvrlOG35bUTMB7ZOKR05VrniZPIB/0vLPqdtsut7IoNuZ83YXdaulNLlU9CGtgvJidxb\nyFfQevYlJwQT7cv7MvbGZewZe9PG5Hb0BPC2xtiF3cl3RZuVUroHIKWUIuJk8h0C1wX+a4B6jyHf\nOOfsiPgYeef4RPKd8w4cIzAhXwlaAHws8s/nLCZ3vVg6Rvmmw8jdDC+IiOPIXwQbkndCT08p/QNA\nRPwL+cYHr0op3RURB5F/hPqrEfHiyewUUkr3RMS/AcdFHg/8ffINGJ5CHnt4bkqpN2bjCuC1EfED\n8lWyG1NK/bqAarh1KqZSSgsj4ljg4Ii4l3x2+EXA35PPhDdj61DyOPTvRsSnyWfAZ5O316NLfXdG\nxH8BB0XEfeRxO1sBHwEuSCl9r5T7YUT8GPhcRDwR+D35oO2FZVnjxfQx5AOtH0XEMeQzxI8Fngf8\neUpppZ/GmMDJ5Ltkzilt/xX5zPuzyHem3Cul9AA5RgN4V0ScRL6K8quUUvtgbNTNAn4QEd8id+Xb\nlLwdNPdXg66jgfbLxYSxExE/Ab5N3v7vJe+7Xwh8ESClNDcivgEcHflnnOaQY+yVwHdSSueTD9r2\nioijydv7S8kHcb3upn2llK6JiCPJ+/vnkU8WPUAep7YL+caC5431+gHafnZE/Aj4bImZq8hXurbu\nU91Xgc833sM25G28fTXn/eTfYD2L3PXxJnL31xeRbwDT+zmsdwKnR8R65IP728gH0i8HFqSUji3f\nlXuRr+Q8tXSR7Lm6HABPZn0PYtDtrHe19F8j4vvAkpTSz8erOCJ2L3W9kLztzSyf+6KU0pmNcr8H\nrk0p7QqQUloc+WdTjo+IG8n71J3Jn/+7VnF/Yez1YewZe415f0o+5ghyQr1lRLyxPD03pdS7Gj64\ntIp3pPLRnQd5J7q4bCRzyGdcbgRm9ym7FfmOZouBZ/V5/lzgnNa8Tcg//XEDeSe0gPw7kuP+tEbZ\n2M8n74CuI+/Y/46Vbwt/DSvfsfXJ5Du5XV+WeQP5NvN/U57fjnzF9sOt1z2HfGXt+Ma8JX3KbUn/\nu8ztTh7rsbC0+7fkmyo8r1FmR/KXxn20foLFx2g8OhxTQU4+byxtOod8hncp8O5W2d3IVx4WkQ84\nTqNxZ+dGufeST0b14uyTwONaZTYm/1TRXeTuyl8kn6VfArxggvf6J+Sk/+qyjJvJBzTNO5D37qjZ\nvpvjLGBxa96jyF/+V5R9wG3kA4FDWfHu0IeW/cfDtPY5a9KDfGD3m/JZXUo+sJrTXE+DrKNSbtz9\ncmOdLZkodoD/IA8nuZO8z/4Vrbuzku8PcjD5pn4PkLvWfae3HZd4OJw8hv3esv1tQ+s7hT4/R1Lm\nv4XlvXbuJndT/SQT/LzbgG3fmHwyphkze5JjtXnH1gA+RO7qdy/5JNPTS3sPbdX5XHIc3lzW53Xk\nG73t3ir3MnICcDv5e+qa8rqXlee/WOrv99ivUc+g63sxrTuslmVc/Qj2BWsBnyrPLSYfYE+0jV87\nxnu5plXuGlr7pzL/bWUbu5/8nX+gsWfsGXvTG3ss3177PR7RXZejVCxJGmIR8Sbg/8hfBo+oG90j\nXO5x5AOXjVJKD6+u5arbImIW+eTDuqlxt24tu1ncHHJvo/Nrt0ejxdgbm7G3ZrBbsiQNmYjYnnzn\nxYvJZ19fAvw7cOF0JrYRsT/5zO/l5BtL7E7uin2kia00KWPdkEbS9DL2RpzJrSQNn3vJ457+CXgC\n+W6LXwU+ON6LpsAicvflZ5KT22uBg1NKR03zcjWc7Bo2Nj8bTSe3r7H52Yw4uyVLkiRJkobeWrUb\nIEmSJEnSqjK5lSRJkiQNPZNbSZIkSdLQM7mVJEmSJA09k1tJkkZQRBwQEUsj4mmP4LVLI+Kw6WiX\nJEnTxeRWkqTRlPBnLyRJaxCTW0mSJEnS0DO5lSRpGkXE7NLN97kRcWZE3BsRCyLigPL8vhHxm4i4\nJyLmRMQzGq9dJyI+GhHXRsSD5e9HImKd1jKeHhHfjYhFEfHHiDgWWG+M9rw9IuZFxP0RcWtEnBgR\nG07nZyBJ0uqwzsRFJEnSKuh1Df4acALwCeCfgC9ExLOBnYAPAI8CPgmcDOxYXvO/wJuAjwE/Bl4O\nfAh4OvBWgIhYFzibnMy+A7gVOBB4Q7shEXEE8H7gWOBfgaeUureOiJenlOzGLEkaWia3kiRNvwQc\nmVI6GSAifg68Dng7MCOltKjMfzJwbERsATwB2AeYlVL6SKnn7IhYAhweEUeklC4DDgBmADuklC4p\n9ZwJXEpOXinztiQntLNSSh9rzP8dOXHeE/j29Lx9SZKmn92SJUlaPc7s/ZNSWgjcAlzUS2yLK8vf\nLYBXkpPik1v1fBkI8hVfgB2A63uJbak/ka8UN+1aXndKRKzdewCXAPeU5UmSNLS8citJ0upxZ2v6\noTHmATwa2Kj8f1OrzM3lb+/5JwF/7LO89rxNycnt1X3KJmDjPvMlSRoaJreSJHXTHeXv5sC1jfmb\nl7+3l783AVv1ef3mrenbyUnsrsDCPuVv7zNPkqShYbdkSZK66XzyldZ9WvPfSk5SzyvTFwJbRMT2\nvQIREcCbW6/7IbAU2DKl9Is+jwXT8i4kSVpNvHIrSVK3BEBK6fKI+Aowu9wR+Scsv1vyKSmly0v5\nk4CDgNMi4hDyWN5/BB7frDSldE1EHAkcFxHPIyfHDwBPA3YBTkgpnYckSUPK5FaSpOnX7yd20jjz\ne/Ynj5H9W+AQ4EbgP4DDlxVO6eGI2AU4DjgeWAScAnwH+MwKFad0SERcAbyT/HNECbgeOAe4aoC2\nSZLUWeFP2kmSJEmShp1jbiVJkiRJQ8/kVpIkSZI09ExuJUmSJElDz+RWkiRJkjT0TG4lSZIkSUPP\n5FaSJEmSNPRMbiVJkiRJQ8/kVpIkSZI09P4/NpWE1RMAH8UAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa7afeeb0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mean = df_model_lib_mean.reorder_levels(['lib', 'model'])[cuda_level_performance].unstack('lib')\n",
    "std = df_model_lib_std.reorder_levels(['lib', 'model'])[cuda_level_performance].unstack('lib')\n",
    "plot(mean, std)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"plot_models_cublas\"></a>\n",
    "### Only GPU libs using cuBLAS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cublas_libs = ['nvidia-cuda', 'nvidia-fp16-cuda']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xa562abd0>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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clpxdDkuWJCk/yg1L3qLRjekrIralOLHURuCdtTrv/PnzmTlzJgAdHR3MmjWL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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa562a170>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mean = df_model_lib_mean.reorder_levels(['lib', 'model'])[cublas_libs].unstack('lib')\n",
    "std = df_model_lib_std.reorder_levels(['lib', 'model'])[cublas_libs].unstack('lib')\n",
    "plot(mean, std)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "model\n",
       "bvlc-alexnet                0.851548\n",
       "bvlc-googlenet              0.906930\n",
       "deepscale-squeezenet-1.0    0.799011\n",
       "deepscale-squeezenet-1.1    0.837633\n",
       "dtype: float64"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# With cuBLAS, NVIDIA's fp16 branch is up to 20% faster than NVIDIA's fp32 mainline.\n",
    "nvidia_fp16_cuda_vs_nvidia_fp32_cuda = mean['nvidia-fp16-cuda'] / mean['nvidia-cuda']\n",
    "nvidia_fp16_cuda_vs_nvidia_fp32_cuda"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"plot_models_cudnn\"></a>\n",
    "### Only GPU libs using cuDNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cudnn_libs = ['nvidia-cudnn', 'nvidia-fp16-cudnn']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xa5665f90>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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2PQ7bvbqlP7Ztj5f2ihUruqo/toduL168mBUrVvTld8Pp2prbiFhP7RJBg82n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\ne/8DTuFOrmx+bzgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa5642350>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mean = df_model_lib_mean.reorder_levels(['lib', 'model'])[cudnn_libs].unstack('lib')\n",
    "std = df_model_lib_std.reorder_levels(['lib', 'model'])[cudnn_libs].unstack('lib')\n",
    "plot(mean, std)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "model\n",
       "bvlc-alexnet                0.683207\n",
       "bvlc-googlenet              0.650557\n",
       "deepscale-squeezenet-1.0    0.689820\n",
       "deepscale-squeezenet-1.1    0.654235\n",
       "dtype: float64"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# With cuDNN, NVIDIA's fp16 branch is up to 35% (roughly one third) faster than NVIDIA's fp32 mainline.\n",
    "nvidia_fp16_cudnn_vs_nvidia_fp32_cudnn = mean['nvidia-fp16-cudnn'] / mean['nvidia-cudnn']\n",
    "nvidia_fp16_cudnn_vs_nvidia_fp32_cudnn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"plot_models_tensorrt\"></a>\n",
    "### TensorRT vs cuDNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "libs = [ 'nvidia-cudnn', 'nvidia-fp16-cudnn', 'tensorrt-fp32', 'tensorrt-fp16' ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xa59cc550>"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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wgZkzZwIwa9Ys1q9fz4oVK1i7di033HADPXr0AGDChAkMHz6c1atXM3fuXC69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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa57d2230>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mean = df_model_lib_mean.reorder_levels(['lib', 'model'])[libs].unstack('lib')\n",
    "std = df_model_lib_std.reorder_levels(['lib', 'model'])[libs].unstack('lib')\n",
    "plot(mean, std)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "model\n",
       "bvlc-alexnet                0.524858\n",
       "bvlc-googlenet              0.480515\n",
       "deepscale-squeezenet-1.0    0.493366\n",
       "deepscale-squeezenet-1.1    0.528403\n",
       "dtype: float64"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# TensorRT using fp16 is roughly twice as fast as TensorRT using fp32.\n",
    "tensorrt_fp16_vs_tensorrt_fp32 = mean['tensorrt-fp16'] / mean['tensorrt-fp32']\n",
    "tensorrt_fp16_vs_tensorrt_fp32"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "model\n",
       "bvlc-alexnet                0.763825\n",
       "bvlc-googlenet              0.635235\n",
       "deepscale-squeezenet-1.0    0.572533\n",
       "deepscale-squeezenet-1.1    0.464047\n",
       "dtype: float64"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# TensorRT using fp32 is up to 54% faster than cuDNN using fp32.\n",
    "tensorrt_fp32_vs_cudnn_fp32 = mean['tensorrt-fp32'] / mean['nvidia-cudnn']\n",
    "tensorrt_fp32_vs_cudnn_fp32"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "model\n",
       "bvlc-alexnet                0.586791\n",
       "bvlc-googlenet              0.469198\n",
       "deepscale-squeezenet-1.0    0.409481\n",
       "deepscale-squeezenet-1.1    0.374795\n",
       "dtype: float64"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# TensorRT using fp16 is up to 63% faster than cuDNN using fp16.\n",
    "tensorrt_fp16_vs_cudnn_fp16 = mean['tensorrt-fp16'] / mean['nvidia-fp16-cudnn']\n",
    "tensorrt_fp16_vs_cudnn_fp16"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "model\n",
       "bvlc-alexnet                0.400900\n",
       "bvlc-googlenet              0.305240\n",
       "deepscale-squeezenet-1.0    0.282468\n",
       "deepscale-squeezenet-1.1    0.245204\n",
       "dtype: float64"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# TensorRT using fp16 up to 4 times faster than cuDNN using fp32.\n",
    "tensorrt_fp16_vs_cudnn_fp32 = mean['tensorrt-fp16'] / mean['nvidia-cudnn']\n",
    "tensorrt_fp16_vs_cudnn_fp32"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"plot_libs\"></a>\n",
    "## Plot by Caffe libs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"plot_libs_all\"></a>\n",
    "### All"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xa9bc7450>"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Pn8enEake1T0Ivhe4FhgMZAL/MMZEAVhrd1hrf2+t/dhau8la+zfgFiAceKIyPvy+++5j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\nY4wZGP4fGt2dcczpwMIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xabf921b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mean = df_model_lib_mean.unstack('model')\n",
    "std  = df_model_lib_std.unstack('model')\n",
    "plot(mean, std, rot=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"plot_libs_gpu\"></a>\n",
    "### All models, only GPU libs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xa572dc70>"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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P69evL/T61NRUHnjgAa688koiIyP54IMPSrPJXFaqtYlIhYmOjq7oJkgFUeyd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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa56996b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mean = df_model_lib_mean.unstack('model').drop('cpu', axis=0)\n",
    "std  = df_model_lib_std.unstack('model').drop('cpu', axis=0)\n",
    "plot(mean, std, rot=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"plot_libs_alexnet\"></a>\n",
    "### Only models with AlexNet-level accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "alexnet_level_accuracy = ['bvlc-alexnet','deepscale-squeezenet-1.0','deepscale-squeezenet-1.1']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xa7eaf230>"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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NGjSgU6dOEfyKUt3UCY5R2Zk13QI5UtnZ2TXdBDkKys9fys5vys9vys9fL774InPnzmX1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ozNkKIZi5ktk7TK3f9XYC074HP3zxRVl8agYt/Miv3plCp/axWV+o8UJyOmK5YZm2vR+fwSxfv4\nVXE6bFa1n+BrcX3oJGLftQMhGABwqrKK/TGpv2KvqPrh9NqlmHY3IklmdmbaBzsV08znKCqCLymu\nN7vfonP+dEVzpT9z16Z9owgw2dJEn5rZQ4rrrz9UVECmKNbfvVDRXyGzVnEdcraUVVbhP5DGNdr4\nKTeVthPS75yv2D+oOMlwpuK62UVmtl5RsV+gqNgvSQfLgyr2cxSV94OKz99d6nWoLavYZyF4c7rv\nuYpZHCsVn90FipOOWxXXIC9TBOUJVezj1xy9vtlToP5DDazYV8WjT8IxxZTZPZK2eqxkMdXdP1F/\n9/yswk8oGgLsu+YjBAMATgkV+9rtVwSfYrfRHxRV+CslyWPd3hWKqXqPKhplZRW8LGDtVjROujp7\ncnd/18yuUezj44owtcX7OxYXOxiP4f1NBjsTgHOo2GMMjzVm3y787IQ0tmM4IWq4sO+ajRAMAECz\nZY3Esmuys4OtfYqmK5fl7vu+IhDdm7YfkfqaEP0o6RX1r+Mqd/9OJVPYc82bcHJU7FFqnIZHvPdD\njn3XXIRgAACa7bii0/3ctJ1VZH9ThKHrsjumILVcsYzSXhWWfEthaHHZi1hvKSL37nTwnUxU7FGK\nz1Fzse+aixAMAECDpesvd0qab2Znu3u29u4/ZvatpPOyn6f771BaJ7ZMtuZk8eCOQHTKqNgDwJAg\nBAMA0HzbFcHofOU6+br7E4MeYGbTJJ0o6frLmpOnBxV7ABgShGAAABrO3T+QdJE0tgtpsXNv7jHj\nTovF5KJiDwDDgxAMAEALpFBkNGgZalTsAWAIGB27AQAAqjXRij0AYPIRggEAACoyqGIPAKgOIRgA\nAAAA0BlT6h4AAAAAAABVIQQDAAAAADqDEAwAAAAA6AxCMAAAAACgMwjBAAAAAIDOIAQDANACZva0\nmY3ktkfMbEnx9rQeLQAAnTWt7gEAAIBJ4ekrc62kn8a5HQCATiIEAwDQQu7+Rd1jAABgGDElCgCA\nFipOh865wsw+NrOjZvazmT1T+eAAAKgRIRgAgO4wSeslbZB0i6Q1kp4cEJYBAGglpkMDANAdLmm1\nuy9P2xvNbKakh83seXf/q8axAQBQCSrBAAB0y7rC9muSZki6qoaxAABQOUIwAADd8mvJtkm6oIax\nAABQOUIwAADdMqtk2yXtq2EsAABUjhAMAEC33FbYvl3SEUnbahgLAACVozEWAADdYZLuMbOpkrZI\nulHSfElPufvhWkcGAEBFqAQDANAeXvjeC7ePKJZGukHSW5LmSXrW3ZdWMzwAAOpn7sX/HwEAAAAA\naCcqwQAAAACAziAEAwAAAAA6gxAMAAAAAOgMQjAAAAAAoDMIwQAAAACAziAEAwAAAAA6gxAMAAAA\nAOgMQjAAAAAAoDP+BR5ncxKU9FMdAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa56ade90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# On this platform with all the libraries, SqueezeNet 1.0 is always slower than AlexNet\n",
    "# despite a 50x reduction in weights (5 MB vs. 250 MB).\n",
    "mean = df_model_lib_mean[alexnet_level_accuracy].unstack('model')\n",
    "std  = df_model_lib_std[alexnet_level_accuracy].unstack('model')\n",
    "plot(mean, std, rot=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"plot_libs_alexnet_cpu\"></a>\n",
    "### Only models with AlexNet-level accuracy, only CPU lib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xa4e82e50>"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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PPZTp06czadIkVq9eTZMmTdh5551zCy2NHDmSyy67jHfffReAN998k8WLF7N8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\nOAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa4e8c650>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# SqueezeNet 1.1 is 46% faster than AlexNet with OpenBLAS (on the CPU).\n",
    "mean = df_model_lib_mean[alexnet_level_accuracy].unstack('model').ix[['cpu']]\n",
    "std  = df_model_lib_std[alexnet_level_accuracy].unstack('model').ix[['cpu']]\n",
    "plot(mean, std)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "lib\n",
       "cpu    0.540034\n",
       "dtype: float64"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean['deepscale-squeezenet-1.1'] / mean['bvlc-alexnet']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"plot_libs_alexnet_cuda\"></a>\n",
    "### Only models with AlexNet-level accuracy, only libs with CUDA-level performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xa4e45430>"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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w7rvvAvDmm2+yatUq1q5dy6677kq7du3YuHEjP/nJT2pNvs444wwef/xxnnnm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\niB23hwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa4c1e950>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# SqueezeNet 1.0 is slower than AlexNet. SqueezeNet 1.1 is 28% faster than AlexNet with \n",
    "# libDNN-CUDA, and roughly equivalent to AlexNet with cuBLAS and cuDNN.\n",
    "mean = df_model_lib_mean[alexnet_level_accuracy].unstack('model').ix[cuda_level_performance]\n",
    "std  = df_model_lib_std[alexnet_level_accuracy].unstack('model').ix[cuda_level_performance]\n",
    "plot(mean, std)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "lib\n",
       "nvidia-cuda     0.977500\n",
       "nvidia-cudnn    1.033459\n",
       "libdnn-cuda     0.726139\n",
       "dtype: float64"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean['deepscale-squeezenet-1.1'] / mean['bvlc-alexnet']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"plot_libs_alexnet_fp16\"></a>\n",
    "### Only models with AlexNet-level accuracy, only fp16 libs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xa4c1e1d0>"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ddx7HHHMM3bt3p1+/fru0e99997F161Y6d+5M48aNGThwYHIYdP/+/bnyyisZ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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa4c328f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# SqueezeNet 1.1 achieves > 500 inferences per second with TensorRT using fp16.\n",
    "mean = df_model_lib_mean[alexnet_level_accuracy].unstack('model').ix[['nvidia-fp16-cudnn', 'tensorrt-fp16']]\n",
    "std  = df_model_lib_std[alexnet_level_accuracy].unstack('model').ix[['nvidia-fp16-cudnn', 'tensorrt-fp16']]\n",
    "plot(mean, std)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "lib\n",
       "nvidia-fp16-cudnn    0.989633\n",
       "tensorrt-fp16        0.632098\n",
       "dtype: float64"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean['deepscale-squeezenet-1.1'] / mean['bvlc-alexnet']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"plot_libs_googlenet_fp16\"></a>\n",
    "### Only GoogleNet, only fp16 libs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xa498feb0>"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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iG5iR2+4ycgvtMHILAHTFXCO3Y7ug1JaS3HaX5BbaIbkFALpiQbcCKqXsvrXP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\nNTMzMzMzs573//EBAcoGqT54AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa5190730>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# GoogleNet achieves nearly 200 inferences per second with TensorRT using fp16.\n",
    "mean = df_model_lib_mean[['bvlc-googlenet']].unstack('model').ix[['nvidia-fp16-cudnn', 'tensorrt-fp16']]\n",
    "std  = df_model_lib_std[['bvlc-googlenet']].unstack('model').ix[['nvidia-fp16-cudnn', 'tensorrt-fp16']]\n",
    "plot(mean, std)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "model\n",
       "bvlc-googlenet    0.469198\n",
       "dtype: float64"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# TensorRT using fp16 is more than twice as fast than cuDNN using fp16.\n",
    "mean.ix['tensorrt-fp16'] / mean.ix['nvidia-fp16-cudnn']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "lib\n",
       "nvidia-fp16-cudnn     89.840365\n",
       "tensorrt-fp16        191.476509\n",
       "Name: bvlc-googlenet, dtype: float64"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "images_per_second(mean['bvlc-googlenet'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<img src=\"https://devblogs.nvidia.com/parallelforall/wp-content/uploads/2016/09/Figure_2-1.png\"/>"
      ],
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Our results (with the batch size of 16) are very close to NVIDIA's results (with the batch size of 64).\n",
    "Image(url=\"https://devblogs.nvidia.com/parallelforall/wp-content/uploads/2016/09/Figure_2-1.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"plot_per_layer\"></a>\n",
    "## Plot execution time per image per layer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df_per_layer_info = get_per_layer_info(df_all)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# pd.options.display.max_columns = len(df_per_layer_info.columns)\n",
    "# pd.options.display.max_rows = len(df_per_layer_info.index)\n",
    "# df_per_layer_info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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tbUREhK1Vq5bt1auXPXLkiLXW2nfffdfWqVPHRkRE2KlTp9qsrCwbFhZWYD5w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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa420c830>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot for a list of batch sizes.\n",
    "# NB: This suggests that the fully connected layers benefit the most from larger batch sizes.\n",
    "plot_time_per_image_per_layer(df_per_layer_info, model='bvlc-alexnet', libs='nvidia-cuda',\n",
    "                              batch_sizes=[2, 8, 16], direction=direction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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/qyIi5aeEN/aU8Ir4J1LCqxpeERERERERSUhKeEVEwuheeZII1I5FROKH+mR/\nKeEVERERERGRhKQa3irw+UUqO/2sioiUn2p4Y081vCL+UQ2viIiIiIiIVClKeEVEwqjORhKB2rGI\nSPxQn+wvJbxxbPz48bzwwgsArFq1iiZNmpTpOpMmTWLw4MHRDA2Ayy67jBkzZkT9uvGqd+/ezJkz\np9h9WVlZVKtWjby8vJMeW1EKxxSvxo4dy8svv+x3GCIiIiKSgJTwFtKsWUPMLGaPZs0aeopj//79\nzJkzhxEjRoReMysyJd2z8pwbz5o3b87bb79dIe+1dOnSEv9wEP4dn+zYilJR/+6/+c1v6NKlC6ed\ndhq33nprkf2HDx/mzjvvpF69eqSmpha4+frYsWOZPHkyx48fr5BYT0Y3hpdEoHYsIhI/1Cf7q4bf\nAcSbrKyvY7o4gtnXno6bOXMmvXv3pmbNmrELRiRKzjrrLB588EH++te/cvjw4SL7hw8fTl5eHp9+\n+impqals2LAhtK9hw4ace+65/PnPf+b666+vyLBFREREJMFphDdOLVu2jB49ehR4zTnH448/Tr16\n9WjRogWvvfYaAGvXrqVRo0YFVgVctGgR7du3L/ba7777Lpdccgmpqamkp6cze/bsYo/Lycmhb9++\n1K9fnzp16tC3b192794dMeYZM2Zw3nnnUadOHa6++mp27NgBwHvvvUe9evVC527cuJG0tDQ+++wz\nIDBKO2XKFNq1a0dqaioDBw7khx9+CF138eLFdOjQgdTUVLp168amTZsAGDJkCDt27KBv374kJyfz\nzDPPlPidQmB694033sjQoUNJTk6mTZs2rF+/HoCnnnqKfv36FTh+1KhRjB49Gig4hTsvL4+xY8dS\nr149WrVqxZIlSwqcF37stm3b6NmzJ3Xr1qV+/frcfPPNHDp0KGKMeXl5TJ48mVatWpGcnEyXLl3Y\nvXt3sVOUSxvTQw89RLdu3UhOTuaqq64iOzsb+HH68+zZs0lPT6d+/fpMnjz5pN9nvmuvvZZrrrmG\ntLS0Ivs+/fRTFi9ezLRp00hLS8PM6NChQ4FjevToUSRev6jORhKB2rGISPxQn+wvJbxxatOmTZxz\nzjkFXtu7dy/Z2dns2bOHmTNncvvtt7N161YuuOACateuXWBq77x587jpppuKXDcrK4vevXszatQo\n9u/fz4YNGyImxnl5edx6663s3LmTHTt2UKtWLe6+++5ij33zzTd54okneOONN9i3bx/du3dn4MCB\nAFx00UWhOLwfAAAgAElEQVSMHDmSoUOHcuTIEQYPHsxjjz1G69atQ+fPnz+fFStW8OWXX7Jx40Zm\nzpwJwEcffcRtt93G9OnTyc7OZsSIEVxzzTUcO3aM2bNn07RpUxYvXsyhQ4cYO3asp+/2L3/5C4MG\nDeLgwYP07duXu+66C4ABAwawbNkyvvvuu9Dnnz9/frHf47Rp01i6dCkbN27kww8/ZMGCBRHfzznH\n+PHj2bt3L1u2bGHXrl1MnDgx4vFTpkzh9ddfZ/ny5Rw6dIgZM2ZQq1YtoOQpyl5imjdvHrNmzWLf\nvn0cPXq0yB8J1qxZw9atW3nrrbd4+OGH+fTTTyO+n1dr164lPT2dhx56iHr16tGuXTsWLlxY4Jhz\nzz2XjRs3lvu9RERERETCKeGNUzk5OSQlJRV4zcx45JFHOOWUU7j00kvp06cPf/zjH4FAspY/4pub\nm8vSpUtDCWe4efPmccUVV9C/f3+qV69Oamoqbdu2LTaGtLQ0rrvuOmrWrMkZZ5zBuHHjWL16dbHH\nTp06lXHjxtG6dWuqVavG/fffz4YNG9i5cycAEyZMICcnhwsuuIAmTZpwxx13FDh/1KhRNGjQgJSU\nFPr27Rua8jp9+nRGjhxJ586dMTMGDx5MzZo1ef/990PnlvZ+d926daNXr16h63388ccANG3alI4d\nO7Jo0SIAVq5cyRlnnEGXLl2KXGP+/PmMHj2axo0bk5KSwrhx4yK+X8uWLenZsyc1atSgTp063Hvv\nvaxatSri8a+88gqPPfYYrVq1AqBNmzakpqae9HN5iWnYsGG0bNmSmjVr0r9//wJTi82MiRMncuqp\np9K2bVvatWsXlSR0165dbNq0idTUVL766itefPFFhg4dWiCZTkpKIicnp9zvFQ2qs5FEoHYsIhI/\n1Cf7SwlvnEpNTSU3N7fIa6eddlpoOz09nT179gAwaNAgFi1axLFjx1i4cCGdOnXi7LPPLnLdnTt3\n0rJly2JfT0pKIikpieTkZCCw0NCIESNo1qwZKSkp9OjRg5ycnGITzKysLEaNGkVaWhppaWnUqVMH\nMwtNY65Rowa33HILmzdvZsyYMUXOb9CgQeh5rVq1+Pbbb0PXnTJlSui6qamp7Nq1K/S5y6Jhwx8X\nDqtVqxZHjhwJTRMeOHAg8+bNAwJ/HBg0aFCx19izZ0+BVbPT09Mjvt8333zDwIEDOfvss0lJSeHm\nm29m//79ALz22muh77xPnz5A4N+iRYsWpf5cXmIq/Nnzv+d8kf4dyuP000/n1FNP5YEHHqBGjRpc\neumlXHbZZaxYsSJ0TG5uLikpKeV+LxERERGRcEp441Tbtm1DNa75Dhw4UGBBoB07dtC4cWMgMCU0\nPT2dpUuXlpioNWnShM8//7zY13Nzc8nNzQ3Vl06ZMoWtW7fywQcfkJOTExrdLS7hbdKkCVOnTiU7\nO5vs7GwOHDjAt99+S9euXQHYvXs3kyZNYtiwYYwZM4Zjx455+h6aNGnCr3/96yLXvfHGG4Hor0Lc\nr18/MjMz2b17N4sWLYr4PTZq1Cg0eg2BxDyS8ePHU61aNTZv3kxOTg6vvvpq6DscNGhQ6DvPr2Ft\n2rQpX3zxRZHrnHHGGQB8//33odf27t1bppgqUv4MgvB2U/jfbcuWLbRr165C44pEdTaSCNSORUTi\nh/pkfynhjVO9e/cu8sPhnGPChAkcO3aMd955hyVLlhRYZGnQoEE8//zzvPPOO0UWX8p30003sXLl\nShYsWMCJEyfIzs6OOG01NzeX008/neTkZLKzs0usOx05ciSTJ0/mX//6FwAHDx4sUEM6bNgwhg8f\nzu9+9zsaN27MAw884Ol7GD58OC+//DJr164F4LvvvmPp0qWhOtsGDRqwbdu2Auc0b9484kJcxQlP\nxOrWrUuPHj0YNmwYLVq0KFJHna9///688MIL7N69mwMHDvDkk09GvH5ubi61a9cmKSmJ3bt38/TT\nT5cYz2233caDDz4Y+sPEpk2bOHDgAHXr1uWss87i1VdfJS8vjxkzZhRIjEsTU3FKmhqemZlJtWqR\nu4sTJ05w5MgRTpw4wfHjxzl69CgnTpwA4NJLL6Vp06Y8/vjjnDhxgjVr1pCZmUmvXr1C569atYqr\nr766VPGKiIiIiJyMEt5C0tMbYEbMHunpDU4eBIEViJctW8bRo0dDrzVq1IjU1FQaN27M4MGDmTp1\naoGFnwYMGMDq1avp2bNnsavlQmDEdOnSpTzzzDOkpaXRoUOHUA1rYaNHj+b777+nbt26XHzxxfTu\n3bvA/vBRumuvvZb777+fAQMGkJKSQtu2bVm+fDkAL7zwAvv27ePhhx8GAqs5z5w5kzVr1hS5TmGd\nOnVi+vTp3H333aSlpdG6dWtmzZoV2j9u3DgeeeQR0tLSePbZZzl27BjZ2dmhkWUvCr//oEGDWLly\nZZHFqsKPGz58OL169aJdu3Z07tyZG264IeKxEyZMYN26daH65MLHFjZmzBj69+/PlVdeyZlnnsnP\nf/7z0Mj+tGnTeOqpp6hbty5btmzhkksuKVNMXr6H8O1du3YVeK/CHn30UWrVqsWTTz7J3LlzqVWr\nFo899hgQmM7+5ptvsmTJElJSUhgxYgRz5swJtd2vvvqKLVu2cO2115YYX0VRnY0kArVjEZH4oT7Z\nX1baBX8qIzNzxX1OMyv1gkcV6YEHHqB+/frcc889fodSaaxZs4aXXnqJuXPn+h1KQrn99tvp168f\nV1xxRdSvPXbsWFq1asXIkSMjHhPvP6siIpWBmVHZelIDKlP3b1b6xTRFJDqCvy8WGeFRwlsFPr9I\nZVeRP6uZmZn6S6xUemrHUhwlvLGnhFeKoz65YkRKeDWlWURERERERBKSRnirwOcXqez0syoiUn4a\n4Y09jfCK+EcjvCIiIiIiIlKlKOEVEQmje+VJIlA7FhGJH+qT/VXDy0FmVhO4COgKNAZOB/YDnwKr\nnXPbSjhdREREREREpMKVWMNrZq2A0cBNwJlAHnAQOAykAacBDlgHvATMds7lxTjmUotUw9usWTOy\nsrJ8iEhESiM9PZ3t27f7HYaISKWmGt7YUw2viH9KfVsiM/sNMBz4CPgDsBrY6Jw7HnZMAwKjvn2A\nnwFfA7c45/4R9U9QDpESXhEREZGqQglv7CnhFfFPWRatagxc4Jy70Dn3nHNuXXiyC+Cc+9o596Zz\n7nagEfBboF1UI5dKQbUJkijUliURqB2LiMQP9cn+iljD65y7rjQXcs4dBV4od0QiIiIiIiIiUeDL\nfXjN7CzgfqATgRHh04FmzrkdHs7dDjQt9LIDrnPO/TnCOZrSLCIiIlWapjTHnqY0i/inXPfhNbOf\nmtmwsO10M3vPzHLNbIGZ1S5lPK0I1PxmE6gNLk3P4IDlBGqH8x8XAatKGYOIiIiIiIgkMK/34X0A\nqBe2/SxwNjANuBSYWJo3dc6tcs41cs79N7CgNOcG7XfOrS30OFiG60iUqDZBEoXasiQCtWMRkfih\nPtlfXhPelsDHAGZ2OtAbGOOc+x9gPFCqel8RERERERGRWPNUw2tm3wNXO+dWmVlPAlOK6zrnDppZ\nd2CFc+70MgVgdhuBkeLmHmt4vwRSgFOB6gRum/SEc+7NEs5RDa+IiIhUaarhjT3V8Ir4p1w1vMB2\noFvw+U+BdWFTiOsDFTmd+M/AL4ArgUHAYWCRmQ2qwBhEREREREQkznlNeKcCE83sQ+BO4JWwfRcB\n/4p2YJE450Y55151zq1xzi0E/gv4EHi8omKQolSbIIlCbVkSgdqxiEj8UJ/sr4j34Q3nnHvezPYT\nWBH5Befc7LDdScDMGMTmiXMuz8zmA0+YWQPn3NfFHXfLLbfQrFkzAFJSUmjfvj0ZGRnAj41Q22Xf\n3rBhQ1zFo21tl3V7w4YNcRWPtrVdlu188RKPtuNjGyATyAh7TiXYzpffvPM/Trxu/xhvZnB/hra1\nre0YbG/YsIGcnBwAtm/fTiS+3Ie3QAClrOGNcI1fAk8AjZxz3xSzXzW8IiIiUqWphjf2VMMr4p9I\nNbyeRnjDLmJAI+C0wvucc9vKHl7ZmVl1YACwo7hkV0RERERERKqmal4OMrM6ZvYH4AiwE9hazKNU\nzOwGM7sB6EzgD3i9g69dGnbMcTObHrY9wMzmmdlgM8swswEEZry0B35V2hgkevKnGYhUdmrLkgjU\njkVE4of6ZH95HeF9BbgM+D/gE+CHKLz3fAjNrHHAb4LPVwGXB58bBZPyL4F6wFNAGvAdgQWrejnn\n3opCTCIiIiIiIpIgvN6H9yAwyjk3M+YRxYBqeEVERKSqUw1v7KmGV8Q/5b0PbzZQ7OrHIiIiIiIi\nIvHIa8L7IjAyuGiVSBGqTZBEobYsiUDtWEQkfqhP9pfX+/A+a2aNgX+Z2VvAgaKHuAlRj05ERERE\nRESkjLzW8PYG/gTUjHCIc85Vj2Zg0aQaXhEREanqVMMbe6rhFfFPeWt4nwU+ANoBNZ1z1Qo94jbZ\nFRERERERkarJa8LbFHjUObfJOXcslgFJ5aTaBEkUasuSCNSORUTih/pkf3lNeD8CGscyEBERERER\nEZFo8lrD2wmYBYxwzq2JeVRRphpeERERqepUwxt7quEV8U+kGl6vCe9OIBmoDXwH5BQ6xDnn0qMR\naCwo4RUREZGqTglv7CnhFfFPeRetWgksBGYTWK15ZaHH21GKUyop1SZIolBblkSgdiwiEj/UJ/vL\n6314b4lxHCIiIiIiIiJR5WlKc2WnKc0iIiJS1WlKc+xpSrOIf0o9pdnMri/DmzQys66lPU9ERERE\nREQk2kqq4X3RzDaY2UgzSyvpImbW3cymAZ8DbaMaoVQKqk2QRKG2LIlA7VhEJH6oT/ZXSTW8/wGM\nBR4mkPxuATYC+4CjQCrQAugMnAmsBq5wzv09phGLiIiIiIiIeHDSGl4zOxW4DugFdAUaA6cB/wY+\nIZDovu6c+yS2oZadanhFRESkqlMNb+yphlfEP+W6D29lp4RXREREqjolvLGnhFfEP+W9D69IiVSb\nIIlCbVkSgdqxiEj8UJ/sLyW8IiIiIiIikpA0pVlERESkCtCU5tjTlGYR/2hKs4iIiIiIiFQpSngl\nKlSbIIlCbVkSgdqxiEj8UJ/sr5MmvGZ2qpmNMrOfVERAIiIiIiIiItHgqYbXzA4DvZxzq2MfUvSp\nhldERESqOtXwxp5qeEX8U94a3i1Ai+iGJCIiIiIiIhI7XhPeh4AHzaxNLIORyku1CZIo1JYlEagd\ni4jED/XJ/qrh8bj7gNrAR2a2HfgKCsyKcc65HlGOTURERERERKTMvNbwZkLJZR/OucuiFFPUqYZX\nREREqjrV8MaeanhF/BOphtdTwlvZKeEVERGRqk4Jb+wp4RXxT3kXrRIpkWoTJFGoLUsiUDsWEYkf\n6pP95TnhNbOzzOxZM/vQzL7Mvy+vmY02swtjF6KIiIiIiIhI6Xmt4T0feAc4AbwH9AG6OOfWm9lz\nQAPn3KCYRloOmtIsIiIiVZ2mNMeepjSL+Ke8U5qnELgXb3PgegL9T76/A13LHaGIiIiIiIhIFHlN\neLsBTzjnvqXoas1fAw2jGpVUOqpNkEShtiyJQO1YRCR+qE/2l9eEN6+EfXWBw1GIRURERERERCRq\nvNbwvgUccs5db2bVgWNA52AN7x+AWs65a2Ica5mphldERESqOtXwxp5qeEX8E6mGt4bH8x8B3jKz\nFcBrBKY1/5eZjQKuAy6NWqQiIiIiIiIiUeBpSrNzbhVwLYFFq2YQ+IPbE0B34Frn3D9iFqFUCqpN\nkEShtiyJQO1YRCR+qE/2l9cRXpxzS4AlZtYKqA/82zn3acwiExERERERESkHrzW8rZ1zn1VAPDGh\nGl4RERGp6lTDG3uq4RXxT3nvw/uJma00s35m5nlUWERERERERMQvXhPeW4HTgdeBXWY22cyaxy4s\nqWxUmyCJQm1ZEoHasYhI/FCf7C+vi1bNdM5dDLQH/gTcCWw1s+Vm9lMz85o4i4iIiIiIiFQITzW8\nRU4yqwUMBO4G2gJfAb8Dfuuc+zqqEUaBanhFRESkqlMNb+yphlfEP+Wt4S2sGYFEtxnwA/BPYAzw\nuZldV8ZrioiIiIiIiESN54TXzE41s5vMbDWwCehL4F68TZxzVwHpwHLg2ZhEKnFNtQmSKNSWJRGo\nHYuIxA/1yf7ytOKymU0BhgCpwF+Ba4Cl4fOEnXMHzOx5YHUsAhUREREREREpDa/34f0G+D3wsnPu\nyxKOqwv0cc7Nil6I5acaXhEREanqVMMbe6rhFfFPpBperwnvqc65H2ISWQVQwisiIiJVnRLe2FPC\nK+Kfci1aVZmTXakYqk2QRKG2LIlA7VhEJH6oT/aXpxpeADO7ErgDOAc4rdBu55xrGc3ARERERERE\nRMrD65Tm3sBfgLeAKwisxlwLuATIAt5xzg2LYZzloinNIiIiUtVpSnPsaUqziH/Kex/eB4HfAL2D\n2w845zKA84HqwLJoBCkiIiIiIiISLV4T3v8kMMKbBziCU6Gdc58BEwkkxFKFqTZBEoXasiQCtWMR\nkfihPtlfXhPePOB4cF7wPqBp2L49gOp3RUREREREJK54reH9OzDdOfd7M1sMpAD9gOPAHKCpc+68\nmEZaDqrhFRERkapONbyxpxpeEf9EquH1ukrzXODc4PMJBBav2hXcPgEMKneEIiIiIiIiIlHk9T68\nv3HO/Sr4fB3QBhgB3Au0d84tiF2IUhmoNkEShdqyJAK1YxGR+KE+2V+e78Mbzjm3C/hdlGMRERER\nERERiRpPNbyVnWp4RUREpKpTDW/sqYZXxD+lruE1s/xbEHnhnHNlGi0WERERERERiYWSktSH8Z7w\nShWXmZlJRkaG32FIAsvMzAzVwIS3t4yMjKi2PbVlSQRqxyIi8UN9sr8iJrzOuYkVGIeISInCE1sz\n0wIQIiIiInJSquEVkUonWKPhdxgiIpWKanhjTzW8Iv6JVMPr6bZEIiIiIiIiIpWNEl6JCk0vleI0\na9gQM4v6A4jJdc2MhmlpPn9rIuWnPllEJH6oT/aXVlYWkZjJ+vrrmEyfM2K3op4dOBCjK4uIiIhI\nRVMNr4jETKzqxWKa8KL6KxFJTKrhjT3V8Ir4p8w1vGZ2qpmtN7MrYxOaiIiIiIiISPSdNOF1zv0A\nNAeOxz4cqaxUmyAiEj/UJ4uIxA/1yf7yWsP7/4ArgbdjGIuISESZwQdAD2Bi8HlG8CEiIiIiUpin\nGl4z6w68CswH3gC+olAJnXNuWywCjAbV8Ir4o/LWi1W2qEVETq7y9sl+R+GdanhF/BOphtdrwpsX\ntlnsCc656mUPL7aU8Ir4o/L+clXZohYRObnK2yf7HYV3SnhF/BMp4fU6pXlYlOORBJOZmUlGRobf\nYYjElczMzFDdTvjPSEZGhn5eJKbUJ4uIxA/1yf7ylPA652bFOhARkXhwyimBvxDGwqpVqwCYNGlS\n1K6Znt6A7dv3Ru16IiIiIomkVPfhNbNqwHlAHeBD59x3sQosmjSlWcQfVX36XGZm4JH/PP+PuxkZ\nPz4vL02fExGvqnqfXBHUJ4v4p1w1vMEL3AVMAOoSqOPt4pxbb2ZvAG87516IZsDRpIRXxB/65Sr2\n9MuViHilPjn21CeL+CdSwnvS+/AGTx4OPE9gheb+BPqffO8AN0QjSKm8dH8xEZH4oT5ZRCR+qE/2\nl6eEFxgDTHHO3Q4sKrTvE+CcqEYlIiIiIiIiUk5eb0t0BOjtnHvbzKoDx4DOwSnNGcBy59xpsQ21\n7DSlWcQfmj4Xe5o+JyJeqU+OPfXJIv4p15RmYD/QLMK+c4DdZYxLREREREREJCa8JryLgYfMrEXY\na87M6gL3EqjtlSpMtQkiIvFDfbKISPxQn+wvrwnvA8BR4J/AWwRWaX4B2AKcAB6OSXQiIiIiIiIi\nZVSa2xIlAaOBXkB94N/AcuA559yhmEUYBarhFfGH6sViT/ViIuKV+uTYU58s4p9y34e3MlPCK+IP\n/XIVe/rlSkS8Up8ce+qTRfxT3kWr8i+SbGYXm1k/M7soOOorotoEEZE4oj5ZRCR+qE/2Vw2vB5rZ\nQ8D/ALUJ/MENINfMnnbOPRqL4ERERERERETKyut9eCcBDwK/A/4AfA00AAYCtwKPOOcmxi7M8tGU\nZhF/aPpc7Gn6nIh4pT459tQni/inXDW8ZrYHmOuc+2Ux+54BBjnnGkcl0hhQwiviD/1yFXv65UpE\nvFKfHHvqk0X8U94a3jOBv0bYtzy4X6ow1SaIiMQP9ckiIvFDfbK/vCa8/wC6RNjXJbhfRERERERE\nJG54ndL8E2ARMA2Yz481vP2B4cBPgU/yj3fO5cUi2LLSlGYRf2j6XOxp+pyIeKU+OfbUJ4v4p7w1\nvPkJbHEHW6HXnXPO8+rPFUEJr4g/9MtV7OmXKxHxSn1y7KlPFvFPpITXa2L6MMUnuyJAoDYhIyPD\n7zBERAT1ySIi8UR9sr88JbzxfMshERERERERkeJ4mtJc2WlKs4g/NH0u9jR9TkS8Up8ce+qTRfxT\n3tsSiYiIiIiIiFQqSnglKnR/MRGR+KE+WUQkfqhP9pcSXhEREREREUlIquEVkZhRvVjsqV5MRLxS\nnxx76pNF/KMaXhEREREREalSPCe8ZtbBzBaa2X4zO25mHYOvTzazq2IXolQGqk0QEYkf6pNFROKH\n+mR/eUp4zawb8B7wn8Brhc7LA0ZGPzQRERERERGRsvNUw2tm7wL/Bq4FqgM/AJ2dc+vN7Hrgf51z\nTWMaaTmohlfEH6oXiz3Vi4mIV+qTY099soh/ItXw1vB4fkfgeuecM7PCP8X7gXrlDVBEREREREQk\nmrzW8B4BakXY1wg4GJ1wpLJSbYKISPxQnywiEj/UJ/vLa8L7LjDazKqHvZY/0nsb8HZUoxIRERER\nEREpJ681vO2ANcB2YAHwIPAi0A7oBHRxzn0auzDLRzW8Iv5QvVjsqV5MRLxSnxx76pNF/FOu+/A6\n5zYClwJfA78m0P/cHdzdI56TXREREREREamaPN+H1zm33jnXE0gCzgaSnXOXOec+ill0UmmoNkFE\nJH6oTxYRiR/qk/3ldZXmEOfcEWBPDGIRERERERERiRpPNbwAZnYu8DOgCXBaod3OOTfU85uanQXc\nT6D+tx1wOtDMObfDw7kWPPd2oCHwKfCwc25hCeeohlfEB6oXiz3Vi4mIV+qTY099soh/ynUfXjMb\nAswgsDLzN8APhQ4p7U92KwLJ8zpgNXBlKc59FBgDjAfWAwOA+WbWxzm3vJRxiIiIiIiISILyWsP7\nIPAmUM85d5ZzrnmhR4vSvKlzbpVzrpFz7r8JrPrsiZnVA/4HeNw591zwOncAfwOeKE0MEl2qTRAR\niR/qk0VE4of6ZH95TXgbAi8553JiGYwHVwGnAHMLvf4q0MbM0is+JBEREREREYlHXhPeNcC5sQzE\no/OAo865Lwq9vplAmcd5FR+SAGRkZPgdgoiIBKlPFhGJH+qT/eV1lea7gYVm9m9gBXCg8AHOubxo\nBhZBGlDcKHN22H4RERERERERzyO8u4CPCEwd/gY4VuhReBErqWJUmyAiEj/UJ4uIxA/1yf7yOsI7\nHbgReAP4BP8S3ANASjGv54/sZhezD4BbbrmFZs2aAZCSkkL79u1D0wvyG6G2y769YcOGuIpH23G0\nTUBG8L/xvg2QmQn5s4/y/x8Vr9uB1zLj599b23GxnS9e4tF2fGxDoM/LCHtOJdjO53d/63X7x3gz\ng/sztK1tbcdge8OGDeTkBCb/bt++nUg83YfXzA4BDzrnnj/pwaVkZrcB04DmJ7sPr5kNBmYC/+Gc\n2xb2+i3AK0AL51xWMefpPrwiPtA9H2NP93wUEa/UJ8ee+mQR/0S6D281j+d/B/wruiGVyXLgOHBT\noddvBv5ZXLIrIiIiIiIiVZPXhPf3wKBovrGZ3WBmNwCdCfwBr3fwtUvDjjluZtPzt51z+4BngXFm\ndq+Z9TCz3xKY/XJ/NOOT0smfZiAiIv5TnywiEj/UJ/vLaw1vFjDQzP4fgVHW4lZpnlHK954PoZk1\nDvhN8Pkq4PLgc6NoUj4eyAXuIXB/4E+Bfs65ZaV8fxEREREREUlgXmt4T3bLIeecqx6dkKJPNbwi\n/lC9WOypXkxEvFKfHHvqk0X8E6mG1+sIb/MoxyMiIiIiIiISU55qeJ1zWSd7xDpQiW+qTRARiR/q\nk0VE4of6ZH95XbRKREREREREpFKJWMNrZtuA65xzG83sSyix7MM551rGIsBoUA2viD9ULxZ7qhcT\nEa/UJ8ee+mQR/5SlhncVcCjsuX56RUREREREpNLwtEpzZacR3tjLzMwkIyPD7zAkzmg0IfY0miDF\nUZ8sxVGfHHvqk6U46pMrRqQRXk81vGb2kJk1jrCvkZk9VN4ARURERERERKLJ6314TwAXOefWFrOv\nE7BW9+EVkcI0mhB7Gk0QEa/UJ8ee+mQR/5RrhJdAfxNJKnC0TFGJiIiIiIiIxEjERavMLAO4POyl\nEWb234UOOx3oA2yOfmhSmag2QUQkfqhPFhGJH+qT/VXSKs09gAeCzx0wrJhjfgD+BdwT5bhERERE\nREREysVrDW8e0LW4Gt7KQDW8Iv5QvVjsqV5MRLxSnxx76pNF/FOW+/CGOOe81vqKiIiIiIiIxAUl\nshIVmZmZfocgIiJB6pNFROKH+mR/KeEVERERERGRhOSphreyUw2viD9ULxZ7qhcTEa/UJ8ee+mQR\n/5T3PrwiIiIiIiIilYoSXokK1SaIiMQP9ckiIvFDfbK/PCe8FnCNmT1jZr83s/Tg6z3MrHHsQhQR\nEREREREpPa/34U0FlgIXArlAbaCLc269mb0KZDvn7olppOWgGl4Rf6heLPZULyYiXqlPjj31ySL+\nKW8N79NAE+ASoA6B/iffW0DPckcoIiIiIiIiEkVeE96fAr92zr0HRf44uINAMixVmGoTRETih/pk\nESj1GowAACAASURBVJH4oT7ZX14T3trA7gj7TqPgiK+IiIiIiIiI77zW8G4AVjjnfmVm1YFjQOdg\nDe+TQHfn3MUxjrXMVMMr8v/bu/Mwycry7uPfmwEGFGRTFoGZUQTXiCYSNERoIYgSFCIuMVERfcEF\nYzDyxi0IuGWRS30FcYuKjuJK3FAREVoUcEskIhGEzKJsowKDIDAgc79/nNNQ1FT1nO6up6pPzfdz\nXXVN1Tmnqu7q+XV13XXO85zRcLxYeY4Xk9SU78nl+Z4sjU6/MbwbN7z/acCpEXEzcEa9bOuIOBJ4\nFXD0YMqUJEmSJGkwGh3SnJkfAt4FnARcVS/+FvAh4D2Z+aky5aktHJsgSfOH78mSNH/4njxaTffw\nkpmvj4j3AwcC2wM3AN/KzGWlipMkSZIkabYajeFtuw19DO/k5OQ93yxNTk4yMTEBwMTExD3XpRIc\nL1ae48UkNeV7cnm+J0uj028Mb9NJqxZNs3otcHNm3jKH+opqW8O7ZMcdWblq1ajLmJHFO+zAiuuv\nH3UZmmf8cFWeH64kNeV7cnm+J0uj06/hbXpaohXA8j6XlcDqiLgyIo4aTLkbtpWrVpHQqkvbGnRJ\nGmeOF5Ok+cP35NFqOob35cAbgdXAmcAqYEfgcGArqlmc9wU+EBF3Zebpgy9VkiRJkqTmmh7SfDKw\nJDOf3WPdmcDKzPyHiFgKPCYzHz/4UmevbYc0t/GQo80Wwpo1o65iZhYv3oEVKzwMu6Q2ZtnD5ySN\nK9+Ty/M9WRqduY7hvR54cWae3WPd04HTM3OHiHgm8JnMvN8gih4UG97y2vYHCfyjNAxmuTxzLKkp\n35PL8z1ZGp25juHdEnhgn3UPAraor/8OuHvm5UmSpEFxvJgkzR++J49W04b3O8A7IuJPOhdGxBOA\ntwPn14t2B345uPIkSZIkSZqdpoc0PwQ4F1hC1dD+GtgeWEQ1U/OBmbk8Il4DrMnM04pVPAse0lxe\n2w45Ag87GgazXJ45ltSU78nl+Z4sjc6cxvDWD7AJcCSwN7ATcB3wfarxu3cNsNaBs+Etr21/kMA/\nSsNglsszx5Ka8j25PN+TpdGZc8PbZja85bXtDxL4R2kYzHJ55li9TE5OMjExMeoyNM/4nlye78nq\nxffk4ZjrpFWSJEmSJLXKTA5pfirwCuDhwGZdqzMzdxtwbQPjHt7y2vYNLPgt7DCY5fLMsaSmfE8u\nz/dkaXT67eHduOGdDwa+SjVx1SOAs4H7AfsAK4HvDq5USZLG1+Tk5D2nqOg8zG1iYsJD3iRJGrCm\nszRfDPwIeA1wF/CEzPyviNgD+Cbwusz8XNFK58A9vOW17RtY8FvYYTDL5Znj8pbsuCMrV60adRkz\nssM223D9jTeOugzNM74nl+d7snpxDO9wzGkPL9Ve3TcDa4Gcul9m/iIiTgSOB+ZtwytJ0mytXLVq\noE3CZH2Zuj5RX5/ouD5XcdNNA3okSZLarWnDuxb4Q2ZmRPyG6vy7P6zXXQvM2/G7kiTNJxMMrrGV\nJM1/7t0draazNF8BLKmv/xg4NiJ2iogHAa8FVgy+NEmSJEmSZq9pw/sp4JH19ROARwNXA9cD+1Md\n7ixJkiRJ6jA1UaFGo9EhzZn5vo7r/xkRfwQ8HdgcODcz/6dQfZIkSZIkzUrj8/C2mbM0l9e2WRTB\nmRSHwSyXZ47La2+O21a1SmtvlkddRXO+J0ujM9dZmqceJICdgM2612XmstmXJ0mSJEnSYDUawxsR\n20XEZ4A7gF8BV/a4SJIkSZI6OIZ3tJru4f0I8BTgVOBy4M5iFUmSpDnZZJPq0K42Wbx4B1asuH7U\nZUiSxkyjMbwRcTPw95l5evGKCnAMb3ltG2MDjrMZBrNcnjkub0PP8eRkdZm6PnU6yYmJe68Pglku\nb0PP8jCYY2l0+o3hbdrwLgdemZnfKFFcaTa85bXtDxL4R2kYzHJ55rg8czwcZrk8s1yeOZZGZ66T\nVp0CvDwizm5V5yhJkiRJfUxOTt4zxnZycpKJ+tCViYmJe64P4jkG9ViaucanJYqIk4G/BM4Fbupa\nnZl5woBrGxj38JbXtm9gwW9hh8Esl2eOyzPHw2GWyzPL5Znjdqv3EA78cQfd8A6jSW+juR7SfDBw\nJrCwzyaZmQvmVmI5Nrzlte0PEvhHaRjMcnnmuDxzPBxmuTyzXJ45brdSDW9Jbay5lH4Nb6PTEgHv\nAn4E7AkszMyNui7zttmVJEmSJG2Ymo7hXQS8OjMvLVmMJEmSJE1nyY47snLVqiKPXeKUbgsXBmvW\nlNsLW6LmcTpVXNOG9yfAg0sWIkmSJEnrs3LVqiKH5weUedw1WezQ/Oow+hKPW+YLhVFoekjzq4Hj\nImKfksVIkiRJkjQoTSet+hXwAGAL4PfA6q5NMjMXD768wXDSqvLaNqkEOLHEMJjl8sxxeeZ4OMxy\neWa5PHM8HKWyXGwPL4PN8eRkdZm6PjUx88TEvdfnqo1Znusszaeznv//zDxy1tUVZsNbXtv+IEE7\nf5HbxiyXZ47LM8fDYZbLM8vlmePh2NAb3mFoY5bn1PC2nQ1vef4iqxezXJ45Ls8cD4dZLs8sl2eO\nh2OQWZ6sL1PXJ+rrEx3X56ptOYZ2ZtmGt0Wv0z9Iw9HGX+S2McvlmePyzPFwmOXyzHJ55ng42pbl\ntuUY2pnlfg1v31maI+JFwNcy84b6+rQy8xNzrFGSJEmSpIHpu4c3ItYCT8zMH9bXp5OZuWDg1Q2I\ne3jL85sr9WKWyzPH5Znj4TDL5Znl8szxcLQty23LMbQzyzPewws8BLiu47okSZIkSa3Rt+HNzJW9\nrkuSJEmS1AYbjboASZIkSZJKsOGVJEmSJI0lG15JkiRJ0liy4ZUkSZIkjaX1NrwRsSAi9oyIBw2j\nIEmSJEmSBqHJHt4Efgw8vnAtkiRJkiQNzHob3sxcC/wKuH/5ciRJkiRJGoymY3g/CBwbEZuWLEaS\nJEmSpEHZuOF2WwK7Acsi4mzgOqpDnadkZp4w6OIkSZIkSZqtyMz1bxSxdj2bZGYuGExJgxcR2eR1\nzhcRQXuqrQTQoh8xABHQply0kVkuzxyXZ46HwyyXZ5bLM8fD0bYsty3H0M4sRwSZGd3LG+3hzUxP\nXyRJkiRJahUbWUmSJEnSWGrc8EblmRFxckR8LCIW18v3i4gHlytRkiRJkqSZa3RIc0RsA3wd2Bu4\nBdgCOAVYCRwF3Ai8ulCNkiRJkiTNWNM9vO8EdgX2AbajGns95VzggAHXJUmSJEnSnDQ9LdGhwHGZ\neXFEdM/G/EuqZliSJEmSpHmj6R7eLYBr+qzbjPvu8ZUkSZIkaeSaNrxXAE/ts24/4NLBlCNJkiRJ\n0mA0PaT5NODUiLgZOKNetnVEHAm8Cji6RHGSJEmSJM1WZGazDSP+BTiO6vDlABJYC/xbZr6pWIUD\nEBHZ9HXOBxFBe6qtBNCiHzEAEdCmXLSRWS7PHJdnjofDLJdnlsszx8PRtiy3LcfQzixHBJm5zlDb\nxg1v/SCLqQ5tfhBwA/CtzFw2sCoLseEtz19k9WKWyzPH5Znj4TDL5Znl8szxcLQty23LMbQzywNp\neNvKhrc8f5HVi1kuzxyXZ46HwyyXZ5bLM8fD0bYsty3H0M4s92t4m47hpT4d0YuAJwE7U83afBGw\nNDPvHlShkiRJkiQNQqM9vPWhzN8E9gCuBlYBOwC7UM3g/LTMXFmwzjlxD295fnOlXsxyeea4PHM8\nHGa5PLNcnjkejrZluW05hnZmud8e3qanJToVeADw55m5KDP3ysxFwJOBrYBTBleqJEmSJElz17Th\n3R94Q2Ze1LkwMy8E3livlyRJkiRp3mja8N4K/LrPul8Dtw2mHEmSJEmSBqNpw/tJ4OV91r0M+MRg\nypEkSZIkaTCaztJ8FfCciLgUOJN7J616NrAl8I2IeMnUxpn50UEXKkmSJEnSTDSdpXntDB4zM3PB\n7EsaPGdpLs/Z59SLWS7PHJdnjofDLJdnlsszx8PRtiy3LcfQzizP9Ty8DxlwPZIkSZIkFdWo4Z3P\n59iVJEmSJKmXppNWSZIkSZLUKja8kiRJkqSxZMMrSZIkSRpLNrySJEmSpLFkwytJkiRJGks2vJIk\nSZKksdT0PLxExBHA84FFwGZdqzMzdxtkYZIkSZIkzUWjhjcijgdOAn4GXAKsKVmUJEmSJElzFZm5\n/o0iVgBfzMzXFK+ogIjIJq9zvogI2lNtJYAW/YgBiIA25aKNzHJ55rg8czwcZrk8s1yeOR6OtmW5\nbTmGdmY5IsjM6F7edAzvdsBXB1uSJEmSJEnlNG14vwPsWbIQSZIkSZIGqWnDeyxwZES8KCIeGBEb\ndV9m8qQRsUtEfCEiVkfEzRFxZkTs2vC+a3tc7o6Ix86kBkmSJEnSeGs6hndtfbXfxpmZTSfA2hz4\nKXA78KZ68duBzYHHZubtDWr5KPChrlU/zcw7+tzHMbyFOTZBvZjl8sxxeeZ4OMxyeWa5PHM8HG3L\ncttyDO3Mcr8xvE1PS/QW+je7M3U0sATYIzOX18VdClwJvAx4T4PHuDYzfzigeiRJkiRJY6hRw5uZ\nJw7wOZ8BfH+q2a0ff0VEXAgcSrOGV5IkSZKkac1o7C1ARGwREbtGxBazfM5HU53Pt9tlwKMaPsYr\nIuKOiPh9RHw7Iv58lrVIkiRJksZU44Y3Ig6KiB8Dq4EVwOqI+GFEHDjD59wWuKnH8huBbRrcfynw\nSuAA4Kj68c6LiH1nWIckSZIkaYw1nWjqIOBrwFXAW4HrgZ2A5wFfj4iDM/NbxarskJlHdNy8MCK+\nQrXH+G2ATa8kSZIkCWg+adWJwDnAIZk5NWMzEfEW4CzgJKBpw3sTvffk9tvzO63MvDUivga8ZLrt\nXvziF7NkyRIAtt56ax73uMcxMTEBwOTkJMC8uQ0wCUx0XKcFt6fUL4eplzNfb99b72S9fsLbJW5T\nmaj/ne+3ocrIqPPZ9Ha1bHL+/H+P6e0pU7cm5vntKaPO58xvT9a3J7xd4DZUGZnouE4Lbk8ZfT6b\n3b633sl6/YS3S9ymMlH/O+9v1wtGnc+mt6tlk/Pn/7vH7UsuuYTVq1cDsGLFCvppelqi24DnZObX\neqw7BPhcZt5vvQ9Ubf9tYJPM3Ldr+fkAmfmUJo/Tdd/3AUf2q8HTEpXndOvqxSyXZ47LM8fDYZbL\nM8vlmePhaFuW25ZjaGeW+52WaKOG918DPKDPui3r9U19BXhiRCzpKG4JsA/w5Rk8ztR9HwAcAvxg\npveVJEmSJI2vpnt4vwj8EXBg5+mEImIR1aHMl2Xmsxo9YcT9gEuA24Hj68VvAe4P7JmZt3U89jLg\nxMx8W73stcDDgfOBa6nO5/taYA9g/8y8qM9zuoe3ML+5Ui9muTxzXJ45Hg6zXJ5ZLs8cD0fbsty2\nHEM7s9xvD2/TMbyvAy4EroiI7wPXATsCT6Satfl1TQvJzNsiYn/g3cAnqDJwLvCaqWZ3quaOy5Qr\ngMPqy1bA74DvUR3O/J9Na5AkSZIkjb9Ge3gBImInqr2pT6aaYOpG4DvAuzPzumIVDoB7eMvzmyv1\nYpbLM8flmePhMMvlmeXyzPFwtC3LbcsxtDPL/fbwNm5428yGtzx/kdWLWS7PHJdnjofDLJdnlssz\nx8PRtiy3LcfQzizPddIqSZIkSZJape8Y3og4D3hlZl5eX59OZuYBgy1NkiRJkqTZm27Sqs7dwRvB\ntEcOrLPrWJIkSZKkUXIM7zzUtnEJ4NgE9WaWyzPH5Znj4TDL5Znl8szxcLQty23LMbQzy3MawxsR\nL4qI7fqs2zYiXjTXAiVJkiRJGqSmk1Z9DNitz7qH1OslSZIkSZo3mja8043RvT/whwHUIkmSJEnS\nwEw3S/PjgD/uWPSMiHhM12abA38NXFmgNkmSJEmSZm26WZoPBU6oryfwpj7b3QC8dJBFSZIkSZI0\nV31naY6IrYCtqQ5nXgY8C/hJ12ZrgFXzfQpkZ2kuz9nn1ItZLs8cl2eOh8Msl2eWyzPHw9G2LLct\nx9DOLPebpbnvHt7MvBm4ub7zQ4DrMvPOciVKkiRJkjQ40x3SfI/MXFm6EEmSJEmSBqlRwxsRa2H6\nIwcyc8FAKpIkSZIkaQAaNbzAW1i34d0OeCqwEDh9gDVJkiRJkjRnTQ9pPrHX8ohYAHyVeqyvJEmS\nJEnzxUZzuXNm3g2cBhw7mHIkSZIkSRqMOTW8tYXAtgN4HEmSJEmSBqbppFWLeizeFHgM8C/AjwdZ\nlCRJkiRJc9V00qoV9J6lOYD/BY4ZVEGSJEmSJA1C04b3Jazb8N4BrAR+VI/llSRJkiRp3ojMaU+v\nOxYiItv0OiNi+pMez0MBtOhHDEAEtCkXbWSWyzPH5Znj4TDL5Znl8szxcLQty23LMbQzyxFBZkb3\n8kaTVkXEHhGxX591+0bE7nMtUJIkSZKkQWo6S/N7gGf0WXcI8O7BlCNJkiRJ0mA0bXifAFzQZ90F\nwF6DKUeSJEmSpMFo2vBuSTVJVS93AVsNphxJkiRJkgajacO7DDigz7r9qU5bJEmSJEnSvNG04f0E\n8JqIOCYiFgJExMKIOAY4Fvh4qQIlSZIkSZqNRqcliogFwGeBZwFrgRuBbaka5jOB52Xm2oJ1zomn\nJSrP6dbVi1kuzxyXZ46HwyyXZ5bLM8fD0bYsty3H0M4s9zst0YzOwxsR+wNPpWp2fwuck5mTgyqy\nFBve8vxFVi9muTxzXJ45Hg6zXJ5ZLs8cD0fbsty2HEM7szyQhretbHjL8xdZvZjl8sxxeeZ4OMxy\neWa5PHM8HG3LcttyDO3Mcr+Gt+kYXqLyzIg4OSI+FhGL6+X7RcSDB1msJEmSJElztXGTjSJiG+Dr\nwN7ALcAWwCnASuAoqjG9ry5UoyRJkiRJM9Z0D+87gV2BfYDtqPbMTzmX/qcskiRJkiRpJBrt4QUO\nBY7LzIvrGZs7/ZKqGZYkSZIkad5ouod3C+CaPus24757fCVJkiRJGrmmDe8VVKcj6mU/4NLBlCNJ\nkiRJ0mA0PaT5NODUiLgZOKNetnVEHAm8Cji6RHGSJEmSJM1W4/PwRsS/AMdRHb4cQAJrgX/LzDcV\nq3AAPA9veZ5fTL2Y5fLMcXnmeDjMcnlmuTxzPBxty3LbcgztzHK/8/A2bnjrB1kMHAhsD9wAfCsz\nlw2sykJseMvzF1m9mOXyzHF55ng4zHJ5Zrk8czwcbcty23IM7czyQBretrLhLc9fZPVilsszx+WZ\n4+Ewy+WZ5fLM8XC0LcttyzG0M8v9Gt5Gk1ZFxL9GRM/xvhHxoIg4a64FSpIkSZI0SE1naf474MKI\neGjnwog4EPgp8PhBFyZJkiRJ0lw0bXj3pjoX708i4kURsUlEvAs4G/gxsGepAiVJkiRJmo2ZzNK8\nGfBe4KXAr4GtgNdl5inlyhsMx/CW59gE9WKWyzPH5Znj4TDL5Znl8szxcLQty23LMbQzy3MawwuQ\nmXcAFwF3AjsAVwJfHViFkiRJkiQNUNNJq7aMiDOAjwAfA/4M2BS4JCKeX7A+SZIkSZJmpefMyz38\nFNgSODwzvwQQEX9MdYjzpyLiaZl5RKEaJUmSJEmasaaHNK8A9pxqdgEy8/bMPAp4DnBIgdokSZIk\nSZq1RpNWxXpmfYqIXTLz6oFWNkBOWlWeg/HVi1kuzxyXZ46HwyyXZ5bLM8fD0bYsty3H0M4sz2nS\nqvU0uxsBt82hNkmSJEmSBq5vwxsRN9bjdKduR0R8JSIe2rXpXsBvShUoSZIkSdJsTLeHd2vuO6nV\nRlRjdbcuWpEkSZIkSQPQ+Dy8kiRJkiS1iQ2vJEmSJGks2fBKkiRJksbSxutZv3PHJFULOpat7thm\nl8GXJUmSJEnS3PQ9D29ErIV1TnEV/ZZl5gLmKc/DW57nF1MvZrk8c1yeOR4Os1yeWS7PHA9H27Lc\nthxDO7Pc7zy80+3hPbJgPZIkSZIkFdV3D+84cQ9veX5zpV7McnnmuDxzPBxmuTyzXJ45Ho62Zblt\nOYZ2ZrnfHl4nrZIkSZIkjSUbXkmSJEnSWLLhlSRJkiSNJRteSZIkSdJYsuGVJEmSJI0lG15JkiRJ\n0liy4ZUkSZIkjSUbXkmSJEnSWLLhlSRJkiSNJRteSZIkSdJYsuGVJEmSJI0lG15JkiRJ0liy4ZUk\nSZIkjSUbXkmSJEnSWLLhlSRJkiSNJRteSZIkSdJYsuGVJEmSJI0lG15JkiRJ0liy4ZUkSZIkjSUb\nXkmSJEnSWLLhlSRJkiSNJRteSZIkSdJYsuGVJEmSJI0lG15JkiRJ0liy4ZUkSZIkjSUbXkmSJEnS\nWLLhlSRJkiSNJRteSZIkSdJYsuGVJEmSJI0lG15JkiRJ0liy4ZUkSZIkjSUbXkmSJEnSWLLhlSRJ\nkiSNJRteSZIkSdJYsuGVJEmSJI0lG15JkiRJ0liy4ZUkSZIkjSUbXkmSJEnSWLLhlSRJkiSNJRte\nSZIkSdJYsuGVJEmSJI0lG15JkiRJ0liy4ZUkSZIkjSUbXkmSJEnSWLLhlSRJkiSNJRteSZIkSdJY\nsuGVJEmSJI0lG15JkiRJ0liy4ZUkSZIkjSUbXkmSJEnSWLLhlSRJkiSNpZE0vBGxS0R8ISJWR8TN\nEXFmROza8L4LI+KdEXFtRNwWERdFxJNL1yxJkiRJapehN7wRsTlwPrAH8ELgBcDuwHn1uvX5KPBS\n4J+AvwSuA74ZEY8tU7EkSZIkqY0iM4f7hBF/D5wM7JGZy+tlS4Argf+bme+Z5r57Aj8BXpyZn6iX\nLQAuAy7PzMP63C+H/TrnIiJoT7WVAFr0IwYgAtqUizYyy+WZ4/LM8XCY5fLMcnnmeDjaluW25Rja\nmeWIIDOje/koDml+BvD9qWYXIDNXABcCh67nvs8E7gQ+13Hfu4HPAAdFxCYDr1aSJEmS1EqjaHgf\nDfysx/LLgEet576PApZn5h097rsp8LC5lydJkiRJGgejaHi3BW7qsfxGYJs53HdqvSRJkiRJnpZI\nkiRJkjSeNh7Bc95E7z25/fbedt93UZ/7wr17etcRsc745XmtXdVWWvYjBtqXizZq40+4bbEwx+W1\n8SfcxliY5fLa+BNuWyzM8XC07afcxliMS5ZH0fBeRjWOt9ujgP9pcN/DImKzrnG8j6aazOqqXnfq\nNVuXJEmSJGm8jeKQ5q8AT6xPRQTcc1qifYAvr+e+X6WanOo5HfddADwX+GZm3jXYUiVJkiRJbTWK\n8/DeD7gEuB04vl78FuD+wJ6ZeVu93SJgGXBiZr6t4/6fBp4K/COwHHglcDDwpMz872G9DkmSJEnS\n/Db0Pbx1Q7s/8AvgE8BS4H+BA6aa3Vp0XDq9GPgY8FbgLGBn4CCbXUmSJElSp5HM0pyZV2fmczJz\n68zcKjMPz8xfdm2zMjMXZOZbu5avyczjMvPBmXm/zHxSZn53uK9gdCJil4j4QkSsjoibI+LMiNi1\na5stIuLkiDi/3mZtROw7qppL2pBe67hpmOXF9f9p9+XuiHjAqGovISIOr38eKyLitoi4PCLeERFb\njLo2Ta9JluvtHhcRZ0fELfV2X46I3UZRc0kR8dSI+HZEXBcRd0TEryLisxHxyFHXpsGIiJ0j4pSI\nuCgifl+/L/eaVHRq+0dGxOci4jcd729/N4fnf2NErIyIuyLiv2Z434HWonabSZYjYkn9Xn9TRNwa\nEedFxJ/M8flnnOWIOKHPZ6O1EXHb+h9hw+NpiVokIjYHzgf2AF4IvADYHTivXjdlO6o94XcB5wDD\nPW59uDak1zo2ZpDlKW8HnthxeRJwy3CqHZrjgD8AbwAOAk4DXkGVa81TTbMcEQ8DLgC2BJ5P9b61\nBPhORDxwuFUXty3wY+AY4EDg9VSTS17c64sAtdLDgGdTnR3jAqb52xsRTwC+TzUHy0uBpwMnAwtm\n88QRsRfwNuAM4M+pfu+a3negtWgsNMpyRGwLXEg1ye5RwPPqVedHxMNn88RzyPKHue9noicCB1B9\nhljffEgbpFHM0qzZO5rqA9IembkcICIuBa4EXga8B6q948AD6/UHAM8aRbHDsCG91jHTKMsdlmfm\nD4da4fAdkpk3dNz+bkTcBJweEROZOTmiujS9pll+PdWHkadl5i31dj+kOrvAcfX6sZCZnwE+07ks\nIn4EXE71wfLdo6hLg5OZ3wF2AoiIl1LNrbKOiAjg48C3MvPZHau+M4enfxRVU/LBzFzR9E6FalHL\nNc0y1ZxBDwL2mcpdRJxPNd/QScBfz+LpZ5XlzLwWuLZzWUS8kOqLm4/Poo6x5x7ednkG8P2pD1UA\n9S/IhcChpZ88IvaLiHPqw/ZujYhLIuLIjvUbR8TbImJ5RKyp/31rRGzcsc3UIapHR8RJEXFtfWjI\nVyJi547tzoqI/+xRw471YR9/X/r1qqhRZ/mvIuJ7HYeW/iAiDulYv2VEnBoR19SHZF4eEcd2PcZ+\ndZafUR8O9Zv6sjQiturY7mcR8YUeNfxpff9DAbqa3Sk/oprHYOce6zQ/NM3y3sDFU81uvd01wM+A\nv5rtk8/HLPdxY/3vH2b7WtVKTwEeAbxrEA9WNxgfq28ui2p4y5vrdQsi4nURcVlE3B4Rv46Ir0fE\nHiVq0QZnb+DKzsa0nnvou8AhETGjnmqOWe7lCGAVHhXWkw1vuzya6sNRt8uoviWasYiYjIjlDbY7\nFDiX6qiAo4FnAh8BFnds9gmq2bNPB/6S6hf5dfXtbq8HdgOOBF5NdYjq0o71S4HHRcQjuu73t1Tf\nhp2xvpo1r800y/9cf9GxOqpxj4/p3iCqsa/nre+JoxqrdSZwPfAiqj1O/0G1l25qL8DXqf54vBM4\nBPgG8K6IeFuPh3wPsJbqMNUTgcO57x7qpcDBnY1D7YXADcDXpil3girvP1/f69LINM3y3VTni++2\nBtgtIjadWjAuWY6IjSJik4jYHfgg1R6JT6/vdWms7FP/e7+IuDgi7oyIVRHx/yJis84NG34eL90M\nlgAACu5JREFUeQXwz/X1w6g+O/x7ffuz3Duh6aHA/wH+h3rv3UxqkXqY7j18c6rPtMBQsnwfEbEL\n1eeFT2bm2gavZcOTmV5acqH6pXpHj+VvBe7sc58DqH5J9+2z/lzgigbPvRz4wTTrH031Qen4ruVv\nqp//MfXtxfV23+7a7rX1djvWtzcDVgNv79ruJ8BZs3mtXubPpWmWgR2pxrIeRvVh5aVUhw/dDDy8\n676/AM5Zz/NuCfwO+Pw02xxSZ/SFXcs/THU6tW3r2/vV2320a7tTgNs6bu9CtVfrqI5lGwO/Bk6Z\npo6dqb6tPXvU/19eps1U0yx/FvglsKBj2RbATfX71g4dy8ciy1RHKKytL1d0/856GY9L/b58N7Co\nx7r31///vwVOAPYF/gH4PXBm17ZNP4+s83xUZ/9YCxwzzf0a1+Jlw7ysJ8v/CtwKbNOxLKiGr9wN\n7N2xvGiWezzOG+j4rO1l3Yt7eDdwmfkXmTntYPuoBuMv5t5vnnrZl2pP1Ke6ln+S6g1hv67l3+i6\nfWn976K6rjuAL1Dt0Z2q44+APan2JGsDkJnXZ+YrM/NLmXlhZn6EKmtQfZnSue0emdlv7M2UP6M6\n5/eHp9nmyVR/OLr3RH2SaqKTJ3Ut/3rX7UuBhRGxfV3X1cAk952M4ulUE64tpYeIuD/VxBN3Ai+Z\npla1x3upGsYPRsSDI2Ix1dEvU7Nw3/Ot/Bhl+QVUhwE+n6o5PzemmclXY2kjqs8GSzPzpMy8IDPf\nRTXm8bDomOynyeeRaRxI9Ts03eeUxrVIPXyAaozs0oh4aETsRPWl4JJ6fed7eOksd3sh8JPM7HW0\nkfCQ5ra5Cdimx/Jt63WlbFf/e80022xb/3td1/Lru9ZPubHr9pr6387DipYCiyJiqll+IdWHJmeg\na79ZZ7n+0P09YK9ZPO9Ulq+eZpttgRszs3us4fVUX97MNsv71E0OVFm+KntMxFUfWncW1R/Rg7Ka\nnELzV6MsZ+aFVJOeHE6Vv2VUe2k/TvXFRneO1mfeZzkzr8jMH2XmZ4G/oGrux2ZyLjUyNTfBuV3L\nz6HK4OMH9DzbUWV9zTTbDKsWjaGs5mn4G+CPqSYbvJrqC72pMeHdn39nq0mW7xERf0o1Nv30AT3/\nWLLhbZfLqA4d7vYoqmP7S/lt/e90E+dMfVDasWv5jl3rG8tq5rxfAi+ox6I9n+rwvUZvAprXBpHl\n2ZyC6resfxKoG4Fto2Oytdqss0w1zvJ2qiw/gOpQ03WOVKif80yqP6hPz8ySv9cajMZZzswPANvX\n2++amQcBD6YaLnL3DJ93Xme5W2beTPUh8WGzeE6112XrWT+o8Ya/pcr6wnlQi8ZUZn6R6j33kcDD\nMnMv4AHAr+ov4wehSZY7HUH1panzI0zDhrddvgI8MSKWTC2or+9Dwb2emfkLYAXVoPl+LqD68NU9\nLfsLqBqTyVk+/SepJmI5mOpNpuchoGqdWWe5PiTyz4EfzOJ5L6Iag3P0NNt8h+qwped0LX8B1R6v\nizuWNWq6M/NW4Ev1Yzyb6nDS+xz+X3+pcwbVxBOHZuaPmjy2Rm5GWc7MuzLz55l5bT1M4y+oxqnP\n1LzNci8RsQPVXoirmjyPxsY3qD6MH9S1/OlUmfvxgJ7nHKrPtNN9ThlWLRpjWbkiM5dHxIOB5zK7\n9/B+mmQZgIjYhOp8wF/P3md6UM3z8LbLh4FjgC9HxPH1srcAK4EPdW4YEU+jGt/1WKpGdCIiHgT8\nPjPP7tju21SD5Xdfz3MfC5xZzxz6AeA3VN9wbZ+ZJ2bmZRHxaeDE+hfwIqoxZv8EnJGZ6/tmlbrO\nbkuBN9bPuTIzL1jnTg1fq+aVRlmOiJOp3vgvpsrcI7j3fKbv6HzAiLiK6ny9B/Z70sy8NSLeALy3\nPr3Kp4BbgMcBt2fm+6g+FH0P+EA9dvEyqlnHX0I1OVHnXrFeme1nKdXhUCcBF+a659w7jaqBeBtw\ne0Ts3bHu6qxOYaP5p2mWd6aalfMiqmZzL6osfyEzP9f5gG3PckT8B/BfwE+phqE8nOpvyJ14Spix\nERGH11efQJWfgyPiN8Bvpv5WZ+aNEfHPwD9FxC3AeVTZPx44PTOXdTxe088j68jMyYg4k2oG8kX1\n82xCNefDWfV43ca1aMPSJMv1kTL/RvVF4u+Ax1C9h19K1/ta6Sx3bP4MqqEpnnt3fUY9a5aXmV2o\nJj35PNUMxjdTHV7Waza55VSTlXRflnVtdz7wvw2fewL4NtUv+u+oZkw+omP9xlQf9JZTfaBbTvWB\nqHNW0sV1HS/peuz96DPDMvDDet1b+9TV6LV6mV+XJlmmOm3VD6jGXq2hOq3JUmD3Ho+3jK7Zv6d5\n7mdRNdG/r5//YuDgjvVbUE0ydA1wB3A58Oqux5jK7P5dy4+gxyyPVI37tVTN+kt71NQvx3cDbx71\n/5eXOWd5e+BbVDMa3051KqNjgY16PF7bs/x/qWZovpFqL/TPqb7QWedvlZf2XqgOAe71fnVej22P\npZp9/I76ve4EOj4b1Ns0+jxCn5l061y+oc74HVSz3J/V/feiSS1eNqxLkyxTHS3zVaqxurdTzc58\nErBZj8cbVpa/VP9N2XjUP8P5fon6ByZJkiRJ0lhxDK8kSZIkaSzZ8EqSJEmSxpINryRJkiRpLNnw\nSpIkSZLGkg2vJEmSJGks2fBKkiRJksaSDa8kSZIkaSzZ8EqSNEIRcURErI2Ih466FkmSxo0NryRJ\no5ejLkCSpHFkwytJktYrIjYddQ2SJM2UDa8kSfNYRDwhIj4fEb+KiNsi4vKIeHtEbNaxzXsj4vqI\nWNB13y0i4paIeEfHsgdGxAci4uqIuCMifh4RR3Xdb+ow6ydHxOci4ibg+8VfrCRJA7bxqAuQJEnT\nWgT8BPgocCvwaODNwEOAv6m3eT9wDPBXwBc67vu3wObABwAiYkvgQmBh/RgrgIOA90fEppn5vq7n\n/iTwaeBw/MwgSWoh/3hJkjSPZeZ/dN6OiIuAW4CPR8QxmXlTZv48Ii4AXsZ9G96jgXMy85f17WOB\nXYHHZOayetl5EbENcEJEvD8z13bc//OZ+foSr0uSpGHwkGZJkuaxiNgyIv41Iq6KiDXAXcBSIIDd\nOzY9DXhKROxW328v4PHUe3drBwE/AFZGxIKpC3AO8EDgUR3bJvClUq9LkqRhcA+vJEnz2+nA/sDx\nwH8Dvwf2Bk4FNuvY7ovAKqq9vP8IvBy4BjirY5vtgd2omuZuCWzXtey6OVcvSdII2fBKkjRPRcRC\n4JnAmzPz1I7le3Zvm5l/iIh/B14REe8Enge8s+sQ5RuomuJXU+0h7nZF98PO8SVIkjRSNrySJM1f\nC4EFwB+6lr+4z/YfBN4IfB7YFPj3rvVnA68CfpWZvx1cmZIkzU82vJIkjV4AT4+I67uW3wxcDLy2\nXvdb4CXATr0eJDOvjYivUM3W/OXMvKZrk3cDzwW+FxHvptqje3/gEcCTM/OwQb0gSZLmAxteSZJG\nL4H39lh+GXAI1WmHTgVuBz5LdYqis3psD9Xe3cOo9vbe90kyfxcRf0Z1SqJ/BHYGVlM1vmfO7SVI\nkjT/RKbDcyRJGhcR8SngSZn50FHXIknSqLmHV5KkMRARe1Odhui5VOfblSRpg+ceXkmSxkBErAVu\noTrk+eVdszNLkrRBsuGVJEmSJI2ljUZdgCRJkiRJJdjwSpIkSZLGkg2vJEmSJGks2fBKkiRJksaS\nDa8kSZIkaSz9f33T8VhOkY6kAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa3c6d590>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot for a list of batch sizes. Only plot layers that consume at least 10% of the total execution time.\n",
    "plot_time_per_image_per_layer(df_per_layer_info, model='bvlc-alexnet', libs='nvidia-cudnn',\n",
    "                              batch_sizes=[8, 16], direction=direction, lower=0.10, rot=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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pJT/22GM9IyPD27Vr57Nnz640w6effuq5ubnetGlTP+KII3zGjBnlakL79etX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9OKcMm4SPoH8IVehmWblGD9abZ/1uPZ+sA5trvruyYVIOlR4EO2T2hblhFKkox32f5xj2cbAme6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\nT+X4gv2AS8sC+zDgU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MtSwO+BbwOPACsBewPLAO8pbb4JfAh4F3BGx9/dDlgAOApA0sLA\nr4D5yr8xHdgQ+KakeW1/vevdJwOnAluQa4YkSZJkEpI/XkmSJElSMbZ/2PlZ0mXAw8B3JH3I9kO2\nr5f0c2A3ZjV4dwV+avuO8vmjwCuBlW3fWu5dJGkxYB9J37T9TMffP932p4fx/5UkSZIkTZAuzUmS\nJElSMZIWlnSwpFskPQnMAE4CBCzf0fQbwLqSXl3+3huA11FOdwsbAlcAt0uae+QCfgosAazY0dbA\nmcP6/0qSJEmSJsgT3iRJkiSpmxOA9YAvAH8EHgVWA44E5u9o9yPgfuKUdy/gA8DdwLkdbV4CvJow\nmrsxsHjXvXufs/RJkiRJ0iJp8CZJkiRJpUiaD3gnsLftIzvu/1t3W9tPSzoW2F3Sl4GtgS93uSg/\nSBjFexAnxN3c2P3PPsf/hSRJkiRplTR4kyRJkqRe5gPmBp7uur9Dn/ZHA58FTgfmBY7tev5j4MPA\nnbYfeP7ETJIkSZI6SYM3SZIkSdpHwMaS7uu6/3fg18Ce5dkDwE7Ay3r9I7bvkXQ2ka35LNt3dzX5\nCrAV8EtJXyFOdBcEXgOsZXuz5+t/KEmSJElqIA3eJEmSJGkfA0f0uH8tMI0oO3Qk8DjwfaJE0bk9\n2kOc7m5GnPbO+hL7H5LeRJQk2gtYEvgbYfj+4Ln9LyRJkiRJfcjO8JwkSZIkmVOQ9F1gDdvLti1L\nkiRJkrRNnvAmSZIkyRyApNWIMkRbEfV2kyRJkmTKkye8SZIkSTIHIOkZ4GHC5fkDXdmZkyRJkmRK\nkgZvkiRJkiRJkiRJMkcyV9sCJEmSJEmSJEmSJMkwSIM3SZIkSZIkSZIkmSNJgzdJkiRJkiRJkiSZ\nI0mDN0mSJEmSJEmSJJkjSYM3SZIkSZIkSZIkmSNJgzdJkiRJkiRJkiSZI/n/d6HxLr6Ix9cAAAAA\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa3c14690>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot for a list of libs.\n",
    "# NB: cuDNN and cuBLAS perform about the same on the fully connected layers (which suggests that\n",
    "# cuDNN falls back to cuBLAS for these).\n",
    "# Unsurprisingly, cuDNN performs better than cuBLAS on the convolution layers.\n",
    "# Surprisingly, cuBLAS performs a bit better than cuDNN on the relu layers.\n",
    "plot_time_per_image_per_layer(df_per_layer_info, model='bvlc-alexnet', libs=['nvidia-cuda','nvidia-cudnn'],\n",
    "                              batch_sizes=16, direction=direction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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6IzeYz6Xy5cvbf//7X//za665xsaOHet//vPPP5tzztauXWtmZlWq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COgwhnf0Z8Biazv8n4MCy9NHeq6n2u1ZQTweQcSwK6gz+j4ZMwGLqq+m6Sn3v3YZjmk/OBsYG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Hv+BrBpi/22BF7tZu6L/+dK4Ptt7HcCabKxrtU73c7NSMt9nR7u6lwNnS6L8gqpy9SPgDkl\njSWtUfq7mLYL5FDNSJfGdrxCOkm+eJD91gF6MbvlaFLXyME8R7p6uTFwWLE8wx9Iub8+Il4fhli6\nnZuRlvtOnU1nS2dcTTpWulFeSwN3N/3fT0jahDTm+LqiC/YrI/T9OxIR/5T0XtLn4qOkSd76lhN6\nnjTL8+Gki0gvS9qX7tZTC5OWP+szvvj30ab9HiaNBe7E88X793mLNEPvf5r2eyepgTmiRMSYotv3\nMcCeTL0ItEdEXNCw65WkO8PvpYtlFRFbS9oV+K6kzwAHxtRJwWa4e2O3379DnS5D9kFS7vcAjpf0\nCKlMfhsRt3YhvvloqlcKdzHtUJ6R+v4D6fRc6kpSPbYD6fP+mqSri58vj4jm431GDXdu7iI12K8v\nnk8grXhwY9N+y5PqtG47kvQ3DmYB4Od091y227kZabmvj9wtbz/yPUh3IbYmVRpPkE5uZvjKMR12\naRzC+48D/tjGfjszAu86FrEtSup6eiXp7tALDMOVy27npgq5HynlRWpo7drPa3OQlnD4L2ns7VDK\nqqvv38Pcd6ueehLYqeH5TKRuzu9p2m974LkO3/t60lI/g+13FHB77hyP9LJqeP/5SXfXXibN0D47\nafKpYbkj2+3370H+VyaNGe+7Oz/DdyOLv/0A4EPF40lguxb7fQx4fqS9fw9zPw9TlzH8N6nBfA2w\nP0Psjt6D3G9LuvD2v8BspPHjT5Mak3MVj75uxT/IneMB/o5hr3e6nZuq5L6Mj+wB+DFyHsAo4Fuk\nMW1TSFf4T6bDbp502KVxCHH+AHiyjf12Jk0Jnz23g8Q5bF+Y3c5N1XKfs7xIF4guHOD1WUiN66F2\nS+/q+2fM/3DVU11rnJImnPlkG/tdChyeO6cjvaxavO+GpPHZfyddfHqLYWyYdvv9ZyCumUh31wdd\nEYEBLtZ1+H82Dm/p6/57cov9jmNoYx27+v6Zyqm/hthXR1puSEM5XiZ1vb29+Jw0D126Hpg7c04X\nBd7V5r7D9R3R1dyUJfdVe6hIvpWcpDlJB9EOpFmT5y9eeh54gNT16cxoc51DSSsX77UjaVxaR93i\ni3j6ujSuxvRdGn9P0aWxk/ct3nsJ0hIjYzv93ZwkbQx8PSI+NMA+swKbk3K/PbBoRMzcwf/R1dyU\nNffNJG1KGu/6YBQzZja9vgRpGYsBZzqekfKStDNwGPCR6KfbdbH+8w+BrSOio1lDu/3+QzHca9vO\nSD0laSvSMiAXDrLfpcCtEXFKu+9dBSOprPp5v1lJa6gfQbozu1lE3DQj79nL9x9iTPOShsxsGhE3\nd/B78wDbkXL/4YiYf5BfafzdTVpsfiEi7mna73zgLxFxYrvv3Yv3n1GSFiINZ1qX1O39z6Q7cM8N\n+IvTvscoUu63j4hVO/i9nuSmWKN7H9LSWYuTLrBMJp2r/TYirhjK+w4hjk1JF3WuaNj2v6S5BBYp\nNv0TOCoiftHme87ouWxXczNScl8nbvhWQIvlg/7K9MsHbUAaQ9Dx8kGSFo2I5qV5rENFQ+TXHTZk\n14+IP3cxrFpptcQPMN0SP5LWB8Z1UlZ9v+fyaq3F8kGTSBcNGpcPWpnUG6TT5YNcTw2jMpVV8f23\nPHB3DP+Yyq6/f4v/b7CllQ4nTdI3gaEt3TRrRLwxAyFWVpvLB/Wtx93rpZUqT9JtpOFvpxTPDwBO\nZ/olfrYAdosOl/jxd4SBG76VIOkS0oyk20fE+H72WZY0+/A/ImLnNt93VmCxTk9q6qa4C9KObUnT\n1c8saQHgzcYTKUnrke6k/MONp+6QdAKpS/JoUteiTUlj+V4n3Ql5oNhv0IavpHcA55OuPj/Q5dBL\nT9JFpN4fjWvbrk6a8Kl5bdsxEdFybVtJK5AmTwtSF+THehB+rXRaVqTjaeaIeK3hPZYEDgTWZOrM\nw2dERCcTN9WOpCmkz7b62aXxtSDdDftdRDzcg/Aqrcj9qIi4rXj+S1Ija+uIuLvYtg6p+/jFEbG/\npPeRliqcCfh5RPxd0qqk7serkLo6fy8i/tj7v6hcJL0AfDwiri2ePwJcExEHNu33U9Ld2/cXz+cj\nNYhXJ93smUIaD/2niLixd3+BlULuvtZ+zPiDISwfRDr5H0864b+PFuPR6NEyJ2V/0OESP6RZB/u2\nnUk6iTm/aZ8/ALPm/tuq9qDDJX5IJzP9Pfq+YDft25b77xvJDzpcPoi0ZMhSDdtnKo6XxvFubwKn\n5/7bqvYYQln9noZlX4D3kRrMr5LWmr2n+HkSsHzuv28kP+hwaaWG74w/F9/r8+f+G8r6oMPlg0hj\nwV8hzfPwUvGZXxt4BvgHaQz/o0X5bN6rv6OsD4awxA9pKMJ/m74XGn/+K7B27r/Nj5HzmAmrgo6W\nDyqWMDmDYowWqdL4paSzJHXUtdOA9MV3DWlM80CPn5AauVuT1ob9Mmnc1VmksdCfAdYg3T3ZnDTu\nw4ZXyyV+gE2A+0lL/Gza8PIbAzz67lxdXzwfccvSjDCdLh90MLBYw/bDSMfE90gXJtYn1WP7Sdq/\nC/HWWadltR5pyECf00iN5xUj4v2R7sysRBq7Vqux0p2KiK1JDavvSrpa0oqNL/fza78kXYg7A5gk\n6TeStvP3+QwbbPmgo0kXHBYkzf59Eeki0N3AyhGxE2lIwA2kpXpsYH1L/PTpW+Kn2fLA85IOJvXY\nOot0Y2cr4OukSSc/T5rk6j7ghmKsr5nX8a2I64BvSvpL9NPtr+jqfBzp5ORLwDkR8ZmG1/clnVAu\nLOnj0dBlzQZ1L+nO+M8H2knSv0mzbB4fEd8stt1DajR/NSLOLXb9azG2aG/SWoE2fJ4hzTo+jYj4\nr6RtgEtIy/ycVrwkUpeps0mN20Zzko6l85jaMLD+dbq27Tuatu8D/Cwivtiw7fZi4p59SDOo2vDo\ntKzmY9q1Zzcg9SL6Z9+GiJhYjF89sysRV0hE/ErSVaQLpPdJOoW0KkJ/To+IPSVtSFoW5eMUS6EU\n3XXPi4h7ux54NaxTzAUB6TP9zhb7zEeajff9pEkQXwKQdDypwfW5KMZRR8Trkn4A/LTrkZffScBl\nkiaQbhQcB5wsaTLpPBfgw8DxwIWkoRQnRcRRDe9xnaT7SY3hRSNiV0lXFr/z8R79HTaC+Y5vNRxC\nOgn/m6SbJf1I0gnF40eSbiJNzDAn8AXSZFe/anyDiOhb629j4KqGit8Gdyepe1O7xjX8fGuLbQBj\naX2l02bMHaQZHqcTEa8Wr11OWsIGUle2yaSryddFxDf6HqTlEgDObthm/XuAdJcWgIiYEhHrxvRj\nE99L6ibYbHnSMIFml5Luqtjw6bSsJpLGM/Z5ndS1udkrpKVebBAR8XxE7Eu6i7UzqcvmtgzQwysi\n/hQRnyPdjfwU6Q7aaOAuSfdIOqT7kZfeD0g3CK4jzSTcambltUh3I2cnNYD7vFT8+0rT/q+Q1k63\nAUSawfh/SQ3gycBBpLxdSrro9h/gN6S7uF8l9eAa0+KtbiDdgV+heH4uaUiSmRu+VVBcVX8vqSvg\na6Rp2w8tHh8j3ak6HHh/sW/f2MXm97mRNJHDe0ndNxdo3sdaOpG0ruuAIuISUhec2Rs2950c/rdp\n99eY/o6XzbhfActIWrDVixHxJvAJ0tXmiRExjnSScyFwTTEcoOXv2qBOonW3wWZrkdYbhWlP8v9N\n68bUa/i7bLh1WlYXAEdIWrzYfhFwmKS36zqlJe4OJXUNtTZFxJ9IE4SdAwzYq6jhd16LiAsjYltS\nD5cvk46R0wb+zdrbDPhQ8diseFzQYr/lSN8Jj5DOsfrsTKqPPtq0/0dofTHPmkTET0gX0b5DauhO\nIg3L+zPFsLCI2Ly4yz6JNMyi2fqk746+1U2exOdTVvCszjUk6RbSrKlH9fP6GqTut7OSJsrwOKFh\nIule4KyI+F7DttWAv8e0M6J+ltT9eYUWb2MZFLMJ/5h0EvpV4GLSVelNI/P6nlVUzLD6DFPHTr8L\nODQizmjab3/gS9GDdYitNUlzkCZlej+pIfwYab3f/5KW2IPUe2Iu4EMRcUeOOMuu1dJKzTMRD/L7\n74+mNWBt6CR9mjQMZhzFjOekrs5nkI6DO0gNs08B/xsRP8wUaiVJ+hppcqsTSPXP66Rxvd8AHoiI\nDxX77QV8OSJW6eetrEbc8K0hSUeTxpouX3TvbLXPu0ndfZZyw3f4SPoWME9EHDTIflcD/4qI3XsT\nmbVL0u6kOyeTSL0jNnPDd/hJOrvF5rsj4vtN+40FJhcTyVgmkmYh9TranzThVaNXSEMIjomIB3sd\nW5V10vC14VfMj/JpYDbSzOZnFxOI/oR0se410vjeQyJiSr5Iq0fSTKS5HfZh6hJfInVT3ysiJhX7\n7Udabu2Mlm9kteKGbw0Vk8EsDTwaEc1jURr3WxhYNSLG9iw4A96+6/50RDyTOxabXrEO8/GkLlkH\nR8R9mUOqrWId7Rcj4vncsVhS3JlcnNS9djLpu+bNvFGZ9Vax1vXzEeEZ/7uoqG/WIw3je9gX12wg\nbviamZmZmZlZpXlCEEPSXJJGS7pY0piimzOSPum1z7rLuS8XJdtLOlXS2ZKWKbZv0jCxj3WBj5Xy\ncFnlI2kJSd+WdIekRyWtXmw/RNL6g/2+DV1T7h9z7nvL9Y61w+v41lzRReRG0syPDwGrA/MUL29G\nmqzhs1mCqzjnvlwkzQ9cQZox8kVgbtLSFxOAz5FmkBydLcAK87FSHi6rfIqJEm8mdfm8lTQR32zF\ny8uQuoPulie6anPu83K9Y+3yHV87jTT5wkqktWjV8NpYYKMcQdWEc18up5Am7dkAWJBpy+s6YPMc\nQdWEj5XycFnlcxrwIGm5nZ2YNvfjSDPeWnc493m53rG2+I6vbQnsGxETJDXP3vwEsESGmOrCuS+X\nHYDDIuLWFuU1kelnsrXh42OlPFxW+WwI7BoRL7XI/dPAohliqgvnPi/XO9YW3/G12UjdNluZF/BM\nnN3j3JfL3KQv0FbmYNorzDa8fKyUh8sqn4GWy1mItKyUdYdzn5frHWuLG752H7BzP69tA9zZw1jq\nxrkvl4eBrfp5bRPg/h7GUjc+VsrDZZXPbcDe/by2C3BLD2OpG+c+L9c71hZ3dbZTgIslAVxQbFu1\nWIB9H2D7XIHVgHNfLj8ETpf0AlPLaz5JewMHAftmi6z6fKyUh8sqn+OA6yRdQ8p9AFtIOhj4GLBx\nzuAqzrnPy/WOtcXr+BqS9gNOJM2A19dd80Xg8Ig4M1tgNeDcl4ukE4HDSGUl0snNFODkiDgyZ2xV\n52OlPFxW+UjaDvgusELD5vHAgRFxZZagasK5z8v1jrXDDV8D0vpnwAeAdwGTgXER0d94CRtGzn25\nFGv3bsnU8ro2Ih7NG1U9+FgpD5dVXpJWpMh9RDycO546ce7zcb1jg3HDt8YkzQZcBHwnIm7KHU+d\nOPflUpTXScAFEXF77njqxMdKebis8ily/3/AVyLimtzx1Ilzn5frHeuEJ7eqsYh4nbSotz8HPebc\nl0tRXp8H5swdS934WCkPl1U+Re6Xw7PX9pxzn5frHeuEPyR2C15YPRfnvlzuBtbIHURN+VgpD5dV\nPtfS/8zz1l3OfV6ud6wtntXZDgUuk/QScBnwJGnCnrdFxEDr09nQOfflcijwK0kTgMvD40R6ycdK\nebis8vkBcL6kWeg/956PoDuc+7xc71hbPMa35iT1VQT9fRAiInyBpAuc+3KR9DgwLzAX8AbwL6Yt\nu4iIZXLEVnU+VsrDZZVPQ+6hn/xHxMw9CqdWnPu8XO9Yu/whsGPpv6Kw7nLuy+V6XF65+FgpD5dV\nPnvnDqDGnPu8XO9YW3zH18zMzMzMzCrNk1uZmZmZmZlZpbmrcw1JOhr4WURMKn4eSETEcb2Iqw6c\n+3KRtCdpIqvJxc8DiojzehBWLfhYKQ+XVT6SzgKOi4jHip8HEhGxTy/iqgPnPi/XOzYU7upcQ8Uk\nAKMi4ramCRlaCU/IMHyc+3JxeeXj3JeHyyofSY8BO0bEvZLGM/A4x4iI5XsTWfU593m53rGhcMPX\nzKwfkpYBJkXEG8XPA4qICT0Iy8zMzMw65DG+NSTpLkmrFT8fLWnx3DHVhXNfOt8B+hq8mwAvRcSE\n/h4Z46wcHyvl4bLKR9JzktYqfj5L0nK5Y6oL5z4v1zs2FG741tMawNzFz8cAS2aMpW6c+3LZAVig\n+PlsYIWMsdSNj5XycFnlMxcwe/HzXsDC+UKpHec+L9c71jFPblVPk4AdJT0NCFhU0tL97RwRE3sW\nWfU59+XyNPAB4DZSeXlsSO/4WCkPl1U+E4DPSeprgK0paY7+do6Im3oTVi0493m53rGOeYxvDUk6\nAjieNk/iPSHA8HHuy0XSd4HRtFdeERG+mDhMfKyUh8sqH0mfAX7C4D34hCf4GVbOfV6ud2wo3PCt\nKUnrAquSum9+C3i0v30j4ue9iqsOnPvykCTg46TyOgY4B3iiv/0j4mu9iawefKyUh8sqH0mLASsB\nY0gX6h7sb9+IuL5XcdWBc5+X6x3rlBu+NSdpDLB/RDyUO5a6ce7LpXHpityx1I2PlfJwWeUj6Wzg\n2Ih4rM39lybNWv9mdyOrPuc+L9c71i43fK1tkmYGXgfWjYi7csdTJ859uUiaCfg78NGI+GvueOrE\nx0p5uKzyce7zce7zcv7rzbM6W6eUO4Aac+7LQ8CyTJ3x03rLx0p5uKzyce7zce7zcv5ryg1fMzMz\nMzMzqzQ3fM3MzMzMzKzS3PA1MzMzMzOzSnPD18zMzMzMzCrNDV8zMzMzMzOrNDd8zczMzMzMrNLc\n8LW2RcRbwN5AWwu02/Bx7ktnCvANYFLuQOrGx0p5uKzyKXK/GfBw7ljqxrnPy/VOvSkicsdgI5Sk\nBYHVIuKm3LHUjXNfLpLmAN4VERNzx1I3PlbKw2WVj6SlgU0j4rzcsdSNc5+X6x1r5Du+NpBNgTG5\ng6ipTXHuy2Q7fPU4l03xsVIWm+KyymVd4OzcQdSUc5/XprjesYIbvmZmZmZmZlZps+QOwHpP0llt\n7rpMVwOpIee+XJhU+qgAACAASURBVCQd3eauq3Y1kBrysVIeLqt8JN3Q5q4LdzWQGnLu83K9Y0Ph\nMb41JGkK8ALw0iC7zgnMHxEzdz+qenDuy6UorwDUxu7h8ho+PlbKw2WVj6S3gMeLx0AWBN7j3A8f\n5z4v1zs2FL7jW08TgWsiYt+BdpL0ceCi3oRUG859uTwF/BYYPch+OwO/6n44teJjpTxcVvn8Hbg1\nIvYaaCfnviuc+7xc71jH3PCtpzuAddrYz90Bhp9zXy53AGsVyx/0q7jyb8PLx0p5uKzy6ST37fRc\nsfY593m53rGOeXKrevo98Gwb+z0AHNvlWOrGuS+XG4F529hvPOClKoaXj5XycFnl8yvg/9rY73bS\n2qU2fJz7vFzvWMc8xtfMzMzMzMwqzXd8zczMzMzMrNLc8K05SfsP8Nrsks7oZTx14tyXi6RtB3n9\n8F7FUjc+VsrDZZWPpJMktZy7RdJCkv7Y65jqwrnPy/WOtctdnWuumJTnd8BnI+K5hu2rAxcCy0TE\nPLniqzLnvlyKpRO+B3wpIt5o2L4o8Atg04iYNVd8VeZjpTxcVvlIehm4H9g1Ih5t2L4laQ6CKRGx\nRK74qsy5z8v1jrXLd3xtG+ADwL2SNgWQNBq4DXgNWDtfaJXn3JfL54HPAX+W9B4ASR8F7gPeA2ye\nMbaq87FSHi6rfNYH5gbulrSnpFklfRu4ijQD7vuyRldtzn1ernesLb7ja0haBDgX2AK4l1RBfw/4\nSuOdLRt+zn25SFqFdPV4BeAaYAfSOr+fi4jnc8ZWdT5WysNllY+kOYDvA/sAz5Bmpf9yRPwga2A1\n4Nzn5XrH2uGGrwEgaXPgj8DswJ3AFhHxQt6o6sG5LxdJawLjSOV1O7BBRLyZN6p68LFSHi6rfCTt\nBfyIlPv7gR0iYnzOmOrCuc/L9Y4Nxl2da07SzJJOIHXHuQHYDVia1F1kw6zBVZxzXz6S9gVuBh4C\njgBWB26VtGLWwCrOx0p5uKzykTSPpAuAnwNnAx8EZgPukbRr1uAqzrnPy/WOtcsNXxsHfBE4PCK2\ni4gLSd1DHgbGSPpG1uiqzbkvEUmXAD8GfgasHxEnAuuRrizfLWnvnPFVnI+V8nBZ5XMfsBWwc0Qc\nEBH/B6wF/Ab4paRzs0ZXbc59Xq53rC3u6lxzkh4kzUJ4T4vXDgWOj4g5ex9Z9Tn35SLpaWDviLii\nafvswKnAARExc5bgKs7HSnm4rPKRNAbYPSKeaPHazsCZEbFg7yOrPuc+L9c71i43fGtO0jsi4uUB\nXl8zIu7uZUx14dyXi6RFIuLpAV7fLiIu72VMdeFjpTxcVvlIUgxwUidpyYj4Zy9jqgvnPi/XO9Yu\nN3zNzMzMzMys0jzG1/olaRVJR+eOo46c+3KRtLSkPXPHUUc+VsrDZZWPpLUlnZU7jjpy7vNyvWON\n3PC1gawKHJM7iJpy7stlXdJMntZ7PlbKw2WVz7LAp3MHUVPL4tzn5HrH3uaGr5mZmZmZmVXaLLkD\nsN6T9Gibu76jq4HUkHNfLpJuaHPXhbsaSA35WCkPl1U+kt7KHUNdOfd5ud6xoXDDt56WAh4grTs3\nkKXxCf1wc+7LZRPg8eIxEC9jNPx8rJSHyyqfN4HbgTGD7LcK8LHuh1Mrzn1ernesY2741tNDwAMR\nscdAOxVrz23Ym5Bqw7kvl78Dt0bEXgPtJOnjwEU9iag+fKyUh8sqn/uBpyPiawPtVOTeja/h5dzn\n5XrHOuYxvvV0B7BOm/uqm4HUkHNfLu2WV+DyGm4+VsrDZZXPnTj3uTj3ebnesY75jm89/Qx4rI39\nxgKbdTmWunHuy+VXwCtt7Hc7sHeXY6kbHyvl4bLK5/vALW3sdwWwXJdjqRvnPi/XO9YxRUTuGMzM\nzMzMzMy6xl2dzczMzMzMrNLc8DUkLSHp25LukPSopNWL7YdIWj93fFXm3JeLpLkkjZZ0saQxkt5d\nbP+kpJVzx1dlPlbKw2WVj6Q1JV0q6VlJb0paq9h+gqStc8dXZc59Xq53rB1u+NacpNVIMxPuAUwC\nlgFmK15eBjg4U2iV59yXi6SlSMsmnAK8G9gYmKd4eTPgsEyhVZ6PlfJwWeUjaUPgVmBl4AKmPceb\nAuyXI646cO7zcr1j7XLD104DHiRNvLAT0858Nw4YlSOomnDuy+U04DVgJWBtpi2vscBGOYKqCR8r\n5eGyyudE4GpgNeCLTa/dBazV84jqw7nPy/WOtcWzOtuGwK4R8ZKkmZteexpYNENMdeHcl8uWwL4R\nMaFFeT0BLJEhprrwsVIeLqt81gJ2ioiQ1Dxz6bPAwhliqgvnPi/XO9YW3/G1KQO8thDtLeViQ+Pc\nl8tswIv9vDYv8GYPY6kbHyvl4bLK51XgHf28thjwQg9jqRvnPi/XO9YWN3ztNvpff3QX2lujzobG\nuS+X+4Cd+3ltG+DOHsZSNz5WysNllc+fgEOa7nj13X3cB7ih9yHVhnOfl+sda4u7OttxwHWSriFN\nyBDAFpIOBj5GmsDHusO5L5dTgIslQSovgFUl7UA6sdk+V2A14GOlPFxW+XyNdIJ/L3AxKfeflvRt\n0rwE62aMreqc+7xc71hbFNE8FMHqRtJ2wHeBFRo2jwcOjIgrswRVE859uUjajzSJyTxMnTzjReDw\niDgzW2A14GOlPFxW+RRL6JxCOtGfmdQF9GbgixFxd87Yqs65z8v1jrXDDV97m6QVgXcBkyPi4dzx\n1IlzXx6S5gI+QFFewLiI6G/srw0zHyvl4bLKR9IcwALAvyPi5dzx1Ilzn5frHRuIG741J2lp4LmI\neKnFa7MCi0XExN5HVn3OvVl7fKyUh8sqH0kbAxMjYnyL1+YB1oyIm3oeWA0493m53rF2eXIrGw88\nLGmdFq+tBTzW23BqZTzOfWlIekvSXyQt0+K19SW9lSOumhiPj5WyGI/LKpcbgfslfbTFa6sCY3ob\nTq3ciHOf03hc71gb3PA1gJeAsZJ2yh1IDTn35SHSWoy3SRqVO5ga8rFSHi6rfO4BLpX0hdyB1JBz\nn5frHRuUG74GsBdwNvBrSV/OHEvd7IVzXya7AX8GbpD0idzB1Mxe+Fgpi71wWeVyKPBV4BRJP5Lk\n87zece7z2gvXOzYIL2dkAG9FxEGSHgS+K+ndwH65g6oJ575cXgR2AE4GfilppYg4LnNMdeFjpTxc\nVhlFxKmSHgZ+CSwn6X9yx1QXzn1WrndsUL4aZW+LiDOA7YCdgauB+fNGVB/OfXlEcjjweeAoSecB\ns2cOqzZ8rJSHyyqfiPgDsBGwCjAOWC5vRPXh3OflescG4oavTSMirgE+CCwD/CpzOLXi3JdLRPwc\n2ArYFvhF5nBqxcdKebis8omIe4H1gf8C5+SNpl6c+7xc71h/3PC1scB/GjdExIPAeqSxjJ7+vXuc\n+3KZALzWuCEixgKjgFeyRFQfPlbKw2WVz7nAvxo3RMRTwCakxpeX0+ke5z4v1zvWFq/ja2ZmZmZm\nZpXmO76Gku0lnSrp7L51SiVtImnx3PFVmXNfPpLeK+kgScdIWrTYtqKkeXLHVmU+VsrDZZWPpLkk\njZZ0saQxxQQ/SPqkpJVzx1dlzn1ernesHb7jW3OS5geuII1FeRGYG1g3Iu6SdD7wXESMzhljVTn3\n5SJpduB8YCfSmr7B1PK6FPhbRHwlZ4xV5WOlPFxW+UhaCrgRWBJ4CFidqbn/CTBzRHw2Y4iV5dzn\n5XrH2uU7vnYKsBSwAbAg6YS+z3XA5jmCqgnnvly+CWwB7AEswrTldSXw4RxB1YSPlfJwWeVzGmke\ngpWAtZk292NJMw1bdzj3ebnesbZ4HV/bATgsIm6VNHPTaxNJFYl1h3NfLrsCR0XEBS3K6zFg2d6H\nVBs+VsrDZZXPlsC+ETGhRe6fAJbIEFNdOPd5ud6xtviOr81NqpRbmYNpr5rZ8HLuy2VB4MF+XpsJ\nr+XbTT5WysNllc9spG6ercwLvNnDWOrGuc/L9Y61xQ1fe5i0FmkrmwD39zCWunHuy+Ux4AP9vLYe\nqTytO3yslIfLKp/7gJ37eW0b4M4exlI3zn1ernesLe7qbD8ETpf0AnBBsW0+SXsDBwH7Zous+pz7\ncjkPOELSeOCSYltI2gz4AvD1THHVgY+V8nBZ5XMKcLEkmJr7VSXtAOwDbJ8rsBpw7vNyvWNt8azO\nhqQTgcNIXUH6ZqudApwcEUfmjK3qnPvyKMYN/RLYhTSJyezAK6RuVBdGxKcyhld5PlbKw2WVj6T9\ngBOBeZjavfNF4PCIODNbYDXg3Oflesfa4YavAVCsd7YVsDAwGbg2Ih7NG1U9OPflImkjYGumltdV\nETE2b1T14GOlPFxW+UiaC/ggU3M/LiL6G39qw8i5z8v1jg3GDV8zMzMzMzOrNE9uZUiaS9JoSRdL\nukHSu4vtn5S0cu74qsy5Lxcl20s6VdLZxdVlJG0iafHc8VWZj5XycFnlI2kJSd+WdIekRyWtXmw/\nRNL6ueOrMuc+L9c71g5PblVzkpYCbgSWBB4CVieNTwHYDNgC+GyW4CrOuS8XSfMDVwDrk8ZtzQ38\nAJgAfA54DhidLcAK87FSHi6rfCStBtwMvAXcCqxJWmYHYBnS7PO75Ymu2pz7vFzvWLt8x9dOI03U\nsxKwNtOudTYW2ChHUDXh3JfLKcBSwAakNX0by+s6YPMcQdWEj5XycFnlcxpprfHlgJ2YNvfjgFE5\ngqoJ5z4v1zvWFt/xtS2BfSNiQjFrbaMngCUyxFQXzn257AAcFhG3tiiviaRGsXWHj5XycFnlsyGw\na0S81CL3TwOLZoipLpz7vFzvWFt8x9dmI3XbbGVe4M0exlI3zn25zE36Am1lDqa9wmzDy8dKebis\n8pkywGsLkZZfs+5w7vNyvWNtccPX7gN27ue1bYA7exhL3Tj35fIwaZmEVjYB7u9hLHXjY6U8XFb5\n3Abs3c9ruwC39DCWunHu83K9Y21xV2c7BbhYEsAFxbZVJe0A7ANsnyuwGnDuy+WHwOmSXmBqec0n\naW/gIGDfbJFVn4+V8nBZ5XMccJ2ka0i5D2ALSQcDHwM2zhlcxTn3ebnesbZ4HV9D0n7AiaQZ8Pq6\na74IHB4RZ2YLrAac+3KRdCJwGKmsRDq5mQKcHBFH5oyt6nyslIfLKh9J2wHfBVZo2DweODAirswS\nVE0493m53rF2uOFrQFr/DPgA8C5gMjAuIvobL2HDyLkvl2Lt3i2ZWl7XRsSjeaOqBx8r5eGyykvS\nihS5j4iHc8dTJ859Pq53bDBu+JqZmZmZmVmleYxvzUkadNxJRNzUi1jqxrkvF0lLD7ZPREzsRSx1\n42OlPFxW+Ujac7B9IuK8XsRSN859Xq53rF2+41tzkqaQxin2KyKa10SzYeDcl4vLKx/nvjxcVvkU\nuW/l7fJw7rvDuc/L9Y61y3d8bbMW2xYEPkJaouWg3oZTK859uXyG6b9Y+8prOdKsntYdPlbKw2WV\nz3IttvXlfjdg996GUyvOfV6ud6wtvuNr/ZL0HWD2iDggdyx149yXi6RfABMi4qjcsdSNj5XycFnl\nI+kY4D0RsVvuWOrGuc/L9Y41mil3ADaiXU5aeN16z7kvl/NJd4St93yslIfLKp+bge1yB1FTzn1e\nrnfsbW742kDeQ1qj1HrPuS+XdwFz5A6ipnyslIfLKp9RwEu5g6gp5z4v1zv2No/xrbl+ZiKcDVgd\n2Ae4tLcR1YdzXy79zBrZV15fJV3Vty7wsVIeLqt8JB3dYnNf7rcDTu9tRPXh3Oflesfa5TG+NTfA\nTISvARcBB0fECz0MqTac+3LpZ9ZIFf+OBT4VEZN6G1U9+FgpD5dVPv3k/jVgAnAh8K2IeK23UdWD\nc5+X6x1rlxu+NSdpmRabX42Ip3seTM049+UiaZMWm18lTWr1VK/jqRMfK+XhsjKzXnO9Y+1yw9fM\nzMzMzMwqzWN8zczMzEpO0tKd7B8RE7sVS90492bl4Du+NdfPuMX+RET4Yskwce7LRdJjdFZeK3Qz\nnjrxsVIeLqt8Osw9ETFzF8OpFec+L9c71i4XvB0H7A3MCfwBeBpYFPgI8DJwNh1U5tYR575cxgIf\nAhYBbiGV1yLABsBTwA35Qqs8Hyvl4bLKZ3/gSOA/wK+ZmvtdgLmBb5Im+7Hh59zn5XrH2uKGr71B\nmnXwwxHxct9GSXMBVwNvRMQ3cwVXcc59uYwD1gM+GBH/7NsoaSngKmBcRPw0V3AV52OlPFxW+awC\n3AV8LBq680k6FrgMWCUivpAruIpz7vNyvWNtcVfnmpP0OHBgRPy+xWs7Aj+IiKV6H1n1OfflIukR\n4IiI+E2L13YBToiIFXsfWfX5WCkPl1U+kp4G9oqIK1u8tg1wTkQs0vvIqs+5z8v1jrVrptwBWHYL\nkRb5bmU2YMEexlI3zn25LElavqiV14AlehhL3fhYKQ+XVT5zAwv389q7gLl6GEvdOPd5ud6xtviO\nb81JuhlYANgyIiY1bF8CuBb4V0S0Wr/UZpBzXy6S7gT+C2wVEa82bJ+TVF5zRsTaueKrMh8r5eGy\nykfS5cD7SN1tb2/Yvh5wKXBPRHwkV3xV5tzn5XrH2uWGb81JWpM0Kc+cwP8xdcKeUaQJAT4UEffk\ni7C6nPtykbQ5cDlp8pIrmFpe2wLzAttEhCe46gIfK+XhsspH0nLAdcCywONMzf1SwGPAFhExPld8\nVebc5+V6x9rlhq8haUHgi6QKYjHgSeBW4DsRMTlnbFXn3JeLpFWAo5i+vI6PiIdyxlZ1PlbKw2WV\nj6RZgb2YPvfnRsQbGUOrPOc+L9c71g43fM3MzMzMzKzSPLlVzUmaSdIsTds+LOnQouuIdYlzX36S\nVpW0s6TFc8dSZT5WysNllY+khSQt3bTt85J+IMnjS7vIuc/L9Y61y3d8a07SRcBrEbFn8Xw/4IfF\ny28A20XEdbniqzLnvlwknQ7MEhH7Fc93An5NuoD4H9KkGrcP8BY2RD5WysNllY+k3wP/jIgDiudf\nA74BPA/MB+wWERdlDLGynPu8XO9Yu3zH10aRJurpczjwM9JkPZcCR+YIqiac+3LZBhjX8PwbwB9I\nM3neBhyTI6ia8LFSHi6rfNYBrm94vh9pffEFgTNI4x+tO5z7vFzvWFvc8LV3AU8ASFoRWA44PSJe\nBM4G1sgYW9U59+WyGDAeQNKSwGrAtyLifuD7wLr5Qqs8Hyvl4bLKZwHSbLZIWh1YFDi3eO0y4D2Z\n4qoD5z4v1zvWFjd87T9MXdh7U+DZiLiveP4WMEeOoGrCuS+Xl4G5i583IZXfHcXzl4B5cgRVEz5W\nysNllc9kYMni5w8BkyLikeL5rPicr5uc+7xc71hbZhl8F6u4ccBXJL0JHMK0XUVWBP6ZJap6cO7L\n5S7gQEkTgQOBayNiSvHacqSlE6w7fKyUh8sqn+uAr0taCDiUdKexz8rAhCxR1YNzn5frHWuLJ7eq\nOUnvJlUQKwCP0rDIuqQbgAkRsXe+CKvLuS8XSesCV5EmKvk3sFnfFWVJvwNejohdM4ZYWT5WysNl\nlY+kRYDzSeMdbwd2iYhni9duA+6MiP0zhlhZzn1ernesXW74GpAW/m5e4FvSGsBTEfGvTGHVgnNf\nHpLmIl29fyQi/tOwfbti29+yBVcDPlbKw2XVG5Jmi4jX29jvncCr7exr7XHuRx7XOzYYN3xrSNLD\npG44l0XErbnjqRPnvlwkXQP8FvhdREzKHU+d+FgpD5dVPpL+Q+qJ8lvg8sYLctZdzn1erndsKDzY\nvp7OBjYG/iTpSUlnStpG0my5A6sB575cHgWOAh6XdJukIyStmjuomvCxUh4uq3z2JU3e8yPgX5Ku\nkXSApMUzx1UHzn1ernesY77jW2OSFgV2BHYANgNeA67GVy+7zrkvF0mjSGW1I2lZikeA3wG/9ZXm\n7vKxUh4uq3wkzQpsTsr99qTldO5k6h2xBzKGV2nOfV6ud6wTbvgaAJLmAbYjVRzbAHMCY3E3z65z\n7stF0spM/ZJdD3gG+AOpvK73OK7u8bFSHi6rvHyxLh/nPh/XOzYYN3xtOq2uXkbEzHmjqgfnvlwk\nLUYqq7evNEfEvHmjqgcfK+XhssqruFjX1xBbNyK8lGWPOPf5uN6xVtzwtUFJWj8i/pw7jjpy7suj\nmLlzm4i4KHcsdeRjpTxcVvlIWjQinsodRx0593m53jFww9csC0kLAG82LYmzHmmh9X+4ch75JM0B\nnACcHhGP5o6nLiS9A1gDCOD+iHglc0jWQrH01+7AmsAU4DbgVxHxWtbAKkbSLsDVEfFC7ljqqKiP\nNiBNFnt9RLwpaU7gs8AqwD+B8yLinxnDrDRJIq2fvDqwAKm+eRK4JSIeyxmbjTxu+NaUpG2BLwFL\nAA8AJ0XEuKZ91gfGuWvI8Cm+EC8gdbsB+DnweeAXwK6ASCf0VwA7RcQbOeK0wUmaF3gO2DQibs4d\nT9VIGg1cGBHPNGw7AjgSmKPY9Arw9Yg4NUOIVpB0FvBiRBxcPF8WGAMsA/Rd3Hsn6btmM6+nOXwk\nTQFeBX4PnEtqBE/JG1U9SFoGuA5YnvTdfTewFWmJo7WBfwPzAc8DoyLikUyhVpak3YCTgcWaXyKd\nS10NHBAR43scmo1QXs6ohiRtRJqMp2/mwfcDN0k6Omtg9XAYsDVwEvBl0iQMZwEfBT5DupM1mjQu\nZZ9MMVpB0sT+HsBfSF+uFxfbJmQOt2q+Ayzb90TSZ4DjSScynywe1wMnFXe9LJ8tgVsann+HdGyM\nioj5ImI+0l2xd5JOUm143UKayOePwBOSTpG0euaY6uBYYBZgW+CDwH9J51bvBFaKiAWAlYHJwDdy\nBVlVRaP3fNIFh9GkmwjnkmZ1PgTYDZgHuEXSErnitJHFd3xrSNJ1pDslO0bEW8UEAN8gNcR+GhH7\nFfv5ju8wk/QgcH5EfLN4vgVwDfDViDipYb8TSXdG1s8TqcHbd1OeJJVRs9lId+mvBp4CiIi9exdd\ntRW5HxURtxXP7wX+FhH/07Tf74AFI2LDDGEaIOlVYIuI+FPx/EXgcxFxYdN+ewKnRcTCGcKspL7j\nBLgf2Bn4NGmiPQH3AOeQupg/myvGqiougB4ZEb8onq9GKofdI+KChv0+DRwbEcvkibSaJN0H/Cki\nDmjavg9wHLAU6QbfzcADEfGZ3kdpI43v+NbTe4EfRsRbABHxRkQcAewB7C3pAklu7HbHMkBjl/K+\npQ3GNe03ltR9yvLajVRPLgocFxF79z2AA4t9vtWwzbpnFeC8FtvPIdVpls/TpLqtz8xAq+7MzwBz\n9SSimomIVyLi/IjYEliaqUMCvke6C/xbSTtmDbJ6FgImNjwfX/w7sWm/CcW+NrxWAi5tsf0S0nf2\nSsVwsR+SekSYueFbU7MC0601Wlyh/DjwMdKaZ3M072Mz7DVg9obnrxb//rfFfu/oSUTWr+KO1crA\n48B9ko6WNFvfy/kiq43GHL9MGjPX7AVSnWb5/Bb4cjGhFcDlpMl9mn2WdEfMuigiJkXEiRGxGrA+\n8FNgQ1KDwIbP46SxvH3WLf5dp2m/dUiTXNnwepbU+G22Mum7o++8agJprLUZXk+snv5O+jK8vvmF\niPiDpI8Al5FmyLPhNZG0oP1VAEVX8zVIZdJoeYrus5ZXMVvqvpLOBX4C7C7pQNIstdZdZxbdZiHd\nRVyJ1G2t0bKkEyDL5+ukcb5/kfRT0tCAUyXdCdxQ7LMlsCppPgPrkYi4Hbhd0hdIc0rY8LkQOFbS\n/MCLpHGmPwSOkfRv4A5gPdLd959li7K6fgN8s/iOuIp0Q2cU8H3g3ojou/O+ODApT4g20rjhW0/X\nAZ+RdGKr2R8j4npJW5Gu2tvwugJ4d+OGiPhri/3+h6ndoG0EiIhbJL0f+ArwO9KFI9/17Z6bmDa/\nd5DGbDXbCbivJxFZSxHxb0kbACcytYutSEsZrVnsdgewdUTc0PpdrJuKLp+X5Y6jYk4kfZ9/hXRh\n7jxS4/dF0qSVQToO7iUtfWfD6wjSEJhzmfa74iHgUw3PlyGtpmHmya3qSNKipO45NzeuI9tiv/eQ\nJpc5t2fBGQDFXeCnG5dysZFD0oqkq8qrAJ/om4DJek/ShsCTEfGP3LHY2+uark26yzITaUbbv0bE\nE1kDq6hiSZ0nI2K64UvWG8UyhTNHxEsN21Yj9Zp7inSu5SWmuqT4DlgfeAt4GLimbw4bs2Zu+JqZ\nmZmZmVmleXIrQ8n2kk6VdHZxBRlJm0haPHd8VSZpLkmjJV0saYykdxfbPylp5dzx2fQkvVfSQZKO\nKXpPIGlFSfPkjq3KXE+Vh8sqH3+n5OPPfV7Ov7XDd3xrrpiU4QpSN5EXgbmBdSPiLknnA89FxOic\nMVaVpKWAG4ElSWNSVmdq7n9C6jrVamZUy0DS7MD5pDGlIo0p6iuvS0lrzH4lZ4xV5XqqPFxW+fg7\nJR9/7vNy/q1dvuNrp5AmjNkAWJB0Qt/nOmDzHEHVxGmkZYtWIo2Ja8z9WGCjHEFZv74JbEFa73oR\npi2vK4EP5wiqJlxPlYfLKh9/p+Tjz31ezr+1xbM62w7AYRFxq6SZm16bSOtZVG14bAnsGxETWuT+\nCWCJDDFZ/3YFjoqIC1qU12OkZXWsO1xPlYfLKh9/p+Tjz31ezr+1xXd8bW7SF2IrfUtSWHfMRuqS\n08q8wJs9jMUGtyDwYD+vzQTM3sNY6sb1VHm4rPLxd0o+/tzn5fxbW9zwtYeBrfp5bRPg/h7GUjf3\nATv389o2wJ09jMUG9xjwgX5eW490LFl3uJ4qD5dVPv5Oycef+7ycf2uLuzrbD4HTJb3A1AW+55O0\nN3AQsG+2yKrvFOBiSTA196tK2gHYB9g+V2DW0nnAEZLGA5cU20LSZsAXgK9niqsOXE+Vh8sqH3+n\n5OPPfV7Ov7XFszobkk4EDiN1BembrXYKcHJEHJkztqqTtB9wIjAPU7vivAgcHhFnZgvMplOMG/ol\nsAtpApnZ0l1RGQAAEj5JREFUgVdI3agujIhPZQyv8lxPlYfLKh9/p+Tjz31ezr+1ww1fA6BY72xL\n4F3AZODaiHg0b1T1IGkuUhfavtyPi4j+xmlZZpI2Is3g3FdeV0XE2LxR1YPrqfJwWeXj75R8/LnP\ny/m3wbjhW2OSZgNOAi6IiNtzx1MnRe4vAr4TETfljscGVpTX/sD1EfGX3PHUieup8nBZ5ePvlHz8\nuc/L+bdOeHKrGouI14HPA3PmjqVuitxvgY/BUijK60Rggdyx1I3rqfJwWeXj75R8/LnPy/m3TriC\ntLuBNXIHUVO3AKNyB2FtexBYPncQNeV6qjxcVvn4OyUff+7zcv6tLZ7V2Q4FfiVpAnB5uO97Lx0K\nXCbpJeAy4EnSZAxvi4gpOQKzlo4Gvifpzojw0gi95XqqPFxW+fg7JR9/7vNy/q0tHuNbc5IeJy1s\nPxfwBvAvpv2ijIhYJkdsVSep7wSkv4MwIsIXp0YISTcDKwELAuOZ/qQyImKTDKFVnuup8nBZ5ePv\nlHz8uc/L+bd2uQK06+n/S9K661ic+zJ5C3ggdxA15XqqPFxW+fg7JR9/7vNy/q0tvuNrZmZmZmZm\nlebJrczMzMzMzKzS3NW5hiTtSRr8P7n4eUARcV4PwqoFSUcDP4uIScXPA4mIOK4XcVlrkjYG7oqI\nl4qfB+T1M4eP66nycFnl4++UfPy5z8v5t6FwV+caKibAGBURtzVMhtGfiIiZexFXHTj35dKivPqr\nMIXLa1j5WCkPl1U+zn0+zn1ezr8Nhe/41tNywKSGn61HImKmVj/biLUZUye02ixnIDXkeqo8XFaZ\n+DslK3/u83L+rWNu+NbTd4AvAX8HNqHoKpI3pHqQdBewR0T8tbGLWu64rF87AI8CL5Hu9t4VES/l\nDak2XE+Vh8sqE3+nZOXPfV7Ov3XMVwfraQdggeLns4EVMsZSN2sAcxc/HwMsmTEWG9zBwGLFz2OA\nVTPGUjeup8rDZZWPv1Py8ec+L+ffOuY7vvX0NPAB4DaKsYl5w6mVScCOkp4m5X5RSUv3t3NETOxZ\nZNbKs8C78bGSg+up8nBZ5ePvlHz8uc/L+beOeXKrGpL0XWA07VUSERG+QDJMJB0BHE+bFbQnY8hL\n0i+AT5BOLpcGngRe62f3iAhfcR4mrqfKw2WVj79T8vHnPi/n34bCH4J6+gJwC6nb5jHAOcATOQOq\ni4g4QdK1pNyfDXyLNIbURqbPA/eRymvP4udns0ZUH66nysNllYm/U7Ly5z4v59865ju+NSfpMWDH\niLg3dyx1I2kMsH9EPJQ7Fhtc49IJuWOpG9dT5eGyysffKfn4c5+X82/tcsPXzMzMzMzMKs2zOpuZ\nmZmZmVmlueFrZmZmZmZmleaGr5mZmZmZmVWaG75mZmZmZmZWaW74mpmZmZmZWaW54WtmZmZmZmaV\n5oavIekGSde02H6tpOtzxFQXkh6T9HCL7Y9I+keOmKx/ks6SdGaL7T+V9PMcMdWF66nycFnl4++U\nfPy5z8v5t3bMkjsAGxGWB15vsX05fHGk2yYAb7bYPhHnfiTai1Re+zZt3xsQsE+vA6oR11Pl4bLK\nx98p+fhzn5fzb4NSROSOwczMzMzMzKxrfAXEzMzMzMzMKs0NX3ubpHkkjZK0nqR35I6nbiTNJmm0\npMVzx2JmNlSS3iFpieLh75JMJC1QfJ8vkTuWOvG5VH6SZpG0vaQFcsdiI4sbvjUkaRdJezY8n0nS\nacC/gFuAW4F/Sfparhhrak7gO8AKuQOxqSStI+l7kk6TtHKxbS1JV0j6W/HvB3PHWVeSNpZ0Q+44\n6k7S4pK+K+kx4EXSmNKJwIvFhEvfdQNs+EmaWdK3JD0h6UlJhxbbDwcmkb7PJ0r6jaTZswZbMT6X\nGtHmAn4LrJY7EBtZPLlVPR0J/LTh+VHA6GJb34x42wBHS3o+Ik7vcXyVJemmAV6emTRB0umSXgAi\nIjbpTWTWiqRRwFgggNeAfSRtC1wOPAPcC6wDXC9pnYj4a7Zg62thwMdJRpJWB8aQLqb/Afgr8Fzx\n8gLAqsDuwO6SNo2Iv2QJtJoOAb4E/Bp4Afi6pDmBrwGnALcBo4BDgS8C38oUZxX5XCojSecN8PKs\npPOpoyQ9TTqf+nRvIrORzJNb1ZCkl4DtI+KG4vlE4JyIOLppvxOBHSNi5QxhVpKkKcDTwEMtXp4F\n2IDUmPo3QERs1rvorJmky4H5gA8DLwOnAx8D/gJsGxFvFF3ZxgCPRMTu2YKtGElLt7nrtsAZETFz\nN+Ox/km6lnSiuX1E/Keffd4J/B54PSK26mV8VSbpL8BvI+JrxfOPAb8Bjo+Irzfsdxzp+3yNLIFW\nkM+l8irOp16gOF9qMhOwJOkC9aukhu/yPQzPRijf8a2nN4DZGp4vBlzXYr9rSVeTbfgcQbpK/BDw\n1Yh4u8KWNB/pLsnBETHQnWHrnbWAgyLiJXj7BGY/YP+IeAMgIl6WdDpwdP9vY0MwnnSnfTBqcz/r\nng8AO/XX6AWIiP9I+hZwSe/CqoVlgcY1Sq8jnfQ3d/8fg7/Ph5vPpfI6E/gU8GPg1Ih4q++FhvOp\nT/h8yhp5jG89/R/wPw3PHySd4Ddbi3R30oZJRJwIvI+0rtzDjeOD8Mn7SDQf6Ypxn0nFv4837TcB\n8PjF4fUKqbvgvoM8fpIrQHvbK6RjZTDzke6+2PB5AViw4Xnfz82T+iwI9HthwobE51IZRcR+wNak\nYRT3Sdq48eU8UdlI5zu+9fQNYKykfwPfBr4M/FJSMPVq5YeBY4rXbRhFxKPA1pJ2A74taR/SXcRJ\nA/+mZTCZdBW/z1ukO1aTm/ZbCPhvr4KqiXuBtyLi5wPtVNRj+/YmJOvH74BTJT3V390VSRsBJwOX\n9TSy6vszcKSke0iTip0MPAB8RdINxZ32eYHDgbszxllFPpfKLCJukbQmaZz7lZIuJo1nfyNvZDZS\neYxvTUnaDjiLdML+HGkGvMYZHwWcA+wbEW/2PMCaKLrjnEq6YvlzUgN4M3fNGRkkXUkauzt6kP1O\nADaOiA17E1n1SfoB8PGIWGyQ/XYGfhMR7sGUSVGP/ZHU5XkSaQz888XL85NmVl2CdIdsu8YhHjZj\nJK0K3MjUO73PAh8kzWi7NPB3YEXSqgEbRcRtGcKsLJ9LjRySVgR+BKwNnAScgM+nrIkbvjUmaW5g\nF9KESouTur5PJs3IeZlnqO2d4m7Ij4FVgE1dUY8MktYC5uubvGSA/X4M/Dkizu5NZNVXLH2zYkSM\nzR2LtUfSDsBHSQ3dvq62z5O+U34P/D580jHsJC1GyvuspItAz0haiHQHcnXgKeDHEfHnjGFWls+l\nRhZJuwOnkWb89/mUTcMNXzMzMzMzqwRJc5Huwj8VEa/ljsdGDncNMzMzs66Q9P/t3XuoZWUZx/Hv\nb0a8kEql3agZdCyyC5amaIJlJXmhiykUGlEYWWaKUdhdwdIyA8XUUbQoTMzLaEWpTFZgZTcvKN00\nNcfL6JSjjuYl03n6412TezZnRst9ztrt/f3A5rDe9e59nrWGOWs/633W+85Lsl237JfmkOdeU2wz\n4BGTXg0z8Z1SSTZJckSSnyVZkeSx7rWiazvCi2V/krwhyXrLazV3kuye5L1d6fNM+1+cxOWMRizJ\ngiQfT3JYV7pJkoVJTkuytPv50r7j1HptRptU6XV9BzKFPPdzLMmWSY5JcmmSS5IcnWR4hm2NQHdd\n3meo7bAky4E7gbuSLEvyvn4i1Diy1HkKJVlAW+NvK+CXtOdQ7u12Pxd4Je1ZlWXAW6rqth7CnGrd\nhD3nV9X8vmOZZt2zW0uBnXlyvdgfAwdV1fKBfjsDV/rvNTpJXgH8Cti8a1oOvIU2W+qmtEl7tgUe\nA7b371R/khyznt0b0WYUPpt2TamqOnpOApsCnvv+JLkX2KOqrum2FwBXAi8Ebuy6vZy2/N0uVeWS\nRiOU5Le0Z9pP6LY/CpwCXEa7bgPsDewBHFhV5/USqMaKie8USrIEeBnwjqq6dR19tqItO3FzVe0/\nZ8FNuCQLn2bXfYBTTaT61c3WfAhwOPA7YHfaEhaPAXtW1R+7fia+I5bkPNokSfvRbsydQZuo5wHa\nl81VSV5Am9H2Z1X10b5inXZJVtNuCmUdXQb3lf9PRsdz35/u3O+yZqbsJOfQkqy9qurarm1H4FLg\nwqo6pLdgJ1CSVbSZ/3/cbf8FWFpVhw71OxPYqape20OYGjOWOk+nPYDPrSvpBej2HdX11ejcCvz1\nabxO6yk+rW0/4OiqOruq/lxVpwM7ACuAK5Ls1G94E21X4MtVdWNV3QN8mnbD7mtVtQqgG0E5CXhT\nf2GKNrqyAjigquYNvmhVRKHNrjrPxGvkPPfjY0/g2DVJL0BVXQV8hXYzW6M1D1g9sL0VcMEM/c6n\nVQdJbNB3AOrFfzPMb0nAaD0CXAFc+BT9dgQOnv1w9BQW0p6R+4+qujPJG2nrll7eLeHySB/BTbjn\nAYPly7d2P28Z6ncDsGAuAtLMqmqvJAcAJyU5CDi0qm5as7vH0Cae536sPJuh60XnGlr5s0brGlop\n80+67WXAIloV0KBFPLmuuKacie90uhw4Nsnvq+qvM3XoSp2/SHueUaNzHfBEVX1jfZ2S3I+J7zj4\nG/CS4caqeijJ3sAS4Ee0NQM1WvfRkt81ngCuppU6D9qcVnquHlXVuUkuA44Hrk9yAnBcz2FNBc99\nr3bs5oIA+DtPzkkw6NnAw3MX0tQ4HvhekmW0R2G+CHw1yUra91xoo/BfAr7bT4gaN5Y6T6cjgE2A\nG5P8PMniJMd1r8VJrqBNzLAJ8PFeI508V/P0Z9hc1zNbmjtXAe+caUdVPdrt+xHw+bkMakr8kTap\nGABVtbqqdqqqG4b6bQfcPKeRaUZVdV9VHQy8FdifNnHiPjjyOOs89735Om2A4HLgBcAbZ+izA200\nUiNUVZcAh9ES4JXAx4CNgYtoN0gfoJU+Xw98pqcwNWYc8Z1CVXVHku1oI4pvB/alPQsEbZTlD7SZ\nIM+sKu9SjtZXeOoyZ6pqCd6YGgfnAp9MskVVrRzeWVWPJ3kP7ZnsveY8usl2PE/+XVqfHWjPcGlM\nVNUvkmwPfApYb3WLRstzP6dmmltg1QxtW+OI46yoqjO6aocP0lYjWU777rSS9l324i5BlgBndZYk\nSbOkW+JlEXBtVQ2XqWsWee4laW0mvpIkSZKkiWYppSRJkiRpopn4SpIkSZImmomvJEmSJGmimfhK\nktSjJO9PsjrJor5jkSRpUpn4SpLUP2ealCRpFpn4SpKkp5Rkw75jkCTpf2XiK0nSGEuyY5ILktye\n5OEkf05ybJKNB/qcnOTuJPOH3rtpkgeTHDfQtmWS05PckeTRJH9K8qGh960pv94tyflJ7gN+PesH\nK0nSLNmg7wAkSdJ6LQSuBb4J/AN4FXAUsDVwYNdnMXAo8C7gwoH3vhfYBDgdIMlmwC+BjbrPuBXY\nE1icZMOqOnXod38HOBfYH78zSJL+j3kRkyRpjFXVRYPbSa4EHgS+neTQqrqvqv6U5Argw6yd+B4M\nLK2q27rtI4AFwKur6pau7adJngMcnWRxVa0eeP8FVfXp2TguSZLmkqXOkiSNsSSbJTk+yU1J/gn8\nCzgbCPCyga6nAW9Ksk33vp2A7elGezt7Ar8BliWZv+YFLAW2BF450LeA783WcUmSNJcc8ZUkabx9\nC3gz8AXgOuAhYGfgFGDjgX4XAytoo75HAh8B7gR+ONDn+cA2tOR5WAFbDLXd9YyjlyRpDJj4SpI0\nppJsBLwDOKqqThlof81w36p6PMlZwCFJTgDeA5wwVLq8kpYcH04bMR52w/DHPsNDkCRpLJj4SpI0\nvjYC5gOPD7V/YB39zwA+C1wAbAicNbT/MuBjwO1Vdc/owpQkabyZ+EqS1L8Aeye5e6h9FfAr4BPd\nvnuAg4AXzfQhVbU8yQ9oszt/v6ruHOpyIvBu4BdJTqSN8D4L2BbYrar2HdUBSZI0Tkx8JUnqXwEn\nz9D+B+BttOWKTgEeAc6jLW30wxn6Qxvt3Zc2+rv2L6l6IMmutKWMjgReDNxPS4CXPLNDkCRpfKXK\nx3ckSZoUSc4BXl9Vi/qORZKkceGIryRJEyDJzrTli95NW69XkiR1HPGVJGkCJFkNPEgrhf7I0GzO\nkiRNNRNfSZIkSdJEm9d3AJIkSZIkzSYTX0mSJEnSRDPxlSRJkiRNNBNfSZIkSdJEM/GVJEmSJE00\nE19JkiRJ0kT7N7slqHwkNPFJAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa41be750>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot for a list of libs.\n",
    "# NB: This suggests that libDNN is faster than cuDNN on the conv1 and expand1x1 layers, but slower on the squeeze1x1, \n",
    "# expand3x3, conv/pool10 layers. (Recall that libDNN is not yet tuned for TX1 but uses parameters optimal for GTX 1080.)\n",
    "plot_time_per_image_per_layer(df_per_layer_info, model='deepscale-squeezenet-1.1', libs=['nvidia-cudnn', 'libdnn-cuda'],\n",
    "                              batch_sizes=16, direction=direction, ymax=0.65)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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TGT16NEOGDCExMTHP+5Nd9erVOXTo9ydXZW88RkVFMWjQIKZMmXJcDDt27OD0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\nJEmS9Ej+H1/ucr8EiPQvAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa3a4ec70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot for a list of libs. Only plot layers that consume between 5% and 10% of the total execution time.\n",
    "# NB: libDNN is slower than cuDNN on the expand3x3 layers and conv10 layers, but a bit faster on the conv1 layer.\n",
    "plot_time_per_image_per_layer(df_per_layer_info, model='deepscale-squeezenet-1.1', libs=['nvidia-cudnn', 'libdnn-cuda'],\n",
    "                              batch_sizes=16, direction=direction, lower=0.05, upper=0.10, rot=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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6LS1NV1RUaC8vrzrznxt7Fs7mxa5evVq7ubnptLS0OufWvt8ff/xRT5s2Tfv6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abncIL/lZGjYMKE7pMDn/SzV8WA900ar+Q2W70t/u2gJn8LXSu9U0Wr2Q512eQeriUAJ/cWdRwE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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa3c55910>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot for a list of libs and a list of batch sizes. (This works but might not be terribly legible).\n",
    "plot_time_per_image_per_layer(df_per_layer_info, model='bvlc-alexnet', libs=['nvidia-cudnn', 'nvidia-cuda'],\n",
    "                              batch_sizes=[4,6], direction=direction)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"plot_ideal\"></a>\n",
    "## Plot the ideal adaptive solution"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Overall, using cuDNN typically results in the minimum execution time. For some layers, however, other libraries may outperform cuDNN (e.g. libDNN from the OpenCL branch of Caffe). As we show below, using the best performing library per layer results in up to 17% execution time reduction over using cuDNN alone. For other models and on other platforms such adaptation can potentially results even in higher savings (e.g. up to 22% on the GTX 1080)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**NB:** Currently, the savings are hypothetical. However, Caffe allows for manual adaptation, i.e. the user can specify the engine to use for each layer in the model file (`*.prototxt`). We are working on generating the optimized model file automatically from the obtained ideal adaptive solution. Please [contact us](info@dividiti.com) if you are interested."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"plot_ideal_all\"></a>\n",
    "### Using all reasonable libs for adaptation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([u'cpu', u'libdnn-cuda', u'nvidia-cuda', u'nvidia-cudnn'], dtype='object', name=u'lib')"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_libs = df_per_layer_info.index.get_level_values('lib').drop_duplicates() \\\n",
    "    .drop(['nvidia-fp16-cuda', 'nvidia-fp16-cudnn', 'tensorrt-fp16', 'tensorrt-fp32'])\n",
    "all_libs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Each row specifies an ideal adaptive solution for a model. Each column specifies the execution time (in ms per image) that the ideal adaptive solution would cumulatively spend using a particular library."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>lib</th>\n",
       "      <th>cpu</th>\n",
       "      <th>libdnn-cuda</th>\n",
       "      <th>nvidia-cuda</th>\n",
       "      <th>nvidia-cudnn</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>deepscale-squeezenet-1.1</th>\n",
       "      <td>0.002339</td>\n",
       "      <td>0.825522</td>\n",
       "      <td>2.406508</td>\n",
       "      <td>3.063189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bvlc-alexnet</th>\n",
       "      <td>0.000500</td>\n",
       "      <td>1.656031</td>\n",
       "      <td>0.616434</td>\n",
       "      <td>4.804996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>deepscale-squeezenet-1.0</th>\n",
       "      <td>0.002098</td>\n",
       "      <td>0.861213</td>\n",
       "      <td>3.959106</td>\n",
       "      <td>6.824531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bvlc-googlenet</th>\n",
       "      <td>0.003723</td>\n",
       "      <td>0.166052</td>\n",
       "      <td>4.076741</td>\n",
       "      <td>11.693360</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "lib                            cpu  libdnn-cuda  nvidia-cuda  nvidia-cudnn\n",
       "model                                                                     \n",
       "deepscale-squeezenet-1.1  0.002339     0.825522     2.406508      3.063189\n",
       "bvlc-alexnet              0.000500     1.656031     0.616434      4.804996\n",
       "deepscale-squeezenet-1.0  0.002098     0.861213     3.959106      6.824531\n",
       "bvlc-googlenet            0.003723     0.166052     4.076741     11.693360"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ideal_all = get_ideal_adaptive_solution(df_per_layer_info, all_libs, direction)\n",
    "df_ideal_all"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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7b2ZmZmbWGPwcbitLUrjC3T4NWLd1PPfQzMzMzKwl+TncZmZmZmZmZq2UK9xm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\nhGnfrdStXNIDwCoRsVHRukOA66ixwi1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      "text/plain": [
       "<matplotlib.figure.Figure at 0xa4257a70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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PgA+BvpQREV8ANwJHFCQflv28rpJ2mpmZmZmZ2Xe3SGs34Dv6oMSxgN4Vvr9P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\ntdf4Tps2jR49etCp04Jdwp4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      "text/plain": [
       "<matplotlib.figure.Figure at 0xa3ebea10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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rWbTuumzdqDLpd8m27VDd2TeOctfQzMzMzMzMWl6rq4QDkb2KbQX8tYE09flt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\nsPbaa3P22Wfz/e9/v2w+zT0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ahfQs756kScn6kWYj/3UVZTqfNLv4jsD4KtL/b3Yeu1Yo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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa4209c30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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kYe71Iml34EHgv8AA4MfARsCzkroWXgZYB7gKuBzoD0wB7pLUp5ZLXA/sLWnN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\n1PgeRhrav25dr4DznHAzMzMzsyXjOeE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vqutPIpfLsXjxYq699lrq6+vJ5XJccsklzJs3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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa3e0c2f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_ideal_adaptive_solution(df_ideal_all, df_model_lib_mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "model\n",
       "bvlc-alexnet                0.954758\n",
       "bvlc-googlenet              0.931626\n",
       "deepscale-squeezenet-1.0    0.895721\n",
       "deepscale-squeezenet-1.1    0.821986\n",
       "dtype: float64"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Up to 17% execution time reduction compared to the best non-adaptive solution (i.e. cuDNN).\n",
    "df_best_lib = df_model_lib_mean.reorder_levels(['lib', 'model'])[cuda_level_performance].unstack('lib')\n",
    "df_ideal_all.sum(axis=1) / df_best_lib.min(axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"plot_ideal_cuda\"></a>\n",
    "### Using CUDA-level performance libs for adaptation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>lib</th>\n",
       "      <th>nvidia-cuda</th>\n",
       "      <th>nvidia-cudnn</th>\n",
       "      <th>libdnn-cuda</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>deepscale-squeezenet-1.1</th>\n",
       "      <td>2.414011</td>\n",
       "      <td>3.066857</td>\n",
       "      <td>0.825522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bvlc-alexnet</th>\n",
       "      <td>0.616434</td>\n",
       "      <td>4.806335</td>\n",
       "      <td>1.656031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>deepscale-squeezenet-1.0</th>\n",
       "      <td>3.962985</td>\n",
       "      <td>6.830830</td>\n",
       "      <td>0.861213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bvlc-googlenet</th>\n",
       "      <td>4.077942</td>\n",
       "      <td>11.704643</td>\n",
       "      <td>0.166052</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "lib                       nvidia-cuda  nvidia-cudnn  libdnn-cuda\n",
       "model                                                           \n",
       "deepscale-squeezenet-1.1     2.414011      3.066857     0.825522\n",
       "bvlc-alexnet                 0.616434      4.806335     1.656031\n",
       "deepscale-squeezenet-1.0     3.962985      6.830830     0.861213\n",
       "bvlc-googlenet               4.077942     11.704643     0.166052"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ideal_cuda = get_ideal_adaptive_solution(df_per_layer_info, cuda_level_performance, direction)\n",
    "df_ideal_cuda"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ZeuutGTlyJL/61a+a91933XXssssunH322Wy22WZsvfXW/OUvf2nX9a0sN4i7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\n+3z5gRGxTNKL5WmSxgM/BC4EbgNeJv0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      "text/plain": [
       "<matplotlib.figure.Figure at 0xa3c28430>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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AM0nDsScBRwEPR8QTtQ4YN24qI0ak9vO66/Zn9Og3tc8XKn0r7O3mbpfkUh9v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\nyy67cPbZZ7PZZpuxzTbb8Oi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      "text/plain": [
       "<matplotlib.figure.Figure at 0xa376d5f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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nA3OyxbwuKezlLWF8Vtc9IuLmiLgZ2BN4F5hQkHct4MjsNfozcDjwGnBQM+Xf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\nVlux1157ce6557ZLvduLA2Izq1m+h6p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      "text/plain": [
       "<matplotlib.figure.Figure at 0xa3be1eb0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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3oOKK0pHuH/Bz4PCihcUOATbO0s3MzMzMzKyNtbSo1gakRvOcNj7v80WLaj0j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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa3e0c710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_ideal_adaptive_solution(df_ideal_cuda, df_model_lib_mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "model\n",
       "bvlc-alexnet                0.954871\n",
       "bvlc-googlenet              0.932139\n",
       "deepscale-squeezenet-1.0    0.896342\n",
       "deepscale-squeezenet-1.1    0.823139\n",
       "dtype: float64"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Hypothetical execution time reduction compared to the best non-adaptive solution (i.e. cuDNN).\n",
    "df_best_lib = df_model_lib_mean.reorder_levels(['lib', 'model'])[cuda_level_performance].unstack('lib')\n",
    "df_ideal_cuda.sum(axis=1) / df_best_lib.min(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "model\n",
       "deepscale-squeezenet-1.1    1.001402\n",
       "bvlc-alexnet                1.000119\n",
       "deepscale-squeezenet-1.0    1.000694\n",
       "bvlc-googlenet              1.000550\n",
       "dtype: float64"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Up to 0.1% worse performance when using the CUDA-level performance libs only.\n",
    "df_ideal_cuda.sum(axis=1) / df_ideal_all.sum(axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"plot_ideal_cudnn_cublas\"></a>\n",
    "### Using cuDNN and cuBLAS for adaptation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>lib</th>\n",
       "      <th>nvidia-cudnn</th>\n",
       "      <th>nvidia-cuda</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>deepscale-squeezenet-1.1</th>\n",
       "      <td>4.323794</td>\n",
       "      <td>2.474317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bvlc-alexnet</th>\n",
       "      <td>6.469998</td>\n",
       "      <td>0.616434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>deepscale-squeezenet-1.0</th>\n",
       "      <td>8.072759</td>\n",
       "      <td>3.962985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bvlc-googlenet</th>\n",
       "      <td>11.917162</td>\n",
       "      <td>4.077942</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "lib                       nvidia-cudnn  nvidia-cuda\n",
       "model                                              \n",
       "deepscale-squeezenet-1.1      4.323794     2.474317\n",
       "bvlc-alexnet                  6.469998     0.616434\n",
       "deepscale-squeezenet-1.0      8.072759     3.962985\n",
       "bvlc-googlenet               11.917162     4.077942"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ideal_cudnn_cublas = get_ideal_adaptive_solution(df_per_layer_info, ['nvidia-cudnn', 'nvidia-cuda'], direction)\n",
    "df_ideal_cudnn_cublas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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lMcRuITYzMzNrNNIlvv2mGbUZQ+wWYjMzMzMzM2tITojNrOaam1+odxXMynJs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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa3e326b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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O+uB+LykJuyG75meL9mkDvkqqmbyO9PihL5AS4uJ7+CfS+/Q1Us/m1OLLK7nW80hJxJak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\n5eVYsUo4Xiwv14RaJRwvVk0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      "text/plain": [
       "<matplotlib.figure.Figure at 0xa3f8bb50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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2ZmZmZmbWrDyGuMlJy/Xam5mZmVkXRo4czowZnhzJbKByQtzAIg6vdxWaSlvb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\n4tay9aeRkuLVI+K1jvbrFuLG8QngvlI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      "text/plain": [
       "<matplotlib.figure.Figure at 0xa3c37bf0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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sBkxs577OBVZlj1srks4lneNtwCjgPOAI4A9lqo8lPScTszpDgRsllX2+I+JF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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa4475290>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_ideal_adaptive_solution(df_ideal_cudnn_cublas, df_model_lib_mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "model\n",
       "bvlc-alexnet                0.955901\n",
       "bvlc-googlenet              0.934854\n",
       "deepscale-squeezenet-1.0    0.925622\n",
       "deepscale-squeezenet-1.1    0.887320\n",
       "dtype: float64"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Hypothetical execution time reduction compared to the best non-adaptive solution (i.e. cuDNN).\n",
    "df_best_lib = df_model_lib_mean.reorder_levels(['lib', 'model'])[cuda_level_performance].unstack('lib')\n",
    "df_ideal_cudnn_cublas.sum(axis=1) / df_best_lib.min(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "model\n",
       "deepscale-squeezenet-1.1    1.079484\n",
       "bvlc-alexnet                1.001197\n",
       "deepscale-squeezenet-1.0    1.033382\n",
       "bvlc-googlenet              1.003465\n",
       "dtype: float64"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Up to 8% worse performance when using cuDNN+cuBLAS only.\n",
    "df_ideal_cudnn_cublas.sum(axis=1) / df_ideal_all.sum(axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"plot_ideal_cudnn_libdnn\"></a>\n",
    "### Using cuDNN and libDNN for adaptation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>lib</th>\n",
       "      <th>nvidia-cudnn</th>\n",
       "      <th>libdnn-cuda</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>deepscale-squeezenet-1.1</th>\n",
       "      <td>4.672342</td>\n",
       "      <td>1.735223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bvlc-alexnet</th>\n",
       "      <td>5.180496</td>\n",
       "      <td>1.925741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>deepscale-squeezenet-1.0</th>\n",
       "      <td>9.148563</td>\n",
       "      <td>2.605430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bvlc-googlenet</th>\n",
       "      <td>14.880228</td>\n",
       "      <td>1.208790</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "lib                       nvidia-cudnn  libdnn-cuda\n",
       "model                                              \n",
       "deepscale-squeezenet-1.1      4.672342     1.735223\n",
       "bvlc-alexnet                  5.180496     1.925741\n",
       "deepscale-squeezenet-1.0      9.148563     2.605430\n",
       "bvlc-googlenet               14.880228     1.208790"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ideal_cudnn_libdnn = get_ideal_adaptive_solution(df_per_layer_info, ['nvidia-cudnn', 'libdnn-cuda'], direction)\n",
    "df_ideal_cudnn_libdnn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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tlL5NF/fjjYi5kiYB+0hqjeyVzxrv2wI/zXnOPM4nDYUeCPwzx/M2y6dR/ohY\n/TlWrBqOF8vLsWLV6IPxMmTIIHryVkVDhuR/zVpaWrjttts46qij2GCDDWhpaWH//ffnvPPOY999\n92XTTTdlzTXX5IQTTuC2225rt++RRx7JscceC8CGG27IPvvsw3HHHVfxXNtuuy1bb711p3m+++23\nH2ecccaint1a684e4HGkiaMuAR4DdgOOBcZFxKlZuUIP8OnAbUA/YE1SXu2hpNzZXYtzajvpZX2Q\nNFt00EUPcLb+KmD/rPxOlXqAs7K7khrYd5MaqasArcCqwOYR8XIXdcvbA7wM8CKwFnBcRFRsXLsH\n2MzMzMysznL2ANvS6cke4LyZ0KVnjzLrFpJufzQCuJXU2Dyl0Pgt2fdE4CHSBFUXApuSemk3L278\ndnJ+SL3F5epRqfxY4IMK29rvHHEn8EVSg/c3pEbwM8D2hcZvF+cqV68O5SLiP6TXaj5wRVf1Msut\nJEfFrCLHilXD8WJ5OVasGo4Xq6Eue4Ct52QzUz8P3B8Ro7so6x5gy6+RcmmsvhwrVg3Hi+XlWLFq\nNFK8uAe4JhopB9hykLQK8ElSL/l6pIm/zLpPo/wRsfpzrFg1HC+Wl2PFquF4sRpyA7g+tgDuI90m\n6uiI6HKSMDMzMzMzM1s63XM3ZKtKRNwfES0RsW5EXFDv+lgf5Fway8uxYtVwvFhejhWrhuPFasgN\nYDMzMzMzM2sKbgCb9UXOpbG8HCtWDceL5eVYsWo4XqyG3AA2MzMzMzOzpuAGsFlf5Fway8uxYtVw\nvFhejhWrhuOlRw0bNoyJEydyxhlncPjh6baq06ZNo6WlhYULF9a5drXnBrCZmZmZmVk3Wme9wUjq\nscc66w2uuk4nnngiF1100aJlqfLtdMePH0///v1ZddVVWXXVVfn4xz/OUUcdxcsvv7yozP33309L\nSwtHHnlku3233357rrjiCgAuv/xyWlpaOPvss9uVWX/99XnggQeqfg7dwbdBMuuLnEtjeTlWrBqO\nF8vLsWLV6IPx8sqMmdB6eM8dv/WirgstpZEjR3LFFVewYMEC/vWvfzF27Fi23HJLHn/8cdZee20A\nVlppJa688kqOP/54Nthgg7LHGThwIGeddRZHHHEEK620Uo/XuyvuATYzMzMzM+vjxo8fz4EHHrho\nOSK4+OKLGTx4MIMHD+YnP/lJ2f369evHRhttxG9+8xvWXHPNduUGDBjA6NGjaW1trXjejTbaiM98\n5jMVj19CbVAmAAAZq0lEQVRrbgCb9UXOpbG8HCtWDceL5eVYsWo4XmqmdNhzW1sbkydP5s477+TM\nM89k4sSJFfdtaWlhzz335MEHH2x3vJNPPpmbbrqJ5557ruI5TznlFM477zzmzJnTPU9kKbgBbGZm\nZmZm1oRaW1tZfvnl2WSTTTj44IO59tprOy0/aNAgZs+e3W7dWmutxTe/+U3Gjh1bcb9NN92UESNG\ncOaZZ3ZLvZeGG8BmfVEfzKWxHuJYsWo4Xiwvx4pVw/FSF5JYb731Fi0PGTKEmTM7742fMWMGAwcO\n7LD+hBNO4M477+Spp56quO+ECRO44IILePXVV5e80t3ADWAzMzMzM7Mm9OKLLy76//Tp0xk0qPKP\nERHBbbfdxg477NBh28CBA/nOd77DmDFjKs4u/bGPfYy99tqL0047rdMZqHuaG8BmfZFzaSwvx4pV\nw/FieTlWrBqOl7qICE455RTmzZvHM888w6WXXsrIkSPbbQdYsGABzz77LCNHjuSVV17h2GOPLXu8\nY489loceeohnn3224jnHjh3LpZdeWtdcYN8GyczMzMzMrButPXhQj96qaO3B+YeNV+ptlcSOO+7I\nhhtuSERw/PHHs8suuyzafv3113PrrbcSEQwaNIgRI0YwadIk1llnnbLHW2WVVTj++OP5wQ9+ULEu\nQ4cO5cADD+TCCy/MXf/upkLL3no3Sb5QZmZmZmZ1tPbgQbz80owO6yXhdlX3qfR6ZuuXavy0e4Ab\niN9UZmZmZmZmS845wGZ9UFtbW72rYA3CsWLVcLxYXo4Vq4bjxWrJDWAzMzMzMzNrCs4BbhCSwtfK\nzMzMzKz3cQ5w93IOsAGVZ3AzMzMzq9aQIYOYOrXjZD5mZn2ZG8ANJOLwelfBGkRb20yGD88/Pb41\nL8eKVcPx0rdIPXeLlra2NoYPH95jx7e+xfFiteQGsJmZmZmZ2VIYMmSIR2t2oyFDhvTYsZ0D3CBS\nDrB7gM3MzKx7SBc5Z9HMGkp35AB7FmgzMzMzMzNrCm4Am/VBbW0z610FaxCOFauG48Xy8n1drRqO\nF6slN4DNzMzMzMysKTgHuEE4B9jMzMy6k3OAzazROAfYzMzMzMzMLCc3gM36IOfpWV6OFauG48Xy\nck6nVcPxYrXkBrCZmZmZmZk1BecANwjnAJuZmVl3cg6wmTUa5wCbmZmZmZmZ5eQGsFkf5Dw9y8ux\nYtVwvFhezum0ajherJbcADYzMzMzM7OmUNMcYEmjgEuLVs0FXgOeAK6NiBvK7LMw++8hEXFZybar\ngO0iYljRuqnABsCEiGgtKX8qcFJE1LThL6kNWBgROy/FMZwDbGZmZt3GOcBm1mgaNQc4gL2BbYDd\ngR8C84FrJd0tabkK+4yTtEyZ9aWf3IV135E0MEf5WvBfFzMzMzMzszqr1xDov0bEoxHxYERcHRH7\nA/sAOwNnlSl/F6lX9xs5j/8A0B84sVtqa9ZgnKdneTlWrBqOF8vLOZ1WDceL1VKuBrCkzSTdLOl1\nSXMl/UPSCUXbp0q6pMx+CyWNzXOOiLgZuBX4uqTlSzY/BtwCnFxmWzkvAhcA35K0bp7zl5J0bPY8\n35M0U9LPJa1SUmahpAmSjpL0gqS3JbVJ+kQnx107O+ZRZba1SnpX0qpLUmczMzMzMzOrrMsGsKSt\ngYeAYcAxwBeAnwDrFRXrriG+twPLAVuV2TYGWAs4OuexTgcWZPtVRdLppOd4J7AHcCYwGvh9meIH\nkF6To7MyGwC3SCr72kbEK6TGfLuE3qz8IcB1EfFWtXU2KzZ8+KB6V8EahGPFquF4sbyGDx9e7ypY\nA3G8WC2V5tSWczbwOvDpiHgvW9fWQ/WZDgjo0GsbEX+XdA1wvKQLIuKdzg4UEW9IOi8r/+OImJKn\nApJWA44DLo2IY7LVd0t6HbhS0h4RUdwQ/gDYIyIWZPsLuB7YGnikwmnOByZK2i4i/i9btwcwGLgw\nTz3NzMzMzMysOp32AEtaAdgWuKqo8duTCjN6VepRHgesAnw/5/HOBv4NjK+iDtsAywJXl6y/DvgP\nsGPJ+rsLjd/M06TnsUGlE0TE/cCztM9p/gbwVEQ8VkVdzcpynp7l5VixajheLC/ndFo1HC9WS131\nAK9GaiTPqEFdANYnNX5nldsYEVMkXQwcI+lnXR0sIt6WdBZwmqQzctahMHN0uzpExAJJbxRtL5hd\nslz4oaCrXOULgB9LOgb4ELAr8K3Odhg9+j6GDk1pyAMG9GfzzddYNByt8KXEy14GePLJ13tVfbzs\nZS972cu9dbktWx7ercsFPXV8L/et5YLeUh8v957lJ598kjlz5gAwdepUukOn9wHOeoDfAc6MiJM7\nKfcsMCkiDihaN5A0dLo1IiZk60YBlwAfjYgXyhznFmAEsHpEzM/WLQROjYix2fK6wPPAL4E1SfcB\n/nDRMaYAD0bEQUXPYTJpOPIzpPsA9+vkuewO/AHYJSLuK1rfD5gH/DQivl+ubtm6IcAUYHREXJGt\nuw+I4vsAZxNqzSD1ag8k5RAPjoh/V6iX7wNsZmZm3cb3ATazRtPj9wGOiHnAn4ADKtyft2AasEnJ\nuj2qqYikvYEvARcUGr8V6jQL+F/gCNpPxFWp/DzgVODLwKdyVOUR4H1gZMn6kUA/oC3HMbqU5TBf\nDXyTNPnVNZUav2ZmZmZmZrb08twG6XvA6sAjkg6QNFzSISVDkK8DPinpHEk7SzoW+C7lc3kF/D9J\nn5a0vaSvSboW+A1p1uWTctTpR6ShxqX5uJX8CpgKfL6rghHxJmkG6MMknStpRDZM+QJSz/Ifcp4z\nj/OBjwLrkHq0zbpFYYibWVccK1YNx4vlVTq01awzjherpS5ngY6Iv0jaDpgA/Ix0m6JpwKVFxS4n\n9cYeSrq9zwOkHtfn6dgIDtIsyQDzgVeBx4F9I+K35apQeoyImC3pHNLw4XLHLy3/gaTWrM5djvWJ\niJMlvUrqnT0CeAO4jI6N8w7nKlrf5bqIeFrSv4C3IuLJruplZmZmZmZmS67THGDrWZI+BvwdODQi\nLuuirHOAzczMrNs4B9jMGk135ADnuQ+wdTNJg0lDn8cDM4Fr61sjMzMzMzOzvi9PDrB1v8OAe4E1\ngP1qdI9layLO07O8HCtWDceL5eWcTquG48VqyQ3gOoiI8RHRLyI2jog/1bs+ZmZmZmZmzcA5wA3C\nOcBmZmbWnZwDbGaNpsfvA2xmZmZmZmbWV7gBbNYHOU/P8nKsWDUcL5aXczqtGo4XqyU3gM3MzMzM\nzKwpOAe4QTgH2MzMzLqTc4DNrNE4B9jMzMzMzMwsJzeAzfog5+lZXo4Vq4bjxfJyTqdVw/FiteQG\nsJmZmZmZmTUF5wA3COcAm5mZWXdyDrCZNRrnAJuZmZmZmZnl5AawWR/kPD3Ly7Fi1XC8WF7O6bRq\nOF6sltwANjMzMzMzs6bgHOAG4RxgMzMz607OATazRtMdOcDLdFdlrOdJF9W7CmZmZtZHDBkyqN5V\nMDOrOTeAG4h/pbW82traGD58eL2rYQ3AsWLVcLxYXo4Vq4bjxWrJOcBmZmZmZmbWFJwD3CBSDrCv\nlZmZmZmZNSffB9jMzMzMzMwsJ+cANxBpqX7sMDPrU1r6L8vC9z+odzWsB609eBAvvzSj3tWwJeCc\nTquG48VqyQ3gBuLbIFlebW0zGT7cs3ta1xo5VqSLoNWfizU1ZSYMq128vNLqux+YmVn3cg5wg/B9\ngM3M2nMDuAm0+j61Zma2mHOAzczMzMzMzHJyA9isD2prm1nvKliDcKxYVaY4Xiyftra2elfBGojj\nxWrJDWAzMzMzMzNrCs4BbhDOATYza885wE3AOcBmZlbEOcBmZmZmZmZmObkBbNYHOa/T8nKsWFWc\nA2w5OafTquF4sVpyA9jMzMzMzMyagnOAG4RzgM3M2nMOcBNwDrCZmRVxDrCZmZmZmZlZTm4Am/VB\nzuu0vBwrVhXnAFtOzum0ajherJbcADYzMzMzM7Om4BzgBuEcYDOz9pwD3AScA2xmZkWcA2xmZmZm\nZmaWU59oAEuaKumSHjz+ZpLGSRpQZttCSWN76txmS8J5nZaXY8Wq4hxgy8k5nVYNx4vVUp9oAAM9\nPT5qc2AcMLDMtm2AX/fw+c3MzMzMzGwpLVPvCjQIUaGRHRGP1rguZl0aPnxQvatgDcKxYlUZ5nix\nfIYPH17vKlgDcbxYLfWKHmBJH5F0haQXJM2VNFnS+RWGHB8jaYqkeZIelfTZMmXWkHShpH9KelfS\ndElXSxpUUq41G8K8iaSJWdmZksYXlRkFFIZXP5+VXyBpg2z7oiHQkr5SOF6ZOt0u6Ymi5X6STpT0\nrKT5kmZIOlvSckv6OpqZmZmZmVllvaIBDAwCpgNHA58HxgM7A38oLiTpUOBc4F5gT+Ay4FqgtKE8\nEJgHnADsCnwP2BD4k6T+ReUKvbo3A3dnx7waGFOU1/sH4NTs/3uThjx/BphV5nncBrwFHFBS77WA\nEcDlRauvBk4CrgK+AJwOHJotmy0V53VaXo4Vq4pzgC0n53RaNRwvVku9Ygh0RDwIPFhYlvQwMBl4\nQNJmEfFXSSLl4f4xIg7Lit4l6XXgupLj/Qs4tuh4LcBDpEb27sCtxcWBiyLix9nyPZJWBb4r6byI\neF3S5GzbXyPihU6ex3uSbgD2B35QtGn/7DzXZvXZHtgXODAirs7KTJT0JnClpE0j4qnKr5iZmZmZ\nmZlVq1c0gCUtC3wfOBAYAiyfbQrgY8BfgfWyR+mMyzcB/ylzzCOAbwAfAVYqOV6pG0qWryP1xm5C\najhX4wrgMEk7R8TEbN0BwL0R8Uq2vCvwHnCTpH5F+95NyjfeAejQAB49+j6GDl0FgAED+rP55mss\nyt8r9OJ42csFbW0ze019vNx7l4cPH9Sr6lPN8iKFXslCfqqXe2552KCan7/QM1TIEfSyl73sZS83\nz/KTTz7JnDlzAJg6dSrdQb3hBvOSfgJ8mzT0+WHgHVJj92ZgdERcIWlr4BHgixHxx5L9Z5F6hg/J\nlo8CfgqcDdwFvEka7v1noDUiJmTlxpEa1CtHxLyi420MPA18NSJuKMoD/mhpD7CkhcXHzNa9ANwf\nEQdL2gh4Btg/Iq7Ltl8EHEZ5AUyIiPHFKyVFxOFdvZRmZk1Dugha/bnYp7VeRG/4nmJmZr2DJCJC\nS3OM3pID/FXg8og4IyLaImISKZe2WCHndu3ilVkP6upljndPRBwfEfdkx3utk/OvXWF5Ru5n0N5V\nwF6Slif1ar8D3FK0/Q1SjvKWwFYlj08Bv1zC85oBZXrIzCpwrFhVnANsORV6cszycLxYLfWWBvCK\ndBzGfAjtbz30EvAiKXe22FfoOJR7ReCDLo5XrPSY+5EarU9ny+9l/65QYf9SVwIrkybN2h+4KSLm\nF22/gzTMe0BEPF7m8XLO85iZmZmZmVlOvSIHmNQgHCXpb8DzwF6kmZYXiYjIbk/0K0mXkPJ0P0qa\n6bm0t/gO4HhJJwKPkmaU/kqFcwv4etaT/BiwG6mxPC4i3snK/D0rd6Sky0mN679GRIfc46yuz0l6\nFPgRaYbrK0u23y/pOuBGSedmdVwIDCNN0nV8RDxfob5mXfK9XS0vx4pVxfcBtpwKOXxmeTherJZ6\nSwP4qOzfwu2G/gCMJDUMF4mISyStBByXbf9b9u9VtO/dnQCsCnyH1NPaRrq90gt07AUO0u2PfgH8\nkNSYPiUiTl1UIOKpLF/4cFLubgupsTo9279cz/KVwM+BlyLivtKNEfG1LFf5ENLtkN4DpgJ3Aq+U\nljczMzMzM7Ol0ysmwaqXokmwlo2IhfWuT2c8CZZVo3gGaLPONHKseBKsOpgys7a9wJ4Eq2G1tbW5\nV89yc7xYXn1pEiwzMzMzMzOzHuUGcOWJscwaVqP26FntOVasKs4Btpzcm2fVcLxYLTV1AzgixkfE\nMr19+LOZmZmZmZktvaZuAJv1Vb63q+XlWLGq+D7AlpPv62rVcLxYLbkBbGZmZmZmZk2hqWeBbiSe\nBdrMrD3PAt0EPAu0mZkV8SzQZmZmZmZmZjm5AWzWBzmv0/JyrFhVnANsOTmn06rheLFacgPYzMzM\nzMzMmoJzgBuEc4DNzNpzDnATcA6wmZkVcQ6wmZmZmZmZWU5uAJv1Qc7rtLwcK1YV5wBbTs7ptGo4\nXqyW3AA2MzMzMzOzpuAc4AbhHGAzs/acA9wEnANsZmZFnANsZmZmZmZmlpN7gBuEJF8oM7MiLf2X\nZeH7H9S7GtaD1h48iJdfmlHvatgSaGtrY/jw4fWuhjUIx4vl1R09wMt0V2Ws5/nHCsvLf0gsL8eK\nVcPxYmZmjc49wA0i5QD7WpmZmZmZWXNyDrCZmZmZmZlZTm4Am/VBvp+e5eVYsWo4Xiwvx4pVw/Fi\nteQGsJmZmZmZmTUF5wA3COcAm5mZmZlZM3MOsJmZmZmZmVlObgCb9UHOpbG8HCtWDceL5eVYsWo4\nXqyW3AA264OefPLJelfBGoRjxarheLG8HCtWDceL1ZIbwGZ90Jw5c+pdBWsQjhWrhuPF8nKsWDUc\nL1ZLbgCbmZmZmZlZU3AD2KwPmjp1ar2rYA3CsWLVcLxYXo4Vq4bjxWrJt0FqEJJ8oczMzMzMrKkt\n7W2Q3AA2MzMzMzOzpuAh0GZmZmZmZtYU3AA2MzMzMzOzpuAGsJmZmZmZmTUFN4B7MUnrSbpR0hxJ\nb0m6SdL69a6X9T6S9s5iZaqkuZL+Iel0SSvXu27W+0m6Q9JCSRPqXRfrnSR9QdL9kt7J/h49Kml4\nvetlvYuk7STdKekVSW9LmiTp4HrXy+pL0mBJP5f0kKR3s783G5Qpt5ykH0uamX2XeUjS9vWos9VP\nnniRtKWkX0p6NiszTdJVkobmOYcbwL2UpBWA+4D/Ag4EDgA+CkzMtpkV+x7wH+BEYFfgfOAI4K56\nVsp6P0n7AZsCnhHRypL0DeAW4DHgy8BXgBuAFetZL+tdJH0SuBtYBjgM+B/gUeDiLIaseW1I+tyY\nDTxA5b83lwCHAj8EvgjMAu6UtGktKmm9Rp54GQl8AvgpsBtwArAF8BdJg7s6gWeB7qUkHQOcDfxX\nREzJ1g0FngO+HxHn1a921ttIWj0i3ihZdyBwGbBLRLTVo17Wu0laDfg78B3gWuDUiBhb31pZbyJp\nCPAscEJE/Lze9bHeS9LpwHHAahExr2j9Q0BExHZ1q5z1GpIOBS4ChkXE9KL1mwFPAKMj4opsXT/g\nGeAfEfHletTX6quTeCn3vXcDYApwSkS0dnZc9wD3Xl8CHik0fgEiYirwf8Ce9aqU9U6lHwKZxwAB\nXf4SZk3rTOCpiPhNvStivdahwALgl/WuiPV6ywIfAPNL1r+Fv29a1/4beB+4vrAiIhYA1wG7Slq2\nXhWz3qfc996sgfwaOb73+gOp99oY+FuZ9c+QuvzNujKcNGzk2TrXw3ohSZ8lpVZ8u951sV5tO+Af\nwH6Snpf0gaTnJH2r3hWzXuey7N+fSVpX0qqSvg7sDJxTv2pZg/gEMCUiSn9AeQboTxoWa1aRpI2A\ntUgj2zq1TM9Xx5bQQODNMutnA6vVuC7WYLL8h/HA3RHxeL3rY71L9kv6hcCPI+L5etfHerVB2eMs\n0hwDLwD7AL+Q1M/Doq0gIp6RtBNwM4t/WHsf+GZE3FC/mlmD6Ox7b2G7WVnZcPkLgVdJueSdcgPY\nrI+RtBJwK+mLxyF1ro71TicAywOn17si1uu1ACsDB0XErdm6NknDSA1iN4ANAEkbAjcBTwOHk4ZC\n7wn8UtL8iLi2nvUzsz7tf4FtgC9ExFtdFXYDuPd6k/I9vZV+ITND0vLA74GhwA4RMbO+NbLeJruV\n2kmk3M7ls5hRtnk5SasC70TEwnrV0XqVN0hDD+8pWX8XKS9vnYh4ufbVsl7oDNIPr1/KcjcB7pO0\nBmmmVjeArTNvAh1ujcTint/ZZbaZIelHpJnnD4qIe/Ps4xzg3usZUh5wqU+QY2y7NR9Jy5B+fd8C\n2D0iHCdWzoeB5YCrSF843iR9sQjg+9n/N6lb7ay3eaaL7f6hxAo2Af5a1PgteBRYXdJadaiTNY5n\ngGHZj7LFNib9sOJ0HetA0smk7y5HRcQ1efdzA7j3+h2wTfENnbP/b0ca3mq2iCQB15AmvtozIh6r\nb42sF3sC2Cl7DC96CLgy+7+/aFjBzdm/u5as3x14KSJerXF9rPd6Gdg8+zG22Dak4dDuwbPO3Eaa\n7Gqfwoosr3Nf4M6I+KBeFbPeSdLRwCnASRFxQTX7egh07/Ur0iQSt0oak62bAEwj3Q/LrNj5pJuG\nnwrMk/Tpom0vRcSM+lTLepuIeJt0Y/l20m8oTIuIB2teKeu1IuJ2SW2kPM41SZNg7Qt8Dhhdx6pZ\n7/ML0i1sfi/pfGAeKQf4q8A5EfGfelbO6kvS3tl/tyL94PoFSa8Br0XEAxHxpKTfAOdJ6k+6n+u3\nSCld+9WjzlY/XcWLpJHAucAfSfNSFH/vfTsiOr0DiiKiJ+pt3UDSeqSLO4J08e8Bji2+EbQZgKQp\nlM+dARgfERNqWR9rPJIWAKdGxLh618V6F0krk/I7v0Kam+IfwBm+f7SVkrQraZK9jUkT7U0m3UP6\novAXzqYmaSEp1abU/RGxc1ZmOeA0YH9gAPBX4Hj/MNt8uooXSZcCB1XYfVFMVTy+P4/MzMzMzMys\nGTgH2MzMzMzMzJqCG8BmZmZmZmbWFNwANjMzMzMzs6bgBrCZmZmZmZk1BTeAzczMzMzMrCm4AWxm\nZmZmZmZNwQ1gMzMzMzMzawpuAJuZmZmZmVlT+P8Ma1jKZ0iKZAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa3ebe810>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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8GrB9RCwsXi/gcmBT4Lc1dnMmcKukLSLiN8W67YHhwNn19NPMzMzMzMyWTo8j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\nJWkWMKLG01MiYmp/9sfqI2khcEJETGp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      "text/plain": [
       "<matplotlib.figure.Figure at 0xa3f40c90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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gQuBbkjau5Ph5ko7PzvNtSfMl/UzSmrkybZImSzpG0ixJ/8qGO3+0g/1umO3z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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa41981b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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NqswvM6C3yH8gcGlE/LOnZbbBbdy4Ec0ugvUTxzZvjm++xo0b1+wiWD9yfPPm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\nzMzMrC24AmxmZmZmZmZt4f8DZiy4pA6aO/cAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa3c3b450>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_ideal_adaptive_solution(df_ideal_cudnn_libdnn, df_model_lib_mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "model\n",
       "bvlc-alexnet                0.958573\n",
       "bvlc-googlenet              0.940343\n",
       "deepscale-squeezenet-1.0    0.903953\n",
       "deepscale-squeezenet-1.1    0.836344\n",
       "dtype: float64"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Hypothetical execution time reduction compared to the best non-adaptive solution (i.e. cuDNN).\n",
    "df_best_lib = df_model_lib_mean.reorder_levels(['lib', 'model'])[cuda_level_performance].unstack('lib')\n",
    "df_ideal_cudnn_libdnn.sum(axis=1) / df_best_lib.min(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "model\n",
       "deepscale-squeezenet-1.1    1.017468\n",
       "bvlc-alexnet                1.003995\n",
       "deepscale-squeezenet-1.0    1.009191\n",
       "bvlc-googlenet              1.009356\n",
       "dtype: float64"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Less than 2% worse performance when using cuDNN+libDNN only.\n",
    "df_ideal_cudnn_libdnn.sum(axis=1) / df_ideal_all.sum(axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"plot_memory\"></a>\n",
    "## Plot memory consumption"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xa3a88e30>"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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SEiwiIiIiIiKthpJgcTkaEywiIiIiIvWlJFhERERERERaDSXB4nI0JlhEREREROpLSbCI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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa3a88b30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_memory = df_all['memory (MB)']\n",
    "# Batch size of 4; repetition 0 (should always be available).\n",
    "df_memory = df_memory.unstack(['model','lib']).loc[4].loc[0].unstack('lib')\n",
    "plot(mean=df_memory, std=pd.DataFrame(), ylabel='Memory size (MB)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"balance_memory_time\"></a>\n",
    "### Balance memory consumption and execution time per image"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above, however, does not tell the full story. The memory consumption, as reported by Caffe, increases linearly with the batch size. In other words, the memory consumption per image is constant. (Note that extra memory may be required e.g. for GPU buffers in host memory.)\n",
    "\n",
    "The execution time per image, however, decreases asymptotically. Since minimizing the execution time almost always should be balanced with minimizing the memory consumption, we should select the batch size that results in \"good enough\" performance.\n",
    "\n",
    "We give several examples below. Note that the execution time per batch is omitted to make the execution time per image more pronounced."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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wVanLpbTjIkbEDqXGad5iXMp/XMp/PMp9XMp/8OabsOuu4WrAHXfACy/AVlvV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\npAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa3cfae10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Is the batch size of 8 \"good enough\"?\n",
    "plot_time_per_image_and_memory_consumption(df_all, 'bvlc-alexnet', 'nvidia-cudnn')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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MHz+eoqKi2NUoSIp9XIp/XIp/PGaWqc9AraNkQAqKJwlBtklITUxoMpVXTAkrKGEZxfxC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\ne+9duX7//fczYMCA1Y4rTT4aNWrEb3/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j7wMamdk3wHmJ48pkZjXMbM1i+7qY2QVmtn2y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\n6h+IQCbwdzl7ggAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa3a7d2b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Is the batch size of 2 \"good enough\"?\n",
    "plot_time_per_image_and_memory_consumption(df_all, 'deepscale-squeezenet-1.1', 'cpu')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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dbWYzzOwLM7syxfONzWycmc0ys3fMbOek53c2s7Vm9tuaa3VmNMdBRERERCRL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\n0a8AAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa3a7d930>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Is the batch size of 10 \"good enough\"?\n",
    "plot_time_per_image_and_memory_consumption(df_all, 'bvlc-googlenet', 'tensorrt-fp32')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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6k24ZndROtu/3JlJmX4qf49NcP2fxe35z/NgmomSips/57AyvZVbKfrPY8rM/NsOxWc0K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\n07m8asgoufwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa3a7dd10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Is the batch size of 8 \"good enough\" (below 2 ms per image)?\n",
    "plot_time_per_image_and_memory_consumption(df_all, 'deepscale-squeezenet-1.1', 'tensorrt-fp16')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"alexnet_vs_squeezenet\"></a>\n",
    "## Compare AlexNet and SqueezeNet 1.1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"alexnet_vs_squeezenet_memory\"></a>\n",
    "### Memory consumption"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "lib\n",
       "cpu                  3.882476\n",
       "libdnn-cuda          3.882476\n",
       "nvidia-cuda          3.882476\n",
       "nvidia-cudnn         3.882476\n",
       "nvidia-fp16-cuda     4.113806\n",
       "nvidia-fp16-cudnn    4.113806\n",
       "tensorrt-fp16             NaN\n",
       "tensorrt-fp32             NaN\n",
       "dtype: float64"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# SqueezeNet consumes about 4 times more memory than AlexNet.\n",
    "df_memory.ix[['bvlc-alexnet', 'deepscale-squeezenet-1.1']].iloc[1] / \\\n",
    "df_memory.ix[['bvlc-alexnet', 'deepscale-squeezenet-1.1']].iloc[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"alexnet_vs_squeezenet_time\"></a>\n",
    "### Execution time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xa3ac7b30>"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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bt46kpKQa5xYUFPD++++zePFi/Pz8GD58OImJibVea9y4ccTExAAwfPhwRo8e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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa3e32990>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Using TensorRT, SqueezeNet is over 1.5 times faster than AlexNet.\n",
    "mean = df_model_lib_mean[['bvlc-alexnet', 'deepscale-squeezenet-1.1']].unstack('lib')\n",
    "std  = df_model_lib_std[['bvlc-alexnet', 'deepscale-squeezenet-1.1']].unstack('lib')\n",
    "plot(mean, std)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "lib\n",
       "cpu                  0.540034\n",
       "libdnn-cuda          0.726139\n",
       "nvidia-cuda          0.977500\n",
       "nvidia-cudnn         1.033459\n",
       "nvidia-fp16-cuda     0.961526\n",
       "nvidia-fp16-cudnn    0.989633\n",
       "tensorrt-fp16        0.632098\n",
       "tensorrt-fp32        0.627858\n",
       "dtype: float64"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Using TensorRT, SqueezeNet is over 1.5 times faster than AlexNet.\n",
    "df_model_lib_mean[['bvlc-alexnet', 'deepscale-squeezenet-1.1']].unstack('lib').iloc[1] / \\\n",
    "df_model_lib_mean[['bvlc-alexnet', 'deepscale-squeezenet-1.1']].unstack('lib').iloc[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"conclusion\"></a>\n",
    "## Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"improvements_of_each_approach\"></a>\n",
    "### What are the improvements brought on by each approach?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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THRHxT/VD5NCRnp5OWFgYJ5xwAmlpabz66qsB88fHx7Np06YSaY0bN+aGG25g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\nAkoViW0uQ58CAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa3c50b50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# TensorRT-fp16 is up to 69x faster than the CPU.\n",
    "plot_speedup_over_baseline(df_mean_time_per_image, baseline='cpu', libs_to_drop=[], fontsize=12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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zysy6W9I9t7CwkO3bt9O/f3/27NlzQktpifvuu4/169eXWW7meBMmTGDOnDkV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\ndO3alQ4dOrBp0yYAQkJC+Oabbzhw4ACNGjWiW7d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dwFvAHCCxqPlNYKIn0YmIiIiIiEid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\niIiISLX1/wHIjpSzil+MCwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa421c0f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# cuDNN-fp16 is about 2x faster than cuBLAS-fp32. TensorRT-fp16 is up to 5.2x faster than cuBLAS-fp32.\n",
    "plot_speedup_over_baseline(df_mean_time_per_image, baseline='nvidia-cuda', libs_to_drop=[], fontsize=12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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GRo0aOfcjIyM5cOCA22tSU1MJDg4ucnzixImsWbOGb775xuW1TzzxBPPmzeOX\nX34pdDx/OvSC06AXx5NYAwICuPvuu7nttts4dOhQoXMvvvgiixcv5oMPPqBq1aou7yMiIiIiInK2\n0mjhDQG6lEI+4qGUlBTn6+TkZEJDQ12mtdayatUqOnfuXORccHAw999/P5MnT3Y5+/Mll1zCLbfc\nwpNPPlkozbFjx8jMzCQzM7NQpdZdrElJSS5jzcnJ4cSJE6SmpjqPvfrqq8yaNYv169fTsKGWehap\nyDT+SsQ1lQ8R91RGxJtcjuE1xkR4mEdIKcUiHrDWMn36dObPn89PP/3Ea6+9xptvvlnoPDgqkHv3\n7iUuLo709HQeeOCBYvN74IEHaNKkidt7Tpkyhejo6POK96WXXqJXr14EBATw1FNPOWdiXrduHXXr\n1iU6OprffvuNSZMmERwcTIsWLQB44403ePzxx9mwYQORkZHndW8REREREanc3E1a9TPgyfBo42G6\nC0Jkg/qYfd4bvB3ZoL5H6Vy1uBpj6NKlC02bNsVay8MPP0y3bt2c55ctW8Z7772HtZbQ0FC6d+/O\n9u3badCg+BnYatasycMPP8wjjzziMpbGjRszfPjwEhepLi7moUOH0qNHD9LS0ujbty+PP/44ABkZ\nGYwdO5bU1FQCAgJo3749H330EX5+fgBMnjyZw4cP065dO6y1GGMYNmwYc+deNEs+i1xUNP5KxDWV\nDxH3VEbEm8zZkww5TxhzHNgEvFNCHlcBd1lrfUs5tlJjjLHFPacxpsgkS1J6oqKiWLBggcsZpC9k\n+uyIiIiISFlw/N3prbyLTjp7ocr7+7xIC5y7Ft6dQI61dkEJGWcAd/3B+ERE5AK2YcMGfUMv4oLK\nh4h7KiPiTe4mrdoOXOlhPsX3v5VKzVW3bBERERERkbLgrktzGNDUWruxbEMqferSLKVNnx0RERER\nKQvq0uwZV12aXVZ4Lyaq8Epp02dHRERERMqCKryecVXhPa91eI0xWopIRESctIaiiGsqHyLuqYyI\nN3lc4TXGdDHGbDTGnAQOGmNOGmM2GGM6ezE+ERERERERkfPiUZdmY8xfgSXAXuBtIB1oAAwAmgGD\nrbUlLV9UbtSlWUqbPjsiIiIiUhbUpdkzf2gMrzFmD/Aj0Ndam1vguA+QAPzZWtuiFOMtVarwSmnT\nZ0dEREREyoIqvJ75o2N4o4B5BSu7AHn7c4HGfzhCuej88ssvdO7cmaCgIB566KHyDkdEvEjjr0Rc\nU/kQcU8jQVYsAAAgAElEQVRlRLzJ0wrvj4CriapCgH2lE075axzWAGOM17bGYQ28Gn9MTAwBAQEE\nBgZSu3ZtYmJi2L17t/P8tGnTGD58eIl5BAcHc+bMmULHU1NTGTBgACEhIdSuXZvo6Gji4+Nd5jN/\n/nzq1avH0aNH+fvf/15i7MOHDyc0NJSgoCAuvfRSFixY4Dz3r3/9ix49elCnTh3q16/PoEGDOHjw\nYIl5ioiIiIhI5VXFw3SPA88bY/ZYa7fmHzTGXA3EAWM9vaExpj8wBLgKqAckA8uBp6y1v5VwbW4x\nhy3Qxlr7jacxuJN0IB17Y2nkVDzzYbr3MsfRlD937lxiY2Ox1hIXF8fw4cP5+uuvC6VxJSkpiS1b\ntlCrVi0SEhLo37+/89zw4cNp06YNKSkp+Pn5sWvXLreVzqSkJC677DKPY3/sscd49dVXqVq1Knv3\n7qVLly60bduWNm3acOTIEe6++2569uxJlSpVGDNmDLGxsXz44Yce5y8i3hMTE1PeIYhUWCofIu6p\njIg3eVrhfQjwB740xqTgmLSqPhCe9/phY8zDeWmttbaLm7wmAEnAo8B+oA0wDYgBOnoQy6vA/LOO\n7fXsMS4s+/fvZ/z48WzevBlrLUOGDKFOnTrs27ePRYsWAY5KZVRUFNnZ2fj4OBrs8/vhG2MYPHgw\nM2fO9Pie8fHxdOjQgauvvprXX3+9UIV369atzJ49G39/fwBat25N69ati80nNjaWN954A2MMs2fP\nZuXKlWzevJndu3fj6+vLBx98QPPmzXn11VeJjo4GoEWL34eBW2sxxpCYmEibNm244YYbCuV/3333\n6T9HERERERFxy9MuzTnA98Am4D/Aibyfm4Af8s7nb8W1whbU21o72Fr7lrV2s7X2BWAccLUxJsaD\nWA5Ya786azvl4XNcMHJzc+nduzdRUVEkJSWRmprK4MGDgaIttK5abE+fPs3ixYu55pprPL5vfHw8\nw4YNY+jQoaxZs4Zff/3Vea5Dhw7ce++9LF26lJSUFLf5vPbaa9x6661MnDiRzMxMunbtCkBCQgKD\nBg3iyJEjDBkyhL59+5KTk+O8bsyYMdSoUYMWLVoQGhrKTTfdVGz+Gzdu5PLLL/f4uUTEuzT+SsQ1\nlQ8R91RGxJtcVniNMYEmryZlrY2x1l7n6ebuhtba/xZzeCtggLA/9DQXka+++oq0tDRmzZpFQEAA\nfn5+dOzoSQM4jBs3juDgYAIDA5k7dy5Tp0716LotW7aQnJzMwIEDadu2LU2bNuXNN990nn/77bfp\n3LkzM2bMoEmTJrRt25Zt27ad03NdeeWV9OvXD19fXx588EFOnTrFl19+6Tz/0ksv8dtvv7FlyxZu\nueUWqlWrViSPb775hunTp/PMM8+c071FRERERKRycdfCewRoB2CMWW+MudSLccTgGIu7x4O09xhj\nThljjhtjPjHGdPJiXOUmJSWFyMhIZzflc/HCCy9w+PBhTp06xapVq+jfv3+hiatciY+Pp0ePHtSu\nXRuAIUOGsHDhQuf5oKAgnnrqKXbt2kV6ejqtW7emX79+ANxzzz3UrFmTwMBAnn76aZf3CA8Pd742\nxtCoUSMOHDhQKI0xho4dO5KSksK8efMKndu3bx833XQTc+bM8fgLABHxPg0xEHFN5aNkp0+f5o47\n7qBx48YEBQXRtm1bPvrooxKv69atGz4+PuTmFu1guHHjRnx8fJgyZYo3QpZSpDIi3uRuDO9poGre\n6xgg0BsBGGPCcIzhXWut/XcJyRcB7wMHgEgcY4vXG2Out9Zu8kZ85SU8PJzk5GRyc3MLVXpr1KjB\niRMnnPtpaWlu8+nUqRNNmzbl448/pmXLli7TnTp1imXLlpGbm0vDhg0Bxy+fjIwMdu3aRatWrQql\nDw4OZsKECcTHx3PkyBHmzZtXpHJanIJdoa217N+/n9DQ0GLTZmdnk5iY6NxPSkqie/fuTJ06laFD\nh5Z4LxEREbkwZGdnExERwebNmwkPD2f16tUMHDiQ3bt3ExERUew1b775JtnZ2cUO7crOzub+++8/\np2FdInJxctd8+CPwmDFmRN7+TcaY21xt53NzY0wN4D0cleuRJaW31t5urX3bWvuZtfZN4Focld8Z\n53P/iqx9+/Y0bNiQRx55hBMnTpCVlcXnn3/OFVdcwaZNm0hJSeHo0aNuW1MBvvjiC/bs2VOospuT\nk0NWVpZzO336NCtWrKBKlSrs2bOHnTt3snPnTvbs2UOnTp2cSw898sgjfPvtt+Tk5HDs2DHmzp1L\n06ZNnS3Cnti+fTsrV64kJyeH5557Dn9/f6655hp+/fVXli5dyvHjx8nNzWXNmjUsWbKE66+/HnAs\nidStWzfGjh3LnXfeeR7vqIh4k8Zfibim8lGy6tWrM2XKFGdPsF69ehEVFcX27duLTZ+ZmckTTzzh\nctnDZ599lp49e3Lppd7soCilRWVEvMldC+/jwGLgRhzdjd31B7GA6wVZi2GM8cfRWtsY6GytPeD+\nimJuau1vxpjVeFBZHjFiBI0bNwagVq1aXHHFFcWmiwyt79WlgyJD63uUzsfHh1WrVjF27FgiIiLw\n8fFh6NChzJ49m4EDBxIdHU1ISAgTJ05k1apVha697777uP/++wFo0KABTz75JD169HCeX7JkCUuW\nLHHuh4WF0bJlS0aOHElYWFiRvMaPH8/MmTM5ceIE/fr14+DBgwQEBHD11VeTkJDg8hmK+8a1T58+\nLF26lNtuu41mzZqxYsUKfH19McYwb9487rnnHnJzc4mMjOT555+nV69eACxYsID//Oc/xMXFERcX\n55zFOTMz06P305vy/5PO746jfe1Xxv18FSUe7Wu/Iu3nqyjxXAj76enp/PDDD/z22+8rVhY8/9hj\nj9G9e3d++umnIuejoqJ47bXXmD17Ns8//zyNGjUq9+fRvvb/+D55+5Tqfr7yf75z39+xYwcZGRkA\n/Pzzz7hi8pewKfakMb5AIxwzMg8AdrpKa61NdHWumHyr4GjZ7QRcX3Bt33NljHkJiLXWVneTxhb3\nnMYY3D2/lK5p06aRmJjobDG+kOmzIyIi4h3Z2dnceOONNGvWjLlz5xY5v23bNu666y62b99OcnIy\nTZo04cyZM84hYH379mXYsGEMGDCA2NhYwsPDeeKJJ8r6MURKjePvTm/lzUXzN23e3+dFWtzcrsNr\nrc0Bkowx04Avz6cVtphADPAmEAP0+oOV3UCgN/CvPxqXiIiIiJQvay3Dhg2jWrVqzJkzp9jzY8aM\n4fnnny/2y+dVq1Zx7NgxBgwYUFYhi0gF57bCm89aO63gft6Y3VXW2iPncc+5OFqLZwAnjTFXFzi3\n31qbaoyJAH4C4qy1M/Lu+b/AJcCnOMbtNgb+F6gPDDmPOEREpJRs2LDB2c1IRApT+fDcqFGjOHTo\nEB988AG+vr5FzmdmZrJ9+3YGDRqEtZacnBystTRq1Ii3336b9evXs337ducEnEePHqVKlSrs2rWL\nFStWlPXjiIdURsSbPKrwFpTXzfk1HEsWnU+F9wYcY34fz9sKmgY8gWNN3vwt3w9A37wtCMgEtuDo\nzlz8jAZSoXi6HrCIiIhUPqNHj+b7779n3bp1+Pn5FZsmKCio0HKGycnJtG/fnn//+9/UrVuXK664\ngkcffdR5fty4cYSFhTF58mSvxy8iFZPbMbzFXuCo8J4BrvJgGaEKQWN4pbTpsyMiIlJ6kpOTady4\nMf7+/s6WXWMML7/8Mp06deLyyy/nu+++c05AlS8pKanIGN6CNIZXLgYaw+sZV2N4VeG9SP6BpWzp\nsyMiIiIiZUEVXs+4qvAW/SqsZBZIArL+cFQiInJR2HD2+gYi4qTyIeKeyoh40zlXeK21udbaKGvt\nt94ISEREREQqj8aNG2CM8drWuHGD8n5EESlH59yl+UKkLs1S2vTZERERKR3e7K7pyP/i6bIplZO6\nNHvmnNfhNcasP4f8rbW223lFJhetX375hQEDBrBz507uuusu/v73v5d3SCIiIiIiUom469LsB1Qt\nYasFxORtF4XGjbzcraaRd7vVxMTEEBAQQGBgILVr1yYmJobdu3c7z0+bNo3hw4eXmEdwcDBnzpwp\ndDw1NZUBAwYQEhJC7dq1iY6OJj4+3mU+8+fPp169ehw9etSjyu5LL71Eu3bt8Pf3Z+TIkUXOnzx5\nknvvvdd5f63XJlJxaPyViGsqHyLuqYyIN7ls4bXWdnJ1zhjjB4wGHgVygEWlH1r5SEpNx471Xv5m\nTrr3MsfRlD937lxiY2Ox1hIXF8fw4cP5+uuvC6VxJSkpiS1btlCrVi0SEhLo37+/89zw4cNp06YN\nKSkp+Pn5sWvXLg4ePOg2r8suu8zj2PPXyVuzZg0nT54scv7OO+8kNzeXH374gdq1a7Njxw6P8xYR\nERERkcrHZYW3OMYYH2AEMAVoBCwHJltrfyj90GT//v2MHz+ezZs3Y61lyJAh1KlTh3379rFokeM7\nhqSkJKKiosjOznauP5ffD98Yw+DBg5k5c6bH94yPj6dDhw5cffXVvP7664UqvFu3bmX27Nn4+/sD\n0Lp1a1q3bl1sPrGxsbzxxhsYY5g9ezYrV65k8+bN7N69G19fXz744AOaN2/Oq6++SnR0NAB9+/Z1\n3ic1NbVQfj/88APvv/8++/fv509/+hMAbdq08fi5RMS71ONCxDWVDxH3VEbEmzyepdkYMxD4Fvgn\n8B3Qzlo7UJVd78jNzaV3795ERUWRlJREamoqgwcPBoq20LpqsT19+jSLFy/mmmuu8fi+8fHxDBs2\njKFDh7JmzRp+/fVX57kOHTpw7733snTpUlJSUtzm89prr3HrrbcyceJEMjMz6dq1KwAJCQkMGjSI\nI0eOMGTIEPr27UtOTk6JcX311VdERkYyZcoUQkJCaN26NcuXL/f4uUREREREpPIpscJrjOlljPka\nWAL8CnS21t5krf26hEvlD/jqq69IS0tj1qxZBAQE4OfnR8eOHT26dty4cQQHBxMYGMjcuXOZOnWq\nR9dt2bKF5ORkBg4cSNu2bWnatClvvvmm8/zbb79N586dmTFjBk2aNKFt27Zs27btnJ7ryiuvpF+/\nfvj6+vLggw9y6tQpvvzyyxKv279/P7t27aJ27dqkpaUxZ84cbr/9dn74Qd+3iFQEGn8l4prKh4h7\nKiPiTS4rvMaYGGPMZ0ACjnG6N1lrO1trt5RZdJVYSkoKkZGRzm7K5+KFF17g8OHDnDp1ilWrVtG/\nf/9CE1e5Eh8fT48ePahduzYAQ4YMYeHChc7zQUFBPPXUU+zatYv09HRat25Nv379ALjnnnuoWbMm\ngYGBPP300y7vER4e7nxtjKFRo0YcOHCgxNjyK/2TJk2iSpUqdO7cmeuuu46PP/64xGtFRERERKRy\ncjeGdz1ggY+Bd4BQY0zRqXPzWGtfLeXYKrXw8HCSk5PJzc0tVOmtUaMGJ06ccO6npaW5zadTp040\nbdqUjz/+mJYtW7pMd+rUKZYtW0Zubi4NGzYEHF2iMzIy2LVrF61atSqUPjg4mAkTJhAfH8+RI0eY\nN28e8+bNK/G5CnaFttayf/9+QkNDS7wuf5yvtdbZhdvd5FsiUrY0/krENZUPEfdURsSbSmo+NEBP\n4BUcY3ddba94McZKqX379jRs2JBHHnmEEydOkJWVxeeff84VV1zBpk2bSElJ4ejRo25bUwG++OIL\n9uzZU6iym5OTQ1ZWlnM7ffo0K1asoEqVKuzZs4edO3eyc+dO9uzZQ6dOnZxLDz3yyCN8++235OTk\ncOzYMebOnUvTpk2dLcKe2L59OytXriQnJ4fnnnsOf39/5xjjnJwcTp06RU5ODtnZ2WRlZTnH93bu\n3JmIiAj+9re/kZOTw2effcaGDRvo2bPnub61IiIiIiJSSbhr4Y0qsygqkMiw+l5dOigyrL5H6Xx8\nfFi1ahVjx44lIiICHx8fhg4dyuzZsxk4cCDR0dGEhIQwceJEVq1aVeja++67j/vvvx+ABg0a8OST\nT9KjRw/n+SVLlrBkyRLnflhYGC1btmTkyJGEhYUVyWv8+PHMnDmTEydO0K9fPw4ePEhAQABXX301\nCQkJLp+huBbYPn36sHTpUm677TaaNWvGihUr8PX1BWDGjBlMmzbNed0bb7zB1KlTmTJlClWqVOG9\n995j1KhRPP3000RGRrJo0SKaN2/u0fspIt61YcMGfUMv4oLKh4h7KiPiTSZ/CZuLmTHGFvecxhgq\nw/NXFNOmTSMxMdHZYnwh02dHpDD9sSLimsqHe47fqd7MH/3OruBURtzzZhm5mMpH3t/nRVrczn1G\nJBERkbPoDxUR11Q+RNxTGRFvctml2RhzLpNQWWvtqFKIR0RERERERKRUuOzSbIzZj2OWZrfXA6E4\nKry+pRxbqVGXZilt+uyIFKbuaCKuqXy4py7NojLinro0e8ZVl2aXLbzW2kYlZPg/wHQcFd6NfzhC\nERERERERkVJ0zpNWGWO6AjOAq4FtwOPW2nVeiK3UqIVXSps+OyIiIqVDLbwi7qmF1zN/eNIqY0x7\nY8xaYB1QE+hvrb26old2RUREREREpHIqscJrjGlljHkP+ALH2ry3AdHW2pXeDk5ERC4MGzZsKO8Q\nRCoslQ8R91RGxJtcVniNMU2NMW8CXwNXAmOAS621i4vtHywiIiIiIiJSgbibpfkMjgrxx8CLwEl3\nGVlr15d6dKVEY3jLzqRJk3j55ZepWrUqBw4cKO9wvEafHRERkdKhMbwi7mkMr2fOZwyvL45lh3oC\nCUD++N2C29oCPy8KjcMbYIzx2tY4vIFX44+JiSEgIICgoCBq1apFu3btmDlzJqdPn3amiYuLw8fH\nh3feecd5LCcnBx8fH5KTkwEYMWIEPj4+bNu2zZkmMTERHx/XH5mUlBT+8Y9/8P3333tU2V26dCmX\nXnoptWrVokGDBsTGxvLbb78BcPr0ae644w4aN25MUFAQbdu25aOPPjrn90NERERERCovl8sSAdeV\nWRQVSNL+dOwL3svfjEv3XuY4vtmYO3cusbGxnDx5kq1btzJ+/HjWrl3LunXrnGnq1KnD1KlT6d+/\nP8YY5/GC+dSpU4dJkyYVqmgWTHO2pKQk6tatS506dTyKtVOnTnz++ecEBwdz4sQJ7rrrLiZNmsTs\n2bPJzs4mIiKCzZs3Ex4ezurVqxk4cCC7d+8mIiLifN4aEfEiraEo4prKh4h7KiPiTS6b66y1G89l\nK8ugK4v9+/fTv39/6tWrR0hICOPGjWPatGkMHz7cmSYpKQkfHx9yc3Odx/K7JQQEBNC5c2cSEhL4\n4osv+OCDD5xpevbsiZ+fH4sWLSpyXb7bb7+db775hs2bN5cY6yeffEKPHj04cOAAgYGBjBw50hnb\nK6+8QlhYGGFhYTz77LPOa8LCwggODgYgNzcXX19f9u3bB0D16tWZMmUK4eHhAPTq1YuoqCi2b9/u\n8fsnIiIiIiKV27ksS+RjjAkxxtQ1xnh8nZyf3NxcevfuTVRUFElJSaSmpjJ48GCgaCuru1ZXgPDw\ncK666qpCFVcfHx+mT5/OtGnTyMnJKfa66tWr89hjj/HYY4+VGG+3bt348MMPCQ0NJTMzk1dffdV5\nbsOGDSQmJrJmzRpmzpzJ+vW/D/f+7LPPqFWrFoGBgSxfvpwHHnig2PzT09P58ccfufzyy0uMRUTK\nnr6ZF3FN5UPEPZUR8SZPliXqZ4zZCBwHDgLpwHFjzHpjzM3eDrCy+uqrr0hLS2PWrFkEBATg5+dH\nx44dzzu/0NBQDh8+XOhY7969CQkJ4Z///KfL6+666y6Sk5NZs2bNed87Li4Of39/WrZsSWxsLG+9\n9Zbz3F/+8hcyMjJITU3loYceKra7cnZ2NsOGDWPEiBE0b978vOMQEREREZHKxW2F1xgzA3gXaJ73\ncyYwK+/1ZcBKY0ycl2OslFJSUoiMjHQ7SdS5SE1NdXYfLmjGjBk8+eSTnDp1qtjr/Pz8mDx5MpMn\nTy50fMuWLdSsWZPAwEBatWrl8r7GGBo1auTcj4yMLHZCq4YNG9KzZ09nK3Y+ay3Dhg2jWrVqzJkz\nx+0zikj50RqKIq6pfIi4pzIi3uRuHd5rgMeA54EIa+0wa+1j1tpHrbXDgHBgDjDJGNOubMKtPMLD\nw0lOTi40NhegRo0anDhxwrmflpZWYl4pKSls376dzp07Fzl3/fXX07RpU+bOneuya3RsbCwZGRks\nX77ceaxTp04cO3aMzMxMdu3a5fLe1lpSUlKc+8nJyYSGhhab9syZM/z000+Fjo0aNYpDhw6xfPly\nfH193T6niIiIiIhIQe6aD+8APrfWPmCtPXP2SWvtGWvt/cCXwF3eCrCyat++PQ0bNuSRRx7hxIkT\nZGVl8fnnn3PFFVewadMmUlJSOHr0KE8//bTLPE6ePMnGjRvp27cv11xzDTfeeGOx6WbMmMGsWbNc\n5uPr60tcXBwzZ848r2eZPn06J0+e5Ntvv+W1115ztuK++eabzspwUlISkyZN4vrrr3deN3r0aL7/\n/nsSEhLw8/M7r3uLSNnQ+CsR11Q+RNxTGRFvcrcsUQccLbglWQyMLZ1wyl9ko/peXTooslF9j9L5\n+PiwatUqxo4dS0REBD4+PgwdOpTZs2czcOBAoqOjCQkJYeLEiaxatarQtffdd59z8qemTZvy17/+\nlQcffNDlvTp27Ej79u3djtMdMmQIf/vb38jIyPAo/oK6dOlC06ZNsdby8MMP061bNwC+++47Jk6c\nSEZGBrVr16ZXr1489dRTgKMleP78+fj7+1O/vuM9M8bw8ssvM2TIkHOOQUREREREKh9z9lI0zhPG\nZAD9rLWfus3AmOuAldbaIC/EVyqMMba45zTGFFmKR0pPUlISTZo04cyZM6U2Frmi0GdHpDCtoSji\nmsqHe47fqd7Mv+jSi1KxqIy4580ycjGVj7y/z4uM0XRXC6mJY2bmkpwAapxvYHJxu1gKkIiIiIiI\nXHjctfDmAldba7e6zcCYq3GM9a2wMwqphbd8qIVXRERESqIWXhH31MLrGVctvCVVeH8BTpeQtx8Q\nogqvVCb67IiIiJQOVXhF3FOF1zPn06V5IfAh8EkJ24dAfGkHLCIiFw6toeje6dOnueOOO2jcuDFB\nQUG0bduWjz76qNi03377LTfccAMhISHFLsc2fPhwQkNDCQoK4tJLL2XBggXeDl/+IJUPEfdURsSb\nXM7SbK2NLctARERELlbZ2dlERESwefNmwsPDWb16NQMHDmT37t1EREQUSlu1alUGDRrEmDFj6Nu3\nb5G8HnvsMV599VWqVq3K3r176dKlC23btqVNmzZl9TgiIiIXDJddmi8m6tIspU2fHRH5o1q3bk1c\nXBz9+vUr9nxiYiLNmzcnJyfHZR4//PAD1113HS+88AIDBgzwVqgiXqUuzSLuqUuzZ1x1aXa3Dm/B\nizuXlMZau+l8AhMREals0tPT+fHHH7n88svP6/oxY8bw+uuvc/LkSdq2bctNN91UyhGKiIhcHDyd\nOncD8GkJm4iIVFIaf+W57Oxshg0bxogRI2jevPl55fHSSy/x22+/sWXLFm655RaqVatWylFKaVL5\nEHFPZUS8ydMK73VA17O2v+KY2OpnoLc3gpMLz6RJkwgJCSE0NLS8QxERqXCstQwbNoxq1aoxZ86c\nP5SXMYaOHTuSkpLCvHnzSilCERGRi4tHFV5r7cZituXW2pFAAnCzd8MsO40jGmCM8drWOKKBV+OP\niYkhICCAoKAgatWqRbt27Zg5cyanT/++ulRcXBw+Pj688847zmM5OTn4+PiQnJwMwIgRI/Dx8WHb\ntm3ONImJiW7X001JSeEf//gH33//PQcOHCgx1pJmIgVYsmQJl112GX/6059o1qwZn332WYn5ikjZ\ni4mJKe8QLgijRo3i0KFDLF++3OX/e+cqOzubxMTEUslLvEPlQ8Q9lRHxJo/G8JZgNbAEuLcU8ip3\nSSnp2A+8l7+5Kd17meP4xn/u3LnExsZy8uRJtm7dyvjx41m7di3r1q1zpqlTpw5Tp06lf//+GGOc\nxwvmU6dOHSZNmlRo6YyCac6WlJRE3bp1qVOnjkexljQT6dq1a3n00UdZtmwZ7dq1Iy0tzaN8RUQq\notGjR/P999+zbt06/Pz83KbNysoiKysLay1ZWVkYY/Dz8+PXX39l/fr19O7dm4CAANauXcuSJUtY\nsmRJGT2FiIjIhcXTLs3uXALklkI+cpb9+/fTv39/6tWrR0hICOPGjWPatGkMHz7cmSYpKQkfHx9y\nc3//J8ifaS0gIIDOnTuTkJDAF198wQcf/F6T79mzJ35+fixatKjIdfluv/12vvnmGzZv3lxirJ98\n8gk9evTgwIEDBAYGMnLkSGdsr7zyCmFhYYSFhfHss886r2nevDmxsbFcdtllxeYZFxfHlClTaNeu\nHQANGzakYcOGJcYiImVP46/cS05OZv78+ezYsYP69etTs2ZNAgMDeeutt0hJSSEwMJD9+/cDjv/X\nAwICaNWqFcYYAgICuPTSSwHHl47z5s0jPDyc4OBgHn74YZ5//nl69epVno8nJVD5EHFPZUS8ydNZ\nmm8r5rAf0BIYBSwvzaAEcnNz6d27N9dffz2LFy/G19eXbdu2sXbt2iKtrO5aXQHCw8O56qqr2Lx5\ns3MmTx8fH6ZPn8748eO59dZbi72uevXqPPbYYzz22GMlVnq7devGhx9+yPDhw53dopOSkgDHf2KJ\niYns27ePrl270qZNG7p27Vri82/bto3/+Z//oVmzZmRlZdGnTx+eeeYZTc4iIheciIiIQl9Mni0z\nM9P5OjIy0mXaunXr6g9DERGRc+BpC+/rxWzzgbuBd4FxpR1YZffVV1+RlpbGrFmzCAgIwM/Pj44d\nO553fqGhoRw+fLjQsd69exMSEsI///lPl9fdddddJCcns2bNmvO+d1xcHP7+/rRs2ZLY2Fjeeuut\nEiX1EZcAACAASURBVK9JT0/nzJkzvPvuu3z22Wfs2LGDr7/+mhkzZpx3HCLiPRp/JeKayoeIeyoj\n4k2eVnijitkaWmsDrLUjrLVHvRVgZZWSkkJkZKTbSaLORWpqKsHBwUWOz5gxgyeffJJTp04Ve52f\nnx+TJ09m8uTJhY5v2bLF2SWvVatWLu9rjKFRo0bO/cjISI8mtAoICADg/9m78/Aqqmz//+9FIIRA\nEqCREJmCCqKI0AoiihrBERU0CopcaNGrXrnYrdiNLYiigiO23e2Xbuz7wwG0cQJE0Ys4oa1cbQVB\nxAFFCTNGpgCBQJL9++MkRzKcopKcSk6Sz+t56iFVu06dVZqVnJ3aa+/f/va3tG7dmpYtWzJ27NgS\nw7JFRERERES8+J2lOaucLdjZl+q59u3bs27dujLD2po2bUpubm54389ETuvXr2fp0qWceeaZZdrO\nOeccjjnmGP72t79FHBo9atQodu7cydy5v4xc79evH7t37yYnJ4eVK1dGfG/nHOvXrw/vr1u3zteS\nRc2bNy/RUYbDD90WkZqjYbaRpacHPPt/erCz/0vVKT9EvClHJEi+OrxmdrGZjYnQ9t9mNjC6Yckp\np5xCWloaf/zjH8nNzSUvL48lS5bQs2dPPvjgA9avX8+uXbt48MEHI15j3759vP/++1x66aWceuqp\nXHjhheWeN3nyZB5++OGI14mLi2PSpEk89NBDlbqX++67j3379rFq1SqeeuoprrrqqnBb6ZlID10+\nadSoUTz++ONkZ2ezY8cOHnvsMS65pM6sgCUi9URW1lacI7AtK0t/fxYREYnE77JEE4k8MVWTovY6\nMda0Y/vUQJcO6tg+1dd5DRo04LXXXuPmm2+mQ4cONGjQgKuvvpo///nPDB06lBNPPJEjjjiC22+/\nnddee63Ea8eMGcOtt94KwDHHHMOQIUMYO3ZsxPc67bTTOOWUUzzrdIcNG8YDDzzAzp07fcV/qLPO\nOotjjjkG5xzjxo1jwIABQGhSq06dOoWfUjRp0oT09HR++OEHACZOnMjPP/9Mly5daNKkCVdeeSXj\nx4+v8PuLSPBUfyUSmfJDxJtyRIJkpZeiKfcks13AFc65t8ppOwd42TnXPID4osLMXHn3aWZlluKR\n6MnKyuKoo47i4MGDUatFjhX63hERv0I/L4K8ftll5URqE+WIiLcgc6Qu5UfR5/MyNZB+eyENgGYR\n2pKARpUNTOq2upJAIuJN9VcikSk/RLwpRyRIfju8K4DyF2sNHf8iOuFIXaOJpkREREREpKb4HdJ8\nGaH1ducA/wNsANoCNwCZwBDnXKQa3xqnIc0SbfreERG/NFxTxJtyRMSbhjT7E2lIs68Ob9EFbgam\nAE2LDwF7gDucc9OiFWgQ1OGVaNP3joj4pQ/zIt6UIyLe1OH1p6o1vDjnHif0VHcgMAK4ADgy1ju7\nIiISPNVfiUSm/BDxphyRIPldlggA59xuIPLaNSIiIiIiIiIxIuKQZjM7yTm3rEIXM0sA0p1z30Qj\nuGjRkGaJNn3viIhfGq4p4k05IuJNQ5r9qcyQ5g/M7FUzu8DMPIc+m1kHMxsP/AhcXMVYRURERERE\nRKrMqyN7LPAzMB/YWtT5vc/MfmtmN5rZH83sH2a2HPgBGAXc5pyb6vWGZna5mb1sZmvNLNfMvjGz\n+80s0jq/h762sZk9Ymabil67xMzOqMD9SgxbuXIl3bt3JyUlhaeeeqqmwxGRClD9lUhkyg8Rb8oR\nCVLEDq9zbqNz7lqgPTAVaAaMBf4M/B24HxgAfE7oqW4X59w/fbzn74F84A7gfOBvwE3AIh+vfRK4\nDrgTuAjYDLxpZif6eK0v6R3bYGaBbekd2xw2hqSkJJKTk0lOTiYuLo7ExMTwsdmzZ0frVqusTZs2\nNG3alOTkZNq2bcv1119PXl4eAAMGDAjHHB8fT0JCQnh/7Nix5V5vypQpXH755ezatYtRo0Z5vveB\nAwfIyMjgiCOOoEWLFvTq1YtFi375Fpo7dy6nnXYaLVq0oF27dowZMyYcm4iIiIiI1A++lyUKv8Cs\nOZAAbHPOHazwG5r9yjm3rdSxEcDTwADn3OIIr+tBqHN9jXNuZtGxOGAV8I1z7lKP9/Rdw2tmuO8q\nckcVY50rNk7+qKOOYsaMGZx99tnBBVVBBQUFxMXFkZaWxty5c+nbty+bN2/mnHPO4eqrr2bChAkl\nzh82bBjdu3dn/Pjxntft27cvt956K0OHDj1sDIWFhXzzzTccd9xxmBkffPABF110ERs3biQ5OZlZ\ns2bRtm1b+vXrx549e7jiiivo1asXDz/8cJXuvZhqeEXEL9UninhTjoh4Uw2vP1VelqiYc26nc25L\nZTq7Ra/fVs7hTwmt69vW46WDgAPAi4dcqwB4HjjfzBpVJp5Y55wr801YWFjIfffdx9FHH03r1q0Z\nMWIEOTk5AHz77bc0atSIp59+mvbt25OamsrUqb+MMl+yZAknnXQSKSkpHHnkkSU6p3PmzKFbt260\nbNmS8847j++//z7clpaWxqOPPsoJJ5xASkpKifiK28855xyWL19eqfvs06cPn332Gb/5zW9ITk5m\ny5YtDBkyhFtuuYWMjAySk5M5//zz2bJlCwANGjTg+OOPxyz0PW1m5OXlsWnTJgBGjBhB//79iY+P\np2XLlowaNYqPPvqoUrGJiIiIiEjtVOEOb0AyAAd87XHO8cCPzrn9pY6vAuKBY4IJLfY88sgjvP32\n2yxZsoQNGzbQqFEjbrnllnB7QUEBS5cuZc2aNbz++utMmDCBtWvXAjBmzBgmTJjArl27+O6777j0\n0tCD8ZUrVzJq1CimT5/OTz/9xJlnnsngwYMpLCwMX/fFF1/knXfeYdu2sn+zWLduHYsWLaJz586V\nuqdPPvmEnj17MnPmTHJycmjTJjT0+9lnn2Xq1Kn8/PPPdOjQocxQ5/79+5OQkEBGRgaDBw+ma9eu\n5V7/gw8+oFu3bpWKTUQOT/VXIpEpP0S8KUckSDXe4TWztsA9wFuHWQapJbCjnOPbD2mvF5544gke\nfPBBUlNTiY+PZ+LEibzwwgvhdjPj3nvvJT4+nl69etG1a1e++OILAOLj41m9ejXbt2+nadOm9O7d\nG4AXXniBzMxMzjjjDBo2bMj48ePJzs7ms88+C1937NixpKam0rhx4/CxCy+8kOTkZNLT0+nUqVOZ\n4cwVVfppdmZmJr169SI+Pp7777+fN998k507d4bb3333Xfbs2cMrr7wScdj3/PnzmT9/PhMnTqxS\nbCIiIiIiUrvUaIfXzJoSmgX6AHBtTcZSm6xfv56BAwfSsmVLWrZsyUknnQTA9u2hvn9cXBwtWrQI\nn5+YmMiePXsAeOaZZ1ixYgVdunShb9++4YmeNm3aRMeOHcOvadCgAW3btmXjxo3hY+3atSsTy8KF\nC8nJyeGtt97iyy+/DMdwOGeddVZ4EqtXX3014nnt27cPf33EEUeQkJDA5s2bS5zTsGFDLrnkEmbP\nnl3mL4SLFy/m+uuv57XXXitxLRGJroyMjJoOQSRmKT9EvClHJEgNa+qNzSwBWACkA2c65zYd5iU7\ngA7lHC9+suvZ07rmmmtIT08HoHnz5vTs2bMi4caUdu3aMXfuXH7961+XacvOzvZ87bHHHsvzzz+P\nc47Zs2eTmZnJzp07OfLII8nKygqfV1hYyMaNG0t0covrZQ9V/ER2wIABDB06lHHjxvmaSfr9998/\n7DkQ6twXy87OJi8vj7S0tHLPzc/PZ82aNeEfmv/3f//HlVdeyQsvvECfPn18vV9lFHeyi99X+9rX\nvvZL7xcr3i3+bBet/V+uXz33o33tR3O/mPJD+9r32qdon6juF6v5+6v4/vLly8MjP4vLN8tVPClS\ndW6EOtqvA7uA3j5fMxHYDySUOj4J2Ac08nitK095xwHnvgtuixRLJOnp6e6dd94pceyBBx5w5557\nrlu/fr1zzrmtW7e61157zTnn3DfffOMaNmxY4vxTTz3VPffcc84552bOnOm2bdvmnHPutddec0lJ\nSS4/P9998cUXLjk52f3rX/9yBw8edJMnT3bHHXecKygocM4516ZNG/fRRx+VuG7pY5s2bXJNmjRx\n33zzTYnzrrrqKjdlypTD3muvXr3cCy+8EN6/4oor3K9+9Sv32Wefuf3797vrr7/eXXDBBc455774\n4gv31ltvuf3797sDBw64J554wiUkJLhvv/3WOefcp59+6lq1auUWLFhw2PetjIr+fxSp6957772a\nDiFmBf2rVj+PYp/yw5tyRJQj3oLMkbqUH0X3UuYmK/SEt2i92zOBXwFPOOe2mNkxwFbn3G6f1zDg\nn0AGcJFz7lOfb/8aoVrfIcCsomvFAUOBN10lZ40urWOHVKzz1mhcKuL1K6K8p6q33347cXFx9O/f\nn61bt5KamsqIESO4+OKLy33NofsLFixg7NixHDhwgPT0dF566SXi4uLo3r07M2bM4IYbbmDLli2c\ndNJJzJ8/nwYNGkSMo/SxtLQ0hg0bxuTJk5k1a5bnPfi91xEjRnDbbbexbNky+vTpwzPPPAOEJua6\n4447WL16NQ0bNqRr16688sordOnSBYCHH36YnTt3MmzYsOI/etCtWzc+/vhjX7GIiIiIiEjt52sd\nXjNrDDwLZBJaPsgRejK7zMzmAqudc3/09YZmfwduBCYTesp7qA3OuY1m1gH4AZjknJt8yGtnA+cB\n44AfgdHAQKCvc26Fx3u68u5Ta6nGtiFDhtC7d2/GjRtX06GUoe8dEfFLa4yKeFOOiHjTOrz+VHUd\n3inAOcAIIJVQp7fY/wLnVyCWCwh1mCcAS0pt1xXHe8h2qGuAp4D7CNX/tgXO9+rsioiIiIiISP3k\nt8M7DLjTOfdPyk4O9SOhiad8cc51cs7FRdjuLTonq2j/vlKvzXPO/d45d6RzLtE519c59y+/7y21\ni9+h0CJS8xaXnv1CRMKUHyLelCMSJL81vL8Cvo7Q1gBoHKFNpNJefPHFmg5BRERERERqMb81vF8D\nzzrnphRNFHUQ6FVUw3sncLlzruwaOTFCNbwSbfreERG/VJ8o4k05IuJNNbz+VLWGdybwRzMbDjQq\nOubM7GzgVuDJ6IQpIiIiIiIiEh1+O7wPE5pReRawo+jYh8DbwELn3OMBxCYiIrWE6q9EIlN+iHhT\njkiQfNXwOucKgKvMbBqhGZlbA9sIdXbfDzA+ERERERERkUrxVcNb26mGV6JN3zsi4pfqE0W8KUdE\nvKmG15+q1vAeeqHWZtah9BadMKU+W7lyJd27dyclJYWnnnqqpsMREREREZFazleH18ySzewpM8sF\nNhNae7f0Viekp7fBzALb0tPbHDaGpKQkkpOTSU5OJi4ujsTExPCx2bNnV8N/BX/atGlD06ZNSU5O\npm3btlx//fXk5eUBMGDAgHDM8fHxJCQkhPfHjh1b7vWmTJnC5Zdfzq5duxg1atRh33/cuHGccMIJ\nNGzYkD/96U9l2rds2cLQoUNJSUmhVatW3HjjjVW7YRGJSPVXIpEpP0S8KUckSH7X4Z0GXA7MAFYC\neYFFVMOysrYGPKxm62HP2b17d/jro446ihkzZnD22WcHF1QFFRQUEBcXh5nx9ttv07dvXzZv3sw5\n55zD1KlTmTBhAu+88074/GHDhtG9e3fGjx/ved2srCwyMzN9x3Hcccdx7rnnMnXq1HLbL7roIi65\n5BKefvpp4uPj+eqrr3xfW0REREREaj+/Q5ovAP7gnLvZOfcP59wzpbcgg6zPnHNlxtUXFhZy3333\ncfTRR9O6dWtGjBhBTk4OAN9++y2NGjXi6aefpn379qSmppboEC5ZsoSTTjqJlJQUjjzySCZMmBBu\nmzNnDt26daNly5acd955fP/99+G2tLQ0Hn30UU444QRSUlJKxFfcfs4557B8+fJK3WefPn347LPP\n+M1vfkNycjJbtmxhyJAh3HLLLWRkZJCcnMz555/Pli1bwq8ZNWoU5557LomJiWWuN2/ePPLy8pg0\naRKJiYk0bNiQE088sVKxicjhZWRk1HQIIjFL+SHiTTkiQapIDe+3gUUhFfLII4/w9ttvs2TJEjZs\n2ECjRo245ZZbwu0FBQUsXbqUNWvW8PrrrzNhwgTWrl0LwJgxY5gwYQK7du3iu+++49JLLwVC9bOj\nRo1i+vTp/PTTT5x55pkMHjyYwsLC8HVffPFF3nnnHbZt21YmpnXr1rFo0SI6d+5cqXv65JNP6Nmz\nJzNnziQnJ4c2bUJDv5999lmmTp3Kzz//TIcOHXwNdQb4+OOPOeaYYxgyZAitWrXi9NNP55NPPqlU\nbCIiIiIiUjv57fA+D1wSZCDi3xNPPMGDDz5Iamoq8fHxTJw4kRdeeCHcbmbce++9xMfH06tXL7p2\n7coXX3wBQHx8PKtXr2b79u00bdqU3r17A/DCCy+QmZnJGWecQcOGDRk/fjzZ2dl89tln4euOHTuW\n1NRUGjduHD524YUXkpycTHp6Op06dSrxxLgySj/NzszMpFevXsTHx3P//ffz5ptvsnPnzsNeZ8OG\nDSxYsIChQ4eydetWrrvuOgYNGsSePXuqFJ+IlE/1VyKRKT9EvClHJEh+O7yLgIvN7Ekzu8LM+pfe\nggxSSlq/fj0DBw6kZcuWtGzZkpNOOgmA7du3AxAXF0eLFi3C5ycmJoY7es888wwrVqygS5cu9O3b\nl0WLFgGwadMmOnbsGH5NgwYNaNu2LRs3bgwfa9euXZlYFi5cSE5ODm+99RZffvllOIbDOeuss8KT\nWL366qsRz2vfvn346yOOOIKEhAQ2b9582Os3adKEbt26MWTIEOLi4rj22mtp2rQpn376qa/4RERE\nRESk9vM7adX8on87AdccctwBVvRvXPTCEi/t2rVj7ty5/PrXvy7Tlp2d7fnaY489lueffx7nHLNn\nzyYzM5OdO3dy5JFHkpWVFT6vsLCQjRs3lujkmpVZ1ir8RHbAgAEMHTqUcePG+ZpJ+v333z/sORDq\n3BfLzs4mLy+PtLS0w77uxBNPLPF0GsqPX0SiQ/VXIpEpP0S8KUckSH6f8J4dYet/yL9STW688UZu\nv/12NmzYAMBPP/3EggULwu1ei0fPmjWL7du3Y2YkJyfToEEDzIwrr7ySefPm8eGHH5Kfn88DDzxA\nq1atOPnkk33HddtttzF//ny+/TZ65d5z585l6dKl5OXlMWHCBM477zyaN28OQH5+Pvv376ewsJCD\nBw+Sl5cXvvehQ4fyww8/MHfuXAoLC5k5cya5ubnhIdwiIiIiIlL3+erwOufeP9wWdKDVpWPHVMwI\nbOvYMbVC8ZT3VPL222/n3HPPpX///qSkpNCvXz8+//zziK85dH/BggUce+yxpKSkMGHCBF566SXi\n4uLo3r07M2bM4IYbbqB169a89957zJ8/nwYNGkSMo/SxtLQ0hg0bxuTJkw97D37vdcSIEdx2220c\nccQR/Pjjj8yYMSPcNmzYMBITE1mwYAHjx48nMTGRefPmAaE1gufNm8edd95JixYtmD59OgsWLKBZ\ns2a+YhGRilH9lUhkyg8Rb8oRCZJ5PQ2sK8zMlXefZub5NFRq1pAhQ+jduzfjxo2r6VDK0PeOSEmL\nFy/WkLQIQj8vgry+98geqXnKD2/KEVGOeAsyR+pSfhR9Pi/zBC1iDa+ZvQuMds59U/S1F+ecG1DV\nIEVEpHbSBxWRyJQfIt6UIxIkr0mrDu0dNyA0MZWfc0WiQpNMiYiIiIhIVWhIcz24f4k+fe+IlKTh\naJFpuKYoP7wpR0Q54k1Dmv2JNKTZ7yzNIiIiIiIiIrVKxCe8ZnZmRS7knPsgKhEFQE94Jdr0vSMi\nfunplYg35YiINz3h9afCk1YBi/Gu2w1fu+i8uMqFJiIiIiIiIhJ9Xh3es6stihrSsWNHTYwkldKx\nY8eaDkEkpqj+SiQy5YeIN+WIBClih9c59351BlIT1q5dW9MhSC2hH8QiIiIiIrVPhWZpNrNWwKnA\nr4DXnHPbzSwBOOCcKwwoxiqLVMMrIiISNNUninhTjoh4Uw2vP1WapdlCHgE2AK8CTwLpRc3zgQlR\nilNEREREREQkKvwuS3QHMAa4F+hDaKKqYq8BF0c5LpGYsnjx4poOIeZNmzaN3r17k5CQwLXXXhvx\nvJtuuomkpCSSk5NJTk4mISGBlJSUcPuOHTu47LLLaNasGZ06dWL27NnVEb5UkXJEJDLlh4g35YgE\nyWvSqkP9J3Cvc+4BMys9G/P3wNHRDUtEapu2bdsyceJE3nzzTfbt2xfxvL///e/8/e9/D++PGjWK\nuLhffqyMHj2ahIQEsrOzWbZsGRdddBE9e/bkuOOOCzR+EREREal7fNXwmlkecIFz7r2iDu9BoJdz\nbpmZ9Qded841CTjWSlMNr0j1mThxIhs3buTJJ5887Ll79+4lLS2NN954g379+pGbm0uLFi346quv\nOPro0N/RRo4cSbt27bj//vuDDl0kEKpPFPGmHBHxphpef6pUwwtsBE6I0NYD+LGygYlI/TVnzhxa\nt25Nv379AFi9ejWNGjUKd3YBevTowapVq2oqRBERERGpxfx2eF8C7jKz0w855sysC3Ab8HzUIxOJ\nIaotCcbMmTMZOXJkeH/Pnj0kJyeXOCclJYXdu3dXd2hSQcoRkciUHyLelCMSJL8d3knAN8AHwHdF\nx14CVhbtPxj1yESkTlu3bh2LFy8u0eFt1qwZOTk5Jc7LyckhKSmpusMTERERkTrAV4fXObcPyACu\nAZYAbwOfAjcA5zrnDgQUn0hMyMjIqOkQ6pxnn32Wfv36kZ6eHj7WpUsX8vPzWbNmTfjYihUr6Nat\nWw1EKBWhHBGJTPkh4k05IkHyO0szzrkCYFbRJiJSQkFBAQcPHqSgoID8/Hzy8vJo2LBhiRmYDzVz\n5kzuuOOOEscSExPJzMzkrrvu4n/+53/4/PPPefXVV1myZEl13IKIiIiI1DG+nvCaWSsz61Dq2I1m\n9riZaQ1eqfNUW3J4kydPJjExkYceeojnnnuOxMREpkyZwvr160lKSmLDhg3hcz/++GM2btzIFVdc\nUeY606ZNIzc3l9atWzN8+HCmT5+uJYlqAeWISGTKDxFvyhEJkt8nvE8CG4DRAGY2EbgH2AGMNrOr\nnXMvBBOiiNQGd999N3fffXe5baUnnTr11FMjTkTVokUL5s2bF/X4RERERKT+8bsO7ybgZufcnKL9\njcBTzrk7zeyvQB/nXJ9gQ608rcMrIiI1RWuMinhTjoh40zq8/lR1Hd6WwNaiC50AtAGeKWp7BTg2\nGkGKSO2Unt4GMwtkS09vU9O3JyIiIiK1lN8O7zagXdHX/YFNzrni5YkaVeA6IrWSaku8ZWVtxTkC\n2bKyttb07YkPyhGRyJQfIt6UIxIkvzW8bwOTzKwVcBuhp7rFugJZ0Q5MREREREREpCr81vCmAs8C\npxJaf3eoc+7norZ/A0udczcFGWhVqIZXJFiqLRGJTPWJIt6UIyLe9DnLn0g1vL46vIe5cDKw3zl3\noEoXCpA6vCLB0g9ikcj0YV7Em3JExJs+Z/lT1UmrInLO5cRyZ1ckGlRbIuJNOSISmfJDxJtyRILk\nt4YXM2sNDCM0I3NCqWbnnLsumoGJiIiIiIiIVIXfGt5jgf8j1EFuCvxMaKmiOGAHsMs5d1SAcVaJ\nhjSLBEtDbUQi03BNEW/KERFv+pzlT1WHND9CaLKqVMCAC4EmwH8CucBlUYpTREREREREJCr8dnh7\nA38D8opf55zLd849Cfw/4M9BBCcSK1RbIuJNOSISmfJDxJtyRILkt8PbDNjunCsEdgGtDmn7lFCH\nWERERERERCRm+K3hXQnc65x7ycw+BlYVT1JlZn8itC5vu2BDrTzV8IoES7UlIpGpPlHEm3JExJs+\nZ/lT1Rret4Bzi77+EzDKzL41s1XA74AnoxOmiIiIiIiISHT47fDeAfwewDn3IjCY0FDmb4GbgLsD\niU4kRqi2RMSbckQkMuWHiDfliATJ1zq8zrk8fpmwCufca8BrQQUlIiIiIiIiUlW+anjDJ5slAycA\nbYGNwErn3O6AYosa1fCKBEu1JSKRqT5RxJtyRMSbPmf5E6mG19cT3qIL3AXcRmjG5uIL7TazR5xz\nk6MTpoiIiIiIiEh0+KrhNbN7gEnAC4Qmr+oOnAO8CNxjZpMCik8kJqi2RMSbckQkMuWHiDfliATJ\n7xPe64FHnXN/OOTYKuBdM9sF3ECoQywiIiIiIiISE/yuw7sXGOyce7uctnOA+c65pgHEFxWq4RUJ\nlmpLRCJTfaKIN+WIiDd9zvKnquvwfgL0jtDWu6hdREREREREJGZE7PCaWYPiDfgtcK2Z/cHM0s2s\nSdG/44BrgTHVFbBITVBtiYg35YhIZMoPEW/KEQmSVw1vPnDo820DHizaKHX8i8NcS0RERERERKRa\nRazhLZp52feAbufcPVGKKepUwysSLNWWiESm+kQRb8oREW/6nOVPpBpeX5NWBRBMW+CPwMlAD6AJ\nkO6cW+fjtWuBDqUOO+Ay59yrEV6jDq9IgPSDWCQyfZgX8aYcEfGmz1n+VHXSqmg7BrgC2A58QAWe\nJBeduxA49ZCtL/B+lGMUCVNtiYg35YhIZMoPEW/KEQlSjdTdOufeB9IAzOw64LwKXuJn59y/ox6Y\niIiIiIiI1Bk1MqS5RAChDu8/gE4+hzT/CPzLOTeyAu+hIc0iAdJQG5HINFxTxJtyRMSbPmf5E2tD\nmqvqEjPba2b7zez/zGxwTQckIiIiIiIisaU2dnhfBW4mNAz6amAfMM/Mrq7RqKROU22JiDfliEhk\nyg8Rb8oRCVKtWzvXOfe7Q/fN7BXgY+AB4J81EpSIiIiIiIjEHN8dXjNLBgYSWhIooVSzc87dF83A\n/HLOFZrZS8CDZpbqnNta3nnXXHMN6enpADRv3pyePXuSkZEB/PJXJe1r32u/WKzEE2v7xYp3iFh8\nXwAAIABJREFUi5qrvF/8HjV9f9rXflX2i0U7P0o/FImV+9W+9iuyX0z5UX/3MzIyYiqe2NynaJ+o\n7her+fur+P7y5cvZuXMnAGvXriUSX5NWmdnpwGtA8winOOdc3GEvVP61KzRpVYRr/AF4EEhzzv1U\nTrsmrRIJkCZTEIlME/KIeFOOiHjT5yx/qjpp1Z+BtUBvIME516DUVqnObjSYWRxwFbCuvM6uSDSU\n/iu0iJSkHBGJTPkh4k05IkHy2+E9DrjTObfUOXcgGm9sZpeb2eVAL8CAgUXHzjzknHwz+59D9q8y\ns9lmNsLMMszsKmAx0BMYF424REREREREKmLatGn07t2bhIQErr32Wl+vGTBgAA0aNKCwsDB87K67\n7uLEE0+kUaNG3HvvvUGFW6/4reFdBzSO8nu/BBQ/P3fAtKKv3wf6F31tlOyU/wgcATwMtAT2Ap8B\n5zvn3o5yfCJhxfUCIlI+5YhIZMoPEW91IUfatm3LxIkTefPNN9m3b99hz//nP/9Jfn4+ZiVH4Hbu\n3JlHHnmE6dOnBxVqveO3w3sP8Ecze8c5lxONN3bOHfbpcumh0s65T4BzovH+IiIiIiIi0XDppZcC\n8Omnn7Jx40bPc3Nycrj33nuZOXMmffv2LdE2YsQIAJ599tlgAq2H/A5pvhhIBX40swVmNrPU9kyA\nMYrUONWWiHhTjohEpvwQ8VbfcmT8+PGMHj2a1NTUmg6lXvD7hLcfoWHHOUC3ctrrxtReIiIiIiIi\nAfnss89YsmQJjz/+OOvWVWqBGqkgXx1e51ynoAMRiWV1obZEJEjKEZHIlB8i3upLjjjn+O///m/+\n8pe/FC+hU9Mh1Qt+hzSLiIiIiIhIJeXk5LB06VKuvPJK0tLSOOWUU3DO0a5dOz766KOaDq/O8t3h\nNbOmZvZbM3vZzN4zs85Fx68ys67BhShS8+pbbYlIRSlHRCJTfoh4qws5UlBQwP79+ykoKCA/P5+8\nvDwKCgpKnJOSksKmTZtYvnw5K1as4I033gBg2bJl9OnTB4D8/Hz2799PYWEhBw8eJC8vr9rvpa7x\n1eE1s/bAF8AjQGfgTCCpqPls4PeBRCciIiIiIhLjJk+eTGJiIg899BDPPfcciYmJTJkyhfXr15OU\nlMSGDRsAaN26dXg74ogjMDNat25Nw4ahStPrr7+exMREnn/+ee6//34SExNr8rbqBPMzdtzMXgRO\nAC4ENgIHgF7OuWVmdjVwt3Pu2EAjrQIzcxojLxKcUB1KUNdGNS5SqwWZH6HrK0ekdlOOiHjT5yx/\niuqirfRxv7M0nwvc4JzLMrO4Um0bgbZVDVBERERERKS2Se/Yhqx1W2s6DInAb4c3HtgdoS0FyI9O\nOCKxafHixfVmBkGRylCOiESm/BDxVttzJGvdVtx3wV0/NHOSVJbfSau+AC6P0HYhsDQ64YiIiIiI\niIhEh98a3kzgZWAG8E/gHWAkoQms7gAGOecWBhhnlaiGVyRYqi0RiUz1iSLelCNS25lZ4E949Tnr\n8CLV8Prq8BZd4L+ABwnNzlx8od3AH5xz/4hWoEFQh1ckWOrwikSmD/Mi3pQjUtupwxsbInV4fa/D\n65ybTmhyqvOB/yA0lLldrHd2RaKhLqwPJxIk5YhIZMoPEW/KEQmSr0mrzOxXzrltzrm9wNsBxyQi\nIiIiIiJSZX5reA8AbwCzgNeccweCDiyaNKRZJFga0iwSmYZrinhTjkhtpyHNsaGqQ5rvBI4CXgK2\nmNl0Mzs9mgGKiIiIiIiIRJOvDq9z7mHn3InAScBTwCXAB2a2xswmmdkxQQYpUtNUWyLiTTkiEpny\nQ8SbckSC5HvSKgDn3HLn3G1Ae0KTVn0E3AZ8E0BsIiIiIiIiIpXme1miMi8MDWkeCQwBUpxzcdEM\nLJpUwysSLNXwikSm+kQRb8oRqe1UwxsbItXw+pql+ZCLdAZGAMOBdGAj8AShyaxEREREREREYoav\nIc1mNsbMPiY0dPkW4APgXKCjc+4O59xXAcYoUuNUWyLiTTkiEpnyQ8SbckSC5PcJ72OE1t8dAcxz\nzu0LLiQRERERERGRqvO7Dm+qc25rNcQTCNXwigRLNbwikak+UcSbckRqO9XwxoYqrcNbmzu7IiIi\nIiIiUj9F7PCa2btm1vWQr722d6ovZJHqp9oSEW/KEZHIlB8i3pQjEiSvGt5DHwc3ALyedZd5dCwi\nIiIiIiJSkyq9Dm9tohpekWCphlckMtUninhTjkhtpxre2FClGl4RERERERGR2sbvOrxnemz9zKy7\nmTUKOliRmqLaEhFvyhGRyOpCfkybNo3evXuTkJDAtddeG/G8mTNn0qtXL1JSUujQoQO33347hYWF\n4fYdO3Zw2WWX0axZMzp16sTs2bOrI3yJcXUhRyR2+V2HdzHeNbwAuWb2V+fchKqFJCIiIiKxpG3b\ntkycOJE333yTffv2RTxv3759/OUvf6FPnz5kZ2dzySWXMHXqVMaNGwfA6NGjSUhIIDs7m2XLlnHR\nRRdV1y2ISD3ldx3eS4DHgRXAy8BWIBUYCpwITAROAW4E7nDOTQ0q4MpQDa9IsFTDKxKZ6hOlLpk4\ncSIbN27kySef9HX+Y489xuLFi5k/fz65ubm0aNGCr776iqOPPhqAkSNHMmvWLOWI1Gqq4Y0NVa3h\nvRRY6Jwb7Jyb5ZxbVPTvJcBC4Gzn3Bjg78B10QtbRERERGqrDz74gG7dugGwevVqGjVqFO7sAvTo\n0aOmQhOResJvh/cyYE6EtpeBwUVfLwQ6VTUokVij2hIRb8oRkcjqa348+eSTLF26lN///vcA7Nmz\nh+Tk5BLnpKSk1ERoEmPqa45I9fDb4Y0Djo7QdkxRO0Be0SYiIiIi9dQrr7zChAkTWLhwIS1btgSg\nWbNm5OTklDiv9L6ISLT57fC+AdxvZpebWRyAmcWZ2RXAFOD1ovO6AWuiH6ZIzcrIyKjpEERimnJE\nJLL6lh8LFy7kxhtvZMGCBRx//PHh4126dCE/P581a375qLhixYqaCFFiTH3LEalefju8NwOrgJeA\nfWa2FdgHvFh0/Oai83YB90c7SBERERGpOQUFBezfv5+CggLy8/PJy8ujoKCgzHnvvvsu//Ef/8Gc\nOXM4+eSTS7QlJiaSmZnJXXfdRW5uLh999BGvvvpqdd2CiNRTvjq8zrmfnXNnABcA9xGq570PON85\nd4ZzblvRec84514OLFqRGqLaEhFvyhGRyOpCfkyePJnExEQeeughnnvuORITE5kyZQrr168nKSmJ\nDRs2hM/Lyclh4MCBJCUlkZycXGLpoWnTppGbm0vr1q0ZPnw406dPr6lbkhhSF3JEYpevZYlqOy1L\nJFW1ePFiDbfxoGWJRDkSmZYlEuWHN+WI1PYc0bJEsSHSskTq8IpIlanDKxKZPsxLbZfesQ1Z67YG\n+h7KEanN1OGNDZE6vA1rIhgRERERqR2y1m0N/MO8iEhQ/E5aJVKvqbZExJtyRCQy5YeIN+WIBEkd\nXhEREREREamTVMMrIlWmGl6RyFTDK7Vdba5PBOWIBK8250hdyo+o1PCa2YnAmcCvgCecc1vM7Bhg\nq3Nud3RCFREREREREak6X0Oazayxmb0EfA78FbgLOLKo+WFgQjDhicQG1ZaIeFOOiESm/BDxphyR\nIPmt4Z0CnAOMAFKBQx8V/y9wfpTjEhEREREREakSXzW8ZrYRuN85N83M4oCDQC/n3DIzOwd4yTnX\nIuBYK001vCLBUg2vSGSq4ZXarjbXJ4JyRIJXm3OkLuVHpBpev094fwV8HaGtAdC4soGJiIiIiIiI\nBMFvh/dHoG+EtlOAb6MTjkhsUm2JiDfliEhkyg8Rb8oRCZLfDu9M4I9mNhxoVHTMmdnZwK3Ak0EE\nJyIiIiIiIlJZfmt444DngKFAHqEhzPuABOB559zwIIOsKtXwigRLNbwikamGV2q72lyfCMoRCV5t\nzpG6lB9VWofXOVcAXGVm0wjNyNwa2AYsdM69H9VIRURERERERKLA75BmAJxz/3LO3emcu8E5d4c6\nu1JfqLZExJtyRCQy5YeIN+WIBMnXE95DmVlrQkOZS3DOrYtKRCIiIiIiIiJR4LeGNxn4C3AlEZYg\ncs7FRTe06FENr0iwVMMrEplqeKW2q831iaAckeDV5hypS/lR1XV4pxHq7M4AbgKuLWcTERGptaZN\nm0bv3r1JSEjg2mu9f6099thjpKWl0bx5c/7zP/+TgwcPApCdnc3VV19N27ZtadGiBWeccUZ1hC4i\nIiIR+O3wXgD8wTl3s3PuH865Z0pvQQYpUtNUWyLirS7kSNu2bZk4cSLXXXed53lvvvkmDz/8MO+9\n9x5ZWVmsWbOGu+++G4A9e/Zwyimn8Pnnn7N9+3ZGjhwJQG5u4OFLDKsL+SESJOWIBKkik1Z9G1gU\nIiIiNezSSy9l0KBBtGzZ0vO8mTNnct1119G1a1dSUlKYOHEiTz31FACdOnXilltuoXXr1pgZ119/\nPQDf6jeoiIhIjfDb4X0euCTIQERiWUZGRuDvEY3hlABZWVn079+fpk2bcvzxx/POO+8EHbpIvcqR\nVatW0aNHj/B+jx49+Omnn9ixY0eZc5cvXw7AMcdU6C2kjqmO/IDgckQkaPXpd4hUv4gdXjPrX7wB\ni4CLzexJM7vi0LZDzhGRKojGcEqAYcOGcfLJJ7N9+3YmT57MFVdcwbZt24IOXyRwsZIje/bsISUl\nJbyfkpKCc47du3eXOC8nJyc8pDkpyfflRSotqBwB2Fb27zkitUqQv0Mktnk94X0beKvo31eATsA1\nwItFxw5tfyvQKEVqWHXUlkRjOOXq1av5/PPPmTRpEo0bNyYzM5Pu3bszZ86cwOOX+q0+5UizZs3I\nyckJ7+fk5GBmJB3Sq92/fz+DBg3itNNOq+BdSl1UXfWJQeUIwJw3Aw9f6rHa/jtEYptXh/dsoH+p\nf0tvh7b7ZmZtzexxM1tiZnvNrNDMOvh8rZnZHWb2o5ntM7PlZpZZkfcXqc28hlN+9dVXHHXUUTRt\n2rRE+6pVq2oiVJEaEXSOdOvWjRUrVoT3ly9fTmpqKi1atADgwIEDXHrppXTo0IHp06dH4Y5Eoqui\nOQKwKsAlV0RiSWV+h0hsaxipwTn3foDvewxwBbAU+AA4rwKvnQyMBcYDy4CrgJfM7CLn3MJoByoC\n1Vd/5Ud5wykBdu/eXaatuH3Tpk3VGqPUP3UhRwoKCjh48CAFBQXk5+eTl5dHw4YNiYsrucz8yJEj\nGTVqFFdffTVpaWlMmTKFUaNGAZCfn8/ll19OYmIiTz/9dLA3KrVGLOUHVDxHAHbvrbbwpB6KpRyp\nzO8QiW2+Jq0ysx/MrNw/X5jZCWb2Q0Xe1Dn3vnMuzTl3MfCy39eZ2RHAbcADzrnHiq5zE/Ae8GBF\nYhCprcobTgmQlJRUpq24PUkFhFKPVDZHJk+eTGJiIg899BDPPfcciYmJTJkyhfXr15OUlMSGDRsA\nOP/88xk3bhxnn3026enpdOrUiUmTJgGwZMkS3njjDRYtWkRKSko49z76qBpuXMSniuYIQFLTModE\n6qTK/A6R2OZ3luZ0oHGEtgSgY1SiObwLgEbAc6WOPwt0N7PqikPqmVhaH85rOGW3bt344Ycf2Lv3\nlz/Fr1ixgm7dutVEqFKP1OYc+cc/pvPXv/6VSZMm4ZyjsLAwvN1999106NCBPXv20L59e8wMM+PW\nW29l69at7Nq1ixkzZhAfH4+ZcdZZZ1FYWMjevXvZs2cPe/bsAeD006v9P4PEkFjKD6h4jgB061zd\nUUp9Eks5UpnPWRLbKrIOr4twvBewMwqx+HE8kOecW1Pq+CrAitpFaqWCggL2799fYjhlQUFBmfNG\njhzJjBkz+Prrr9m5c2eJ4ZSdO3emZ8+e3HPPPeTl5TFv3jxWrlzJ5ZdfXt23IxJ1QeVIXt5Bfv43\nuO+C2USqS1A5AnD5+dV6KyJRF+TnLIlxzrlyN+BWYF3RVgBsOWS/eMsuansu0nUOtwHXFV2jg49z\nnwA2lXP8aKAQGB7hdU4k1k2aNMmZmWvQoEF4u+eee9y6detcs2bN3Pr168PnPvbYYy41NdWlpKS4\n6667zh04cCDclpWV5TIyMlyTJk1c165d3bvvvht47B4/Sqq8KX+lWFA5Ajj3XXBbkPmhHJFDKUeU\nIxJZkJ+zanOO1KX8KLqXMjdpobayzGwwcGnR7m+AN4o6uIfKA74C/j/nXG7Fu9tgZtcB/wA6OefW\nHebcJ4BLnHNHljp+NPAdMMI5V3q4M2bmIt2niFSdmRFUipmFfsqLBMXMAn0Sa50JLD9AOSLBU46I\neKvNOVKX8iP0edRZ6eNeszTPB+YXvxi41zn3Y2AR+rMDaF7O8eIFtbZHeuE111xDeno6AM2bN6dn\nz57hGeGK6wa0r/1I+8uXL+eWW24J7PpXDc1ka/YOgpJ6RAu2/LQ9avGWt1+seLd4wsWq7he/Ryx9\nP2i//P//GRkZgV3/mpFXkbV+K0Fb/Eno34w+0d0PX39xUXtGdPd/uf7iovYM7cfQfvEx5Uf5++Hr\nLy5qz4ju/i/XX1zUnqH9GNsvnSvRvn56hzaB50hQ+RHeX1y0nxHd/XD8MfT94Hd/+fLl7NwZqqxd\nu3YtkUR8wltdKviEdwTwNNDZOffDIcevAWYARznnssp5nZ7wSpUsPqTDFQQzw70R2OWxgcH+9U5P\neKU254gNDLbWVk+vpDbnByhHJHjKkcNcX094fYn0hLdBTQRTBQuBfGB4qeP/AXxZXmdXJBqC/CEs\nUhcoR0QiU36IeFOOSJAiDmkOmpkVTxvbi9AMywPNLBvIds59UHROPvCUc+56AOdctpn9CbjDzPYA\ny4CrgAzgkmq+BREREREREYlhNfmE9yXgReAGQkseTSvan3TIOUbZGMcDk4HfEnri2xcY4pz734Dj\nlXpscelCBxEpQTkiEpnyQ8SbckSCVGNPeJ1zh+1sO+fiyjnmgPuLNhEREREREZFyHbbTaWbxZrbM\nzM6rjoBEYlFdqS3ZsWMHl112Gc2aNaNTp07Mnj273PMOHDjAf/3Xf9GmTRtatWrF4MGD2bx5c7j9\nm2++YcCAATRv3pwuXbpUV/gSw+pKjogEQfkh4k05IkHy85T1ANCJ0GRRIlKLjR49moSEBLKzs3n2\n2We56aab+Prrr8uc9+c//5lPPvmEL7/8kk2bNtG8eXPGjBkDQEFBAYMHD2bQoEHs2LGDJ554AoDv\nv6/WWxEREREROSy/NbxvAXrCK/VWXagtyc3NZe7cuUyePJkmTZpw+umnM2jQIGbNmlXm3LVr13L+\n+efTqlUr4uPjufLKK/nqq6+A0NPdzZs387vf/Q4z4+yzzwagnMtIPVIXckQkKMoPEW/KEQmS3w7v\n48AwM5tqZv3M7GgzO+rQLcggRaTqVq9eTaNGjTj66KPDx3r06MGqVavKnHvdddfx4YcfsnnzZnJz\nc3nuuecYOHCg5/W//DLqIYuIiIiIVInfDu/7QHtgbNHXq4HvSm0idVZdqC3Zs2cPycnJJY6lpKSw\ne/fuMud27tyZ9u3b07ZtW5o3b84333zDxIkTATj22GNp3bo1U6dOJT8/n0WLFgGQmxv8PUjsqgs5\nIhIU5YeIN+WIBMnvLM2jAo1CRALXrFkzcnJyShzLyckhKSmpzLmjR4/mwIED7Nixg8TERB566CEu\nuOACPv74Yxo2bMgrr7zCmDFjeOihh+jVqxcA7dpVy22IiIiIiPjm6wmvc+6Zw21BBypSk+pCbUmX\nLl3Iz89nzZo14WMrVqygW7duZc5dsWIF11xzDSkpKTRq1Iibb76Zf//732zfvh2AE044gcWLF5Od\nnc3//m9oCexTTqme+5DYVBdyRCQoyg8Rb8oRCZLfIc0AmFkDMzvBzM4ys6ZBBSUi0ZeYmEhmZiZ3\n3XUXubm5fPTRR7z66quMGDGizLm9e/dm5syZ5OTkcPDgQaZNm0bbtm1p2bIlACtXriQvL4/c3Fym\nTp0KwDXXVOfdiIiIiIgcnu8Or5n9N7AF+AJ4Fzi26PgrZvbbYMITiQ11pbZk2rRp5Obm0rp1a4YP\nH8706dM57rjj+PDDD0vU906dOpXGjRvTuXNnUlNTWbhwIfPmzQu3z5o1i7S0NNq0acN7770HQKNG\n1X47EkPqSo6IBEH5IeJNOSJBMufc4U8yux74O/AksAh4EejlnFtmZrcBg5xzZwUaaRWYmfNznyI1\nxcxwbwR4/YEQZA6YGUFd3izY2KV2CDJHbCC4AKdetM4Elh+gHJFq+h2iHJFaTDnice06lB+hz6PO\nSh/3+4R3LPCoc+4GYF6ptm8oetorUlfV9tqSxvGhHwJBbSK1PUdEgqT8EPGmHJEg+Z2luRPwZoS2\nvUDz6IQjIkHIOxD8Xx5FRERERGKN3ye8PwPpEdqOBTZGJRqRGKXaEhFvyhGRyJQfIt6UIxIkvx3e\nBcBdZnbUIcecmbUCbgVeiXpkIiIiIiIiIlXgt8N7J5AHfAm8DTjgr8DXQAFwbyDRicQI1ZaIeFOO\niESm/BDxphyRIPnq8DrnfgZ6AQ8AjYA1hOp//x/Q1zm3K7AIRURERERERCrB17JEtZ2WJZJYp+ny\nPa5dh6bLl8rTskQe11eO1Hv6HXKY6ytH6j3liMe161B+VGlZIjN7zMx6Rj8sERERERERkWD4reG9\nBlhqZl+a2TgzaxtgTCIxR7UlIt6UIyKRKT9EvClHJEh+O7ypwFDge+A+IMvM3jazkWbWNLDoRERE\nRERERCrJ76RVB5xzc5xzlwJpwG+BJsDTwFYzmxVciCI1T+vDiXhTjohEpvwQ8aYckSD5fcIb5pzb\n7pz7m3PudOBsYAdwddQjExEREREREamCCnd4zaypmf3GzN4itCZvK2BO1CMTiSGqLRHxphwRiUz5\nIeJNOSJB8jtLcwMzu8DMngO2Ak8CjYGbgDbOuaEBxigiIiIiIiJSYQ19nrcJOILQpFUPAs8659YG\nFZRIrFFtiYg35YhIZMoPEW/KEQmS3w7vy8As59wnQQYjIiIiIiIiEi1+Z2keo86u1GeqLRHxphwR\niUz5IeJNOSJB8j1plZmlmdlUM/vUzNYU/fuwmbUJMkARERERERGRyvA7aVUXYAWh9Xf3AP8u+vd3\nwHIz6xxYhCIxQLUlIt6UIyKRKT9EvClHJEh+a3gfAnYBpxw6WZWZdQQWFbVnRj06ERERERERkUry\nO6T5bGBi6ZmZnXNZwKSidpE6S7UlIt6UI1IX7Nixg8suu4xmzZrRqVMnZs+eXe55AwcOJCkpieTk\nZJKTk2ncuDE9evQIt2dlZdG/f3+aNm3K8ccfz6OPPlpdtyBSK+l3iATJb4c3HtgdoW13UbuIiIhI\nrTV69GgSEhLIzs7m2Wef5aabbuLrr78uc94bb7zB7t27ycnJIScnh9NOO42hQ4eG24cNG8bJJ5/M\n9u3bmTx5MpMmTWLbtm2+O9QAy5Yt46yzziIpKYm0tDQef/zxcNuSJUvo06cPycnJ9OzZM7r/EURE\n6hi/Hd7lwM1mVuJ8MzNgdFG7SJ1VXFsSrQ8rK1as4Mwzz6R58+Z06NAh6PBFAqf6K6ntcnNzmTt3\nLpMnT6ZJkyacfvrpDBo0iFmzZnm+bu3atfzrX/9ixIgRAHz33Xd8/vnnTJo0icaNG5OZmcmvf/1r\n5syZ47tDvW3bNi688EJuuukmduzYwffff895550HhH4PDRo0iNtvv51du3bxhz/8AYBde6P8H0Sk\nGul3iATJb4f3XuAc4Gszu9fMbjKze4BVwLnAPUEFKBJLovFhBeDqq68mIyODnTt3hofxLNDCXyIi\nNWb16tU0atSIo48+OnysR48erFq1yvN1M2fO5Mwzzwz/8XLVqlUcddRRNG3atMR1li9f7rtD/ac/\n/YkLLriAq666ioYNG9K0aVOOPfZYIPR0Ny0tjczMTMyM4cOHAzD3oyr/JxARqZP8rsO7ELiY0PDl\nCcA04E5CMzVf7JxbFFiEIjFg8eLFFfrrv9eHFQjVd1199dUAHHXUUQCsWlc99yISBNVfSW23Z88e\nkpOTSxxLSUlh9+5IFV0hs2bNYtSoUSWuk5KSUuKcnTt3snHjRt8d6o8//pgWLVpw+umnk5qayuDB\ng1m/fn243TlX5jVfZnnfn0gs0+8QCZLvdXidcwudc72AJKA9kOScO8U592Zg0YnEkIr89f9wH1Zu\nueUWnnnmGfLz8/n2228BOPfXwd+DiIiUr1mzZuTk5JQ4lpOTQ1JSUsTXfPjhh2zdupXLL7/c8zp7\n9+4lPj7ed4d6w4YNzJw5k8cff5z169eTnp7OsGHDAOjbty+bNm3ihRdeID8/n2eeeQaA3LyK3a+I\nSH3hu8NbzDmX65zb6JzLDSIgkViUkZFRob/+e31YAbjooot4+eWXadKkCccffzwAJx0T7D2IBEn1\nV1LbdenShfz8fNasWRM+tmLFCrp16xbxNTNnziQzM5PExMTwsW7duvHDDz+wd+8vRbXbtm2ja9eu\nvjvUTZo04bLLLuOkk04iPj6eu+++myVLlrB7925atmzJ/PnzefTRR2nTpg2LFoUG2bVrVelbF6lx\n0Z4rJT09ncTExPBM6lK/VbjDK1JfVeSv/14fVnbs2MEFF1zApEmTyMvLCz/5nf56tdyGSKD0YUVq\nq8TERDIzM7nrrrvIzc3lo48+4tVXXw1PRlXa/v37efHFF0sMZwbo3LkzPXv25J577iEvL4958+ax\ncuVKbrjhBt8d6hNPPJHQvKC/OHT/jDPO4N///jc///wzM2fOBOCULpW+dZGYEa25UsyM119/PTyT\nutRv6vCK+LB48eIK/fXf68PKDz/8QMOGDRk+fDgNGjTgyCOPBOCNzwK8AZGAFddf6cOK1GbTpk0j\nNzeX1q1bM3z4cKZPn85xxx3Hhx9+WOYPL6+88gotWrTgrLPOKnOd559/nk8//ZQWLVoygHc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tomdj1cvv+BlnF6YtfD5fujtTO6JXY9XP4H/vbvJnY9XH62vz0jMeteWnbSj9/KWF+zZg01atRg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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa3c8da50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# TensorRT-fp16 is up to 2.7x faster than cuDNN-fp16.\n",
    "plot_speedup_over_baseline(df_mean_time_per_image, baseline='nvidia-fp16-cudnn', libs_to_drop=[], fontsize=12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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cc889BAYGusoaY0osUbN9+3ZX77977rnH7T0Kk1ZvtE6rf/DW0joGmG+tTQQw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\nND1YRMo6TQ/2DrXXJafY+7QCfyVjf9YhwPvW2rOeCk5EREREREQkP05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3bl0eeuih0ghZ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fQS//3vf3nooYe48MILM+tu3LiRTp060atXLxo3bswpp5xCamoqL730Ep9//jk33HADHTt2PK7X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\nFRERERERkWLr/wP/GOHCRcNLlwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa41e44f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# AlexNet and SqueezeNet 1.1 have very similar performance with cuBLAS and cuDNN. (They also have very similar accuracy!)\n",
    "# SqueezeNet, however, benefits much more from TensorRT optimizations, becoming over 1.5 times faster than AlexNet.\n",
    "# At the same time, SqueezeNet requires about 4 times more memory than AlexNet (at least, with Caffe), so it's a trade-off.\n",
    "plot_speedup_over_baseline(df_mean_time_per_image.ix[['bvlc-alexnet', 'deepscale-squeezenet-1.1']],\n",
    "                           baseline='nvidia-cuda', libs_to_drop=[], fontsize=18)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"compare_with_whitepaper\"></a>\n",
    "### How do dividiti's results compare with NVIDIA's results in the whitepaper?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In Nov/2015, NVIDIA published a [whitepaper](https://www.nvidia.com/content/tegra/embedded-systems/pdf/jetson_tx1_whitepaper.pdf) entitled \"GPU-Based Deep Learning Inference: A Performance and Power Analysis\", which presented the throughput of inference (images per second) on AlexNet and GoogleNet using small and large batch sizes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Several points of difference complicate direct comparison:\n",
    "- TX1 board support package: NVIDIA's is before Nov/2015; dividiti's is Nov/2016. [In a [pinned post](https://devtalk.nvidia.com/default/topic/935300/jetson-tx1/deep-learning-inference-performance-validation-on-tx1/) on NVIDIA's Jetson forum published in May/2016, the following are mentioned: Linux for Tegra 23.1 (vs 24.2.1); CUDA Toolkit 7.0.73 (vs 8.0.33); cuDNN 4 (vs 5.1).]\n",
    "- Caffe source: NVIDIA's is probably a mix of open-source and proprietary, before Nov/2015; dividiti's is public, Nov/2016;\n",
    "- \"small batch size\": NVIDIA's is 1; dividiti's is 2;\n",
    "- \"large batch size\": NVIDIA's is 128 (AlexNet) and 64 (GoogleNet); dividiti's is 16;\n",
    "- GPU clock frequency: NVIDIA's is 690 MHz (presumably to demonstrate better power consumption); dividiti's is 998 MHz (to demonstrate maximum performance)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "nvidia_data = []\n",
    "nvidia_data.append({\n",
    "    'source':'nvidia', 'model':'bvlc-alexnet', 'batch size':1, 'clock':'690 MHz',\n",
    "    'fp32 (images/s)':47, 'fp16 (images/s)':67\n",
    "})\n",
    "nvidia_data.append({\n",
    "    'source':'nvidia', 'model':'bvlc-alexnet', 'batch size':128, 'clock':'690 MHz',\n",
    "    'fp32 (images/s)':155, 'fp16 (images/s)':258 \n",
    "})\n",
    "nvidia_data.append({\n",
    "    'source':'nvidia', 'model':'bvlc-googlenet', 'batch size':1, 'clock':'690 MHz',\n",
    "    'fp32 (images/s)':33, 'fp16 (images/s)':33\n",
    "})\n",
    "nvidia_data.append({       \n",
    "    'source':'nvidia', 'model':'bvlc-googlenet', 'batch size':64, 'clock':'690 MHz',\n",
    "    'fp32 (images/s)':52, 'fp16 (images/s)':75\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "dividiti_data = []\n",
    "dividiti_data.append({\n",
    "    'source':'dividiti', 'model':'bvlc-alexnet', 'batch size':2, 'clock':'998 MHz',\n",
    "    'fp32 (images/s)':(1000/(df_time['bvlc-alexnet','nvidia-cudnn',2].mean()/2)),\n",
    "    'fp16 (images/s)':(1000/(df_time['bvlc-alexnet','nvidia-fp16-cudnn',2].mean()/2)),\n",
    "})\n",
    "dividiti_data.append({\n",
    "    'source':'dividiti', 'model':'bvlc-alexnet', 'batch size':16, 'clock':'998 MHz',\n",
    "    'fp32 (images/s)':(1000/(df_time['bvlc-alexnet','nvidia-cudnn',16].mean()/16)),\n",
    "    'fp16 (images/s)':(1000/(df_time['bvlc-alexnet','nvidia-fp16-cudnn',16].mean()/16)),\n",
    "})\n",
    "dividiti_data.append({\n",
    "    'source':'dividiti', 'model':'bvlc-googlenet', 'batch size':2, 'clock':'998 MHz',\n",
    "    'fp32 (images/s)':(1000/(df_time['bvlc-googlenet','nvidia-cudnn',2].mean()/2)),\n",
    "    'fp16 (images/s)':(1000/(df_time['bvlc-googlenet','nvidia-fp16-cudnn',2].mean()/2)),\n",
    "})\n",
    "dividiti_data.append({\n",
    "    'source':'dividiti', 'model':'bvlc-googlenet', 'batch size':16, 'clock':'998 MHz',\n",
    "    'fp32 (images/s)':(1000/(df_time['bvlc-googlenet','nvidia-cudnn',16].mean()/16)),\n",
    "    'fp16 (images/s)':(1000/(df_time['bvlc-googlenet','nvidia-fp16-cudnn',16].mean()/16)),\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>fp16 (images/s)</th>\n",
       "      <th>fp32 (images/s)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model</th>\n",
       "      <th>batch size</th>\n",
       "      <th>source</th>\n",
       "      <th>clock</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">bvlc-alexnet</th>\n",
       "      <th>1</th>\n",
       "      <th>nvidia</th>\n",
       "      <th>690 MHz</th>\n",
       "      <td>67.000000</td>\n",
       "      <td>47.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <th>dividiti</th>\n",
       "      <th>998 MHz</th>\n",
       "      <td>106.877381</td>\n",
       "      <td>65.283171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <th>dividiti</th>\n",
       "      <th>998 MHz</th>\n",
       "      <td>197.438889</td>\n",
       "      <td>134.891707</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <th>nvidia</th>\n",
       "      <th>690 MHz</th>\n",
       "      <td>258.000000</td>\n",
       "      <td>155.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">bvlc-googlenet</th>\n",
       "      <th>1</th>\n",
       "      <th>nvidia</th>\n",
       "      <th>690 MHz</th>\n",
       "      <td>33.000000</td>\n",
       "      <td>33.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <th>dividiti</th>\n",
       "      <th>998 MHz</th>\n",
       "      <td>67.485718</td>\n",
       "      <td>35.521055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <th>dividiti</th>\n",
       "      <th>998 MHz</th>\n",
       "      <td>89.840365</td>\n",
       "      <td>58.446279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <th>nvidia</th>\n",
       "      <th>690 MHz</th>\n",
       "      <td>75.000000</td>\n",
       "      <td>52.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            fp16 (images/s)  fp32 (images/s)\n",
       "model          batch size source   clock                                    \n",
       "bvlc-alexnet   1          nvidia   690 MHz        67.000000        47.000000\n",
       "               2          dividiti 998 MHz       106.877381        65.283171\n",
       "               16         dividiti 998 MHz       197.438889       134.891707\n",
       "               128        nvidia   690 MHz       258.000000       155.000000\n",
       "bvlc-googlenet 1          nvidia   690 MHz        33.000000        33.000000\n",
       "               2          dividiti 998 MHz        67.485718        35.521055\n",
       "               16         dividiti 998 MHz        89.840365        58.446279\n",
       "               64         nvidia   690 MHz        75.000000        52.000000"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(nvidia_data+dividiti_data).set_index(['model','batch size','source','clock']).sortlevel()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>fp16 (images/s)</th>\n",
       "      <th>fp32 (images/s)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model</th>\n",
       "      <th>batch size</th>\n",
       "      <th>source</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">bvlc-alexnet</th>\n",
       "      <th>1</th>\n",
       "      <th>nvidia</th>\n",
       "      <td>67.000000</td>\n",
       "      <td>47.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <th>dividiti</th>\n",
       "      <td>73.893179</td>\n",
       "      <td>45.135659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <th>dividiti</th>\n",
       "      <td>136.505845</td>\n",
       "      <td>93.261802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <th>nvidia</th>\n",
       "      <td>258.000000</td>\n",
       "      <td>155.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">bvlc-googlenet</th>\n",
       "      <th>1</th>\n",
       "      <th>nvidia</th>\n",
       "      <td>33.000000</td>\n",
       "      <td>33.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <th>dividiti</th>\n",
       "      <td>46.658463</td>\n",
       "      <td>24.558646</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <th>dividiti</th>\n",
       "      <td>62.114080</td>\n",
       "      <td>40.408750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <th>nvidia</th>\n",
       "      <td>75.000000</td>\n",
       "      <td>52.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    fp16 (images/s)  fp32 (images/s)\n",
       "model          batch size source                                    \n",
       "bvlc-alexnet   1          nvidia          67.000000        47.000000\n",
       "               2          dividiti        73.893179        45.135659\n",
       "               16         dividiti       136.505845        93.261802\n",
       "               128        nvidia         258.000000       155.000000\n",
       "bvlc-googlenet 1          nvidia          33.000000        33.000000\n",
       "               2          dividiti        46.658463        24.558646\n",
       "               16         dividiti        62.114080        40.408750\n",
       "               64         nvidia          75.000000        52.000000"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Scale dividiti's data.\n",
    "pd.concat([\n",
    "    pd.DataFrame(nvidia_data).set_index(['model','batch size','source']).drop(labels='clock',axis=1),\n",
    "    pd.DataFrame(dividiti_data).set_index(['model','batch size','source']).drop(labels='clock',axis=1)*(690.0/998.0)\n",
    "]).sortlevel()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With scaling dividiti's data to the lower frequency of 690 MHz, NVIDIA's fp16 performance figures look in order. NVIDIA's fp32 performance figure on GoogleNet using the batch size of 1 (33 images/s) is 50% higher than expected (a copy-and-paste error from the fp16 figure?). NVIDIA's performance figures on AlexNet using the batch size of 128 also seem to be on the high side given the observed trend of improvements tailing off pretty rapidly."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"tx1_vs_myriad2\"></a>\n",
    "### How does TX1 compare to Myriad2?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[According to Movidius](http://uploads.movidius.com/1461814467-Fathom-Combined-2-pager.pdf), using fp16 their Myriad2 processor runs GoogleNet at 15 images per second (~67 ms per image), while consumes about 1 Watt of power."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[According to the NVIDIA 2015 whitepaper](https://www.nvidia.com/content/tegra/embedded-systems/pdf/jetson_tx1_whitepaper.pdf), using fp16 their TX1 processor runs GoogleNet at up to 75 images per second, while consumes up to 6 Watts of power. This would make TX1 and Myriad2 roughly comparable in terms of images per second per unit power: ~13 vs 15 images per second per Watt."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "However, the performance improvements brought by TensorRT (<a href=\"#plot_libs_googlenet_fp16\">\n",
    "~2x vs cuDNN</a>) swing the comparison in favour of NVIDIA even for small batch sizes: ~18-22 images per second per Watt, according to their [blog introducing TensorRT](https://devblogs.nvidia.com/parallelforall/jetpack-doubles-jetson-tx1-deep-learning-inference/blog)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<img src=\"https://devblogs.nvidia.com/parallelforall/wp-content/uploads/2016/09/Figure_3-1.png\"/>"
      ],
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image(url=\"https://devblogs.nvidia.com/parallelforall/wp-content/uploads/2016/09/Figure_3-1.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"cknowledge_ai\"></a>\n",
    "## [cknowledge.org/ai](http://cknowledge.org/ai): Crowdsourcing benchmarking and optimisation of AI"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A suite of open-source tools for [collecting knowledge on optimising AI](http://bit.ly/hipeac49-ckdl):\n",
    "\n",
    "\n",
    "* [Android app](https://play.google.com/store/apps/details?id=openscience.crowdsource.video.experiments&hl=en_GB)\n",
    "* [Desktop app](https://github.com/dividiti/ck-crowdsource-dnn-optimization)\n",
    "* [CK-Caffe](https://github.com/dividiti/ck-caffe)\n",
    "* [CK-TensorRT](https://github.com/dividiti/ck-tensorrt)\n",
    "* [CK-TensorFlow](https://github.com/ctuning/ck-tensorflow)\n",
    "* etc."
   ]
  }
 ],
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