{
 "cells": [
  {
   "cell_type": "markdown",
   "source": [
    "# Huggingface Sagemaker-sdk - Distributed Training Demo\n",
    "### Distributed Question Answering with `transformers` scripts +  `Trainer` and `squad` dataset"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "1. [Introduction](#Introduction)  \n",
    "2. [Development Environment and Permissions](#Development-Environment-and-Permissions)\n",
    "    1. [Installation](#Installation)  \n",
    "    2. [Development environment](#Development-environment)  \n",
    "    3. [Permissions](#Permissions) \n",
    "4. [Fine-tuning & starting Sagemaker Training Job](#Fine-tuning-\\&-starting-Sagemaker-Training-Job)  \n",
    "    1. [Creating an Estimator and start a training job](#Creating-an-Estimator-and-start-a-training-job)  \n",
    "    2. [Estimator Parameters](#Estimator-Parameters)   \n",
    "    3. [Download fine-tuned model from s3](#Download-fine-tuned-model-from-s3)\n",
    "    3. [Attach to old training job to an estimator ](#Attach-to-old-training-job-to-an-estimator)  \n",
    "5. [_Coming soon_:Push model to the Hugging Face hub](#Push-model-to-the-Hugging-Face-hub)"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Introduction\n",
    "\n",
    "Welcome to our end-to-end `distributed` Question-Answering example. In this demo, we will use the Hugging Face `transformers` and `datasets` library together with a custom Amazon sagemaker-sdk extension to fine-tune a pre-trained transformer for question-answering on multiple-gpus. In particular, the pre-trained model will be fine-tuned using the `squad` dataset. The demo will use the new `smdistributed` library to run training on multiple gpus as training scripting we are going to use one of the `transformers` [example scripts from the repository](https://github.com/huggingface/transformers/blob/master/examples/question-answering/run_qa.py).\n",
    "\n",
    "To get started, we need to set up the environment with a few prerequisite steps, for permissions, configurations, and so on. \n",
    "\n",
    "![image-2.png](attachment:image-2.png)\n",
    "\n",
    "\n",
    "_**NOTE: You can run this demo in Sagemaker Studio, your local machine or Sagemaker Notebook Instances**_"
   ],
   "metadata": {},
   "attachments": {
    "image-2.png": {
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"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Development Environment and Permissions "
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Installation\n",
    "\n",
    "_*Note:* we only install the required libraries from Hugging Face and AWS. You also need PyTorch or Tensorflow, if you haven´t it installed_"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "!pip install \"sagemaker>=2.48.0\" \"transformers==4.6.1\" \"datasets[s3]==1.6.2\" --upgrade"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Development environment "
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "**upgrade ipywidgets for `datasets` library and restart kernel, only needed when prerpocessing is done in the notebook**"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "source": [
    "%%capture\n",
    "import IPython\n",
    "!conda install -c conda-forge ipywidgets -y\n",
    "IPython.Application.instance().kernel.do_shutdown(True) # has to restart kernel so changes are used"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "source": [
    "import sagemaker.huggingface"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Permissions"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "_If you are going to use Sagemaker in a local environment. You need access to an IAM Role with the required permissions for Sagemaker. You can find [here](https://docs.aws.amazon.com/sagemaker/latest/dg/sagemaker-roles.html) more about it._"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "import sagemaker\n",
    "\n",
    "sess = sagemaker.Session()\n",
    "# sagemaker session bucket -> used for uploading data, models and logs\n",
    "# sagemaker will automatically create this bucket if it not exists\n",
    "sagemaker_session_bucket=None\n",
    "if sagemaker_session_bucket is None and sess is not None:\n",
    "    # set to default bucket if a bucket name is not given\n",
    "    sagemaker_session_bucket = sess.default_bucket()\n",
    "\n",
    "role = sagemaker.get_execution_role()\n",
    "sess = sagemaker.Session(default_bucket=sagemaker_session_bucket)\n",
    "\n",
    "print(f\"sagemaker role arn: {role}\")\n",
    "print(f\"sagemaker bucket: {sess.default_bucket()}\")\n",
    "print(f\"sagemaker session region: {sess.boto_region_name}\")"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Fine-tuning & starting Sagemaker Training Job\n",
    "\n",
    "In order to create a sagemaker training job we need an `HuggingFace` Estimator. The Estimator handles end-to-end Amazon SageMaker training and deployment tasks. In a Estimator we define, which fine-tuning script should be used as `entry_point`, which `instance_type` should be used, which `hyperparameters` are passed in .....\n",
    "\n",
    "\n",
    "\n",
    "```python\n",
    "huggingface_estimator = HuggingFace(entry_point='train.py',\n",
    "                            source_dir='./scripts',\n",
    "                            sagemaker_session=sess,\n",
    "                            base_job_name='huggingface-sdk-extension',\n",
    "                            instance_type='ml.p3.2xlarge',\n",
    "                            instance_count=1,\n",
    "                            transformers_version='4.4',\n",
    "                            pytorch_version='1.6.0',\n",
    "                            py_version='py36',\n",
    "                            role=role,\n",
    "                            hyperparameters = {'epochs': 1,\n",
    "                                               'train_batch_size': 32,\n",
    "                                               'model_name':'distilbert-base-uncased'\n",
    "                                                })\n",
    "```\n",
    "\n",
    "When we create a SageMaker training job, SageMaker takes care of starting and managing all the required ec2 instances for us with the `huggingface` container, uploads the provided fine-tuning script `train.py` and downloads the data from our `sagemaker_session_bucket` into the container at `/opt/ml/input/data`. Then, it starts the training job by running. \n",
    "\n",
    "```python\n",
    "/opt/conda/bin/python train.py --epochs 1 --model_name distilbert-base-uncased --train_batch_size 32\n",
    "```\n",
    "\n",
    "The `hyperparameters` you define in the `HuggingFace` estimator are passed in as named arguments. \n",
    "\n",
    "Sagemaker is providing useful properties about the training environment through various environment variables, including the following:\n",
    "\n",
    "* `SM_MODEL_DIR`: A string that represents the path where the training job writes the model artifacts to. After training, artifacts in this directory are uploaded to S3 for model hosting.\n",
    "\n",
    "* `SM_NUM_GPUS`: An integer representing the number of GPUs available to the host.\n",
    "\n",
    "* `SM_CHANNEL_XXXX:` A string that represents the path to the directory that contains the input data for the specified channel. For example, if you specify two input channels in the HuggingFace estimator’s fit call, named `train` and `test`, the environment variables `SM_CHANNEL_TRAIN` and `SM_CHANNEL_TEST` are set.\n",
    "\n",
    "\n",
    "To run your training job locally you can define `instance_type='local'` or `instance_type='local_gpu'` for gpu usage. _Note: this does not working within SageMaker Studio_\n"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Creating an Estimator and start a training job\n",
    "\n",
    "In this example we are going to use the capability to download/use a fine-tuning script from a `git`- repository. We are using the `run_qa.py` from the `transformers` example scripts. You can find the code [here](https://github.com/huggingface/transformers/tree/master/examples/question-answering)."
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "source": [
    "from sagemaker.huggingface import HuggingFace\n",
    "\n",
    "# hyperparameters, which are passed into the training job\n",
    "hyperparameters={\n",
    "    'model_name_or_path': 'bert-large-uncased-whole-word-masking',\n",
    "    'dataset_name':'squad',\n",
    "    'do_train': True,\n",
    "    'do_eval': True,\n",
    "    'fp16': True,\n",
    "    'per_device_train_batch_size': 4,\n",
    "    'per_device_eval_batch_size': 4,\n",
    "    'num_train_epochs': 2,\n",
    "    'max_seq_length': 384,\n",
    "    'max_steps': 100,\n",
    "    'pad_to_max_length': True,\n",
    "    'doc_stride': 128,\n",
    "    'output_dir': '/opt/ml/model'\n",
    "}\n",
    "\n",
    "# configuration for running training on smdistributed Data Parallel\n",
    "distribution = {'smdistributed':{'dataparallel':{ 'enabled': True }}}\n",
    "\n",
    "# git configuration to download our fine-tuning script\n",
    "git_config = {'repo': 'https://github.com/huggingface/transformers.git','branch': 'v4.6.1'}\n",
    "\n",
    "# instance configurations\n",
    "instance_type='ml.p3.16xlarge'\n",
    "instance_count=2\n",
    "volume_size=200\n",
    "\n",
    "# metric definition to extract the results\n",
    "metric_definitions=[\n",
    "     {\"Name\": \"train_runtime\", \"Regex\": \"train_runtime.*=\\D*(.*?)$\"},\n",
    "     {'Name': 'train_samples_per_second', 'Regex': \"train_samples_per_second.*=\\D*(.*?)$\"},\n",
    "     {'Name': 'epoch', 'Regex': \"epoch.*=\\D*(.*?)$\"},\n",
    "     {'Name': 'f1', 'Regex': \"f1.*=\\D*(.*?)$\"},\n",
    "     {'Name': 'exact_match', 'Regex': \"exact_match.*=\\D*(.*?)$\"}]"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "# estimator\n",
    "huggingface_estimator = HuggingFace(entry_point='run_qa.py',\n",
    "                                    source_dir='./examples/pytorch/question-answering',\n",
    "                                    git_config=git_config,\n",
    "                                    metric_definitions=metric_definitions,\n",
    "                                    instance_type=instance_type,\n",
    "                                    instance_count=instance_count,\n",
    "                                    volume_size=volume_size,\n",
    "                                    role=role,\n",
    "                                    transformers_version='4.6',\n",
    "                                    pytorch_version='1.7',\n",
    "                                    py_version='py36',\n",
    "                                    distribution= distribution,\n",
    "                                    hyperparameters = hyperparameters)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "# starting the train job\n",
    "huggingface_estimator.fit()"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Deploying the endpoint\n",
    "\n",
    "To deploy our endpoint, we call `deploy()` on our HuggingFace estimator object, passing in our desired number of instances and instance type."
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "predictor = huggingface_estimator.deploy(1,\"ml.g4dn.xlarge\")"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "Then, we use the returned predictor object to call the endpoint."
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "data = {\n",
    "\"inputs\": {\n",
    "\t\"question\": \"What is used for inference?\",\n",
    "\t\"context\": \"My Name is Philipp and I live in Nuremberg. This model is used with sagemaker for inference.\"\n",
    "\t}\n",
    "}\n",
    "predictor.predict(data)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "Finally, we delete the endpoint again."
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "source": [
    "predictor.delete_endpoint()"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Estimator Parameters"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "source": [
    "# container image used for training job\n",
    "print(f\"container image used for training job: \\n{huggingface_estimator.image_uri}\\n\")\n",
    "\n",
    "# s3 uri where the trained model is located\n",
    "print(f\"s3 uri where the trained model is located: \\n{huggingface_estimator.model_data}\\n\")\n",
    "\n",
    "# latest training job name for this estimator\n",
    "print(f\"latest training job name for this estimator: \\n{huggingface_estimator.latest_training_job.name}\\n\")\n",
    "\n"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "container image used for training job: \n",
      "558105141721.dkr.ecr.us-east-1.amazonaws.com/huggingface-training:pytorch1.6.0-transformers4.3.0-tokenizers0.9.4-datasets1.2.1-py36-gpu-cu110\n",
      "\n",
      "s3 uri where the trained model is located: \n",
      "s3://philipps-sagemaker-bucket-us-east-1/huggingface-training-2021-02-04-20-17-42-373/output/model.tar.gz\n",
      "\n",
      "latest training job name for this estimator: \n",
      "huggingface-training-2021-02-04-20-17-42-373\n",
      "\n"
     ]
    }
   ],
   "metadata": {
    "scrolled": true
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "# access the logs of the training job\n",
    "huggingface_estimator.sagemaker_session.logs_for_job(huggingface_estimator.latest_training_job.name)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Attach to old training job to an estimator \n",
    "\n",
    "In Sagemaker you can attach an old training job to an estimator to continue training, get results etc.."
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "from sagemaker.estimator import Estimator\n",
    "\n",
    "# job which is going to be attached to the estimator\n",
    "old_training_job_name=''"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "# attach old training job\n",
    "huggingface_estimator_loaded = Estimator.attach(old_training_job_name)\n",
    "\n",
    "# get model output s3 from training job\n",
    "huggingface_estimator_loaded.model_data"
   ],
   "outputs": [],
   "metadata": {
    "scrolled": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [],
   "outputs": [],
   "metadata": {}
  }
 ],
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