{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "CPU_GPU_tensorflow.ipynb",
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/raqueeb/TensorFlow2/blob/master/CPU_GPU_tensorflow.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0_vRX29fPmEM",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from __future__ import absolute_import, division, print_function, unicode_literals\n",
        "import tensorflow as tf"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d_j-EXTFQyqN",
        "colab_type": "code",
        "outputId": "33bc5997-c07f-44bc-8a21-4141add9cc75",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 87
        }
      },
      "source": [
        "tf.config.experimental.list_physical_devices()"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[PhysicalDevice(name='/physical_device:CPU:0', device_type='CPU'),\n",
              " PhysicalDevice(name='/physical_device:XLA_CPU:0', device_type='XLA_CPU'),\n",
              " PhysicalDevice(name='/physical_device:XLA_GPU:0', device_type='XLA_GPU'),\n",
              " PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uQ3DeAzfRZba",
        "colab_type": "code",
        "outputId": "277de6e5-e5dd-4642-8fe6-41f44cecc68a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "len(tf.config.experimental.list_physical_devices('GPU'))"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oTTpI27rSUPN",
        "colab_type": "code",
        "outputId": "02ece91e-897c-4e9a-ce3e-499ccd8701d0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "tf.debugging.set_log_device_placement(True)\n",
        "\n",
        "# Place tensors on the CPU\n",
        "with tf.device('/CPU:0'):\n",
        "  a = tf.constant([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]])\n",
        "  b = tf.constant([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]])\n",
        "\n",
        "c = tf.matmul(a, b)\n",
        "print(c)"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Tensor(\"MatMul_1:0\", shape=(2, 2), dtype=float32)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qiCeKR4HR1OM",
        "colab_type": "code",
        "outputId": "47011f4d-6e59-4be4-9652-f42da1cc8c43",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "gpus = tf.config.experimental.list_physical_devices('GPU')\n",
        "if gpus:\n",
        "  # Create 2 virtual GPUs with 512MB memory each\n",
        "  try:\n",
        "    tf.config.experimental.set_virtual_device_configuration(\n",
        "        gpus[0],\n",
        "        [tf.config.experimental.VirtualDeviceConfiguration(memory_limit=512),\n",
        "         tf.config.experimental.VirtualDeviceConfiguration(memory_limit=512)])\n",
        "    logical_gpus = tf.config.experimental.list_logical_devices('GPU')\n",
        "    print(len(gpus), \"Physical GPU,\", len(logical_gpus), \"Logical GPUs\")\n",
        "  except RuntimeError as e:\n",
        "    # Virtual devices must be set before GPUs have been initialized\n",
        "    print(e)"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1 Physical GPU, 2 Logical GPUs\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JjNGiv2STVjO",
        "colab_type": "code",
        "outputId": "2858de66-d5b1-432b-fc27-41c4ce8e3de1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 87
        }
      },
      "source": [
        "import time\n",
        "\n",
        "cpu_slot = 0\n",
        "gpu_slot = 0\n",
        "\n",
        "# Using CPU at slot 0\n",
        "with tf.device('/CPU:' + str(cpu_slot)):\n",
        "    # Starting a timer\n",
        "    start = time.time()\n",
        "\n",
        "    # Doing operations on CPU\n",
        "    A = tf.constant([[3, 2], [5, 2]])\n",
        "    print(tf.eye(2,2))\n",
        "\n",
        "    # Printing how long it took with CPU\n",
        "    end = time.time() - start\n",
        "    print(end)\n",
        "\n",
        "# Using the GPU at slot 0\n",
        "with tf.device('/GPU:' + str(gpu_slot)):\n",
        "    # Starting a timer\n",
        "    start = time.time()\n",
        "\n",
        "    # Doing operations on GPU\n",
        "    A = tf.constant([[3, 2], [5, 2]])\n",
        "    print(tf.eye(2,2))\n",
        "\n",
        "    # Printing how long it took with GPU\n",
        "    end = time.time() - start\n",
        "    print(end)"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Tensor(\"eye_2/diag:0\", shape=(2, 2), dtype=float32, device=/device:CPU:0)\n",
            "0.002489328384399414\n",
            "Tensor(\"eye_3/diag:0\", shape=(2, 2), dtype=float32, device=/device:GPU:0)\n",
            "0.0018601417541503906\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}