TensorFlow_with_GPU.ipynb

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      "source": [
        "# Tensorflow with GPU\n",
        "\n",
        "This notebook provides an introduction to computing on a [GPU](https://cloud.google.com/gpu) in Colab. In this notebook you will connect to a GPU, and then run some basic TensorFlow operations on both the CPU and a GPU, observing the speedup provided by using the GPU.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oM_8ELnJq_wd"
      },
      "source": [
        "## Enabling and testing the GPU\n",
        "\n",
        "First, you'll need to enable GPUs for the notebook:\n",
        "\n",
        "- Navigate to Edit→Notebook Settings\n",
        "- select GPU from the Hardware Accelerator drop-down\n",
        "\n",
        "Next, we'll confirm that we can connect to the GPU with tensorflow:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "id": "sXnDmXR7RDr2",
        "outputId": "230c8c7a-13fd-46b3-c5b1-9c6330cfd846"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "TensorFlow 2.x selected.\n",
            "Found GPU at: /device:GPU:0\n"
          ]
        }
      ],
      "source": [
        "import tensorflow as tf\n",
        "device_name = tf.test.gpu_device_name()\n",
        "if device_name != '/device:GPU:0':\n",
        "  raise SystemError('GPU device not found')\n",
        "print('Found GPU at: {}'.format(device_name))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "v3fE7KmKRDsH"
      },
      "source": [
        "## Observe TensorFlow speedup on GPU relative to CPU\n",
        "\n",
        "This example constructs a typical convolutional neural network layer over a\n",
        "random image and manually places the resulting ops on either the CPU or the GPU\n",
        "to compare execution speed."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 139
        },
        "id": "Y04m-jvKRDsJ",
        "outputId": "bb32a619-b9f8-4866-fe6a-40d8c5341e7e"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Time (s) to convolve 32x7x7x3 filter over random 100x100x100x3 images (batch x height x width x channel). Sum of ten runs.\n",
            "CPU (s):\n",
            "3.862475891000031\n",
            "GPU (s):\n",
            "0.10837535100017703\n",
            "GPU speedup over CPU: 35x\n"
          ]
        }
      ],
      "source": [
        "import tensorflow as tf\n",
        "import timeit\n",
        "\n",
        "device_name = tf.test.gpu_device_name()\n",
        "if device_name != '/device:GPU:0':\n",
        "  print(\n",
        "      '\\n\\nThis error most likely means that this notebook is not '\n",
        "      'configured to use a GPU.  Change this in Notebook Settings via the '\n",
        "      'command palette (cmd/ctrl-shift-P) or the Edit menu.\\n\\n')\n",
        "  raise SystemError('GPU device not found')\n",
        "\n",
        "def cpu():\n",
        "  with tf.device('/cpu:0'):\n",
        "    random_image_cpu = tf.random.normal((100, 100, 100, 3))\n",
        "    net_cpu = tf.keras.layers.Conv2D(32, 7)(random_image_cpu)\n",
        "    return tf.math.reduce_sum(net_cpu)\n",
        "\n",
        "def gpu():\n",
        "  with tf.device('/device:GPU:0'):\n",
        "    random_image_gpu = tf.random.normal((100, 100, 100, 3))\n",
        "    net_gpu = tf.keras.layers.Conv2D(32, 7)(random_image_gpu)\n",
        "    return tf.math.reduce_sum(net_gpu)\n",
        "\n",
        "# We run each op once to warm up; see: https://stackoverflow.com/a/45067900\n",
        "cpu()\n",
        "gpu()\n",
        "\n",
        "# Run the op several times.\n",
        "print('Time (s) to convolve 32x7x7x3 filter over random 100x100x100x3 images '\n",
        "      '(batch x height x width x channel). Sum of ten runs.')\n",
        "print('CPU (s):')\n",
        "cpu_time = timeit.timeit('cpu()', number=10, setup=\"from __main__ import cpu\")\n",
        "print(cpu_time)\n",
        "print('GPU (s):')\n",
        "gpu_time = timeit.timeit('gpu()', number=10, setup=\"from __main__ import gpu\")\n",
        "print(gpu_time)\n",
        "print('GPU speedup over CPU: {}x'.format(int(cpu_time/gpu_time)))"
      ]
    }
  ],
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      "name": "TensorFlow with GPU",
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