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TensorFlow_with_GPU.ipynb

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+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "name": "TensorFlow with GPU",
+      "version": "0.3.2",
+      "provenance": [],
+      "collapsed_sections": [],
+      "toc_visible": true,
+      "include_colab_link": true
+    },
+    "kernelspec": {
+      "display_name": "Python 2",
+      "name": "python2"
+    },
+    "accelerator": "GPU"
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "[View in Colaboratory](https://colab.research.google.com/github/dwcar49us/tutorials/blob/master/TensorFlow_with_GPU.ipynb)"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "BlmQIFSLZDdc",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "# Confirm TensorFlow can see the GPU\n",
+        "\n",
+        "Simply select \"GPU\" in the Accelerator drop-down in Notebook Settings (either through the Edit menu or the command palette at cmd/ctrl-shift-P)."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "3IEVK-KFxi5Z",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 34
+        },
+        "outputId": "2aed0996-09b4-4758-9a19-15ed20af1153"
+      },
+      "cell_type": "code",
+      "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))"
+      ],
+      "execution_count": 2,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Found GPU at: /device:GPU:0\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "QXRh0DPiZRyG",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "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."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "t9ALbbpmY9rm",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 119
+        },
+        "outputId": "d84c9f11-fa31-4656-8425-b7034cdf6227"
+      },
+      "cell_type": "code",
+      "source": [
+        "import tensorflow as tf\n",
+        "import timeit\n",
+        "\n",
+        "# See https://www.tensorflow.org/tutorials/using_gpu#allowing_gpu_memory_growth\n",
+        "config = tf.ConfigProto()\n",
+        "config.gpu_options.allow_growth = True\n",
+        "\n",
+        "with tf.device('/cpu:0'):\n",
+        "  random_image_cpu = tf.random_normal((100, 100, 100, 3))\n",
+        "  net_cpu = tf.layers.conv2d(random_image_cpu, 32, 7)\n",
+        "  net_cpu = tf.reduce_sum(net_cpu)\n",
+        "\n",
+        "with tf.device('/gpu:0'):\n",
+        "  random_image_gpu = tf.random_normal((100, 100, 100, 3))\n",
+        "  net_gpu = tf.layers.conv2d(random_image_gpu, 32, 7)\n",
+        "  net_gpu = tf.reduce_sum(net_gpu)\n",
+        "\n",
+        "sess = tf.Session(config=config)\n",
+        "\n",
+        "# Test execution once to detect errors early.\n",
+        "try:\n",
+        "  sess.run(tf.global_variables_initializer())\n",
+        "except tf.errors.InvalidArgumentError:\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\n",
+        "\n",
+        "def cpu():\n",
+        "  sess.run(net_cpu)\n",
+        "  \n",
+        "def gpu():\n",
+        "  sess.run(net_gpu)\n",
+        "  \n",
+        "# Runs 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)))\n",
+        "\n",
+        "sess.close()"
+      ],
+      "execution_count": 1,
+      "outputs": [
+        {
+          "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",
+            "10.0379359722\n",
+            "GPU (s):\n",
+            "0.98605799675\n",
+            "GPU speedup over CPU: 10x\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    }
+  ]
+}