Add TensorFlow multi-gpu computation notebook.

README.md

@@ -102,6 +102,7 @@ IPython Notebook(s) demonstrating deep learning functionality.
 | [tsf-cnn](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/deep-learning/tensor-flow-examples/3_neural_networks/convolutional_network.ipynb) | Implement convolutional neural networks in TensorFlow. |
 | [tsf-mlp](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/deep-learning/tensor-flow-examples/3_neural_networks/multilayer_perceptron.ipynb) | Implement multilayer perceptrons in TensorFlow. |
 | [tsf-rnn](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/deep-learning/tensor-flow-examples/3_neural_networks/recurrent_network.ipynb) | Implement recurrent neural networks in TensorFlow. |
+| [tsf-gpu](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/deep-learning/tensor-flow-examples/4_multi_gpu/multigpu_basics.ipynb) | Learn about basic multi-GPU computation in TensorFlow. |
 
 ### tensor-flow-exercises
 

deep-learning/tensor-flow-examples/notebooks/4_multi_gpu/multigpu_basics.ipynb

@@ -0,0 +1,184 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Basic Multi GPU Computation in TensorFlow\n",
+    "\n",
+    "Credits: Forked from [TensorFlow-Examples](https://github.com/aymericdamien/TensorFlow-Examples) by Aymeric Damien\n",
+    "\n",
+    "## Setup\n",
+    "\n",
+    "Refer to the [setup instructions](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/deep-learning/tensor-flow-examples/Setup_TensorFlow.md)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "This tutorial requires your machine to have 2 GPUs\n",
+    "* \"/cpu:0\": The CPU of your machine.\n",
+    "* \"/gpu:0\": The first GPU of your machine\n",
+    "* \"/gpu:1\": The second GPU of your machine\n",
+    "* For this example, we are using 2 GTX-980"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import tensorflow as tf\n",
+    "import datetime"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "#Processing Units logs\n",
+    "log_device_placement = True\n",
+    "\n",
+    "#num of multiplications to perform\n",
+    "n = 10"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# Example: compute A^n + B^n on 2 GPUs\n",
+    "\n",
+    "# Create random large matrix\n",
+    "A = np.random.rand(1e4, 1e4).astype('float32')\n",
+    "B = np.random.rand(1e4, 1e4).astype('float32')\n",
+    "\n",
+    "# Creates a graph to store results\n",
+    "c1 = []\n",
+    "c2 = []\n",
+    "\n",
+    "# Define matrix power\n",
+    "def matpow(M, n):\n",
+    "    if n < 1: #Abstract cases where n < 1\n",
+    "        return M\n",
+    "    else:\n",
+    "        return tf.matmul(M, matpow(M, n-1))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# Single GPU computing\n",
+    "\n",
+    "with tf.device('/gpu:0'):\n",
+    "    a = tf.constant(A)\n",
+    "    b = tf.constant(B)\n",
+    "    #compute A^n and B^n and store results in c1\n",
+    "    c1.append(matpow(a, n))\n",
+    "    c1.append(matpow(b, n))\n",
+    "\n",
+    "with tf.device('/cpu:0'):\n",
+    "  sum = tf.add_n(c1) #Addition of all elements in c1, i.e. A^n + B^n\n",
+    "\n",
+    "t1_1 = datetime.datetime.now()\n",
+    "with tf.Session(config=tf.ConfigProto(log_device_placement=log_device_placement)) as sess:\n",
+    "    # Runs the op.\n",
+    "    sess.run(sum)\n",
+    "t2_1 = datetime.datetime.now()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# Multi GPU computing\n",
+    "# GPU:0 computes A^n\n",
+    "with tf.device('/gpu:0'):\n",
+    "    #compute A^n and store result in c2\n",
+    "    a = tf.constant(A)\n",
+    "    c2.append(matpow(a, n))\n",
+    "\n",
+    "#GPU:1 computes B^n\n",
+    "with tf.device('/gpu:1'):\n",
+    "    #compute B^n and store result in c2\n",
+    "    b = tf.constant(B)\n",
+    "    c2.append(matpow(b, n))\n",
+    "\n",
+    "with tf.device('/cpu:0'):\n",
+    "  sum = tf.add_n(c2) #Addition of all elements in c2, i.e. A^n + B^n\n",
+    "\n",
+    "t1_2 = datetime.datetime.now()\n",
+    "with tf.Session(config=tf.ConfigProto(log_device_placement=log_device_placement)) as sess:\n",
+    "    # Runs the op.\n",
+    "    sess.run(sum)\n",
+    "t2_2 = datetime.datetime.now()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Single GPU computation time: 0:00:11.833497\n",
+      "Multi GPU computation time: 0:00:07.085913\n"
+     ]
+    }
+   ],
+   "source": [
+    "print \"Single GPU computation time: \" + str(t2_1-t1_1)\n",
+    "print \"Multi GPU computation time: \" + str(t2_2-t1_2)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.4.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}