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Add TensorFlow multi-gpu computation notebook.
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‎README.md

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@@ -102,6 +102,7 @@ IPython Notebook(s) demonstrating deep learning functionality.
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| [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. |
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| [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. |
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| [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. |
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| [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. |
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### tensor-flow-exercises
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‎deep-learning/tensor-flow-examples/notebooks/4_multi_gpu/multigpu_basics.ipynb

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

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