287 lines
27 KiB
Plaintext
287 lines
27 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {
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"collapsed": false,
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"deletable": true,
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"editable": true
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},
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"outputs": [],
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"source": [
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"import matplotlib\n",
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"%matplotlib inline\n",
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"matplotlib.rcParams['figure.figsize'] = (20, 10)\n",
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"matplotlib.rcParams['font.size'] = 24\n",
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"matplotlib.rcParams['lines.linewidth'] = 5\n",
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"matplotlib.rcParams['lines.markersize'] = 20\n",
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"\n",
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"import tensorflow as tf\n",
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"from tensorflow.python.client import device_lib\n",
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"\n",
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"import time\n",
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"import numpy as np\n",
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"from matplotlib import pyplot\n",
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"from collections import defaultdict\n",
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"from ipy_table import make_table, set_row_style, set_column_style\n",
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"from jupyter_progressbar import ProgressBar"
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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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"deletable": true,
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"editable": true
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},
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"source": [
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"# Devices\n",
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"\n",
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"lists the devices found by tensorflow"
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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": false,
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"deletable": true,
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"editable": true
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},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<table border=\"1\" cellpadding=\"3\" cellspacing=\"0\" style=\"border:black; border-collapse:collapse;\"><tr><td style=\"border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\"><b>name</b></td><td style=\"border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\"><b>type</b></td></tr><tr><td style=\"border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">/device:CPU:0</td><td style=\"border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">CPU</td></tr><tr><td style=\"border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">/device:GPU:0</td><td style=\"border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">GPU</td></tr></table>"
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],
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"text/plain": [
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"<ipy_table.ipy_table.IpyTable at 0x7f58b3ab14a8>"
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]
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},
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"execution_count": 2,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"devices = device_lib.list_local_devices()\n",
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"\n",
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"make_table([[\"name\", \"type\"]] + [\n",
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" [device.name, device.device_type]\n",
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" for device in devices\n",
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"])\n",
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"set_row_style(0, bold=True)"
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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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"deletable": true,
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"editable": true
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},
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"source": [
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"# Use all devices once such that they are initialized"
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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": false,
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"deletable": true,
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"editable": true
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},
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"outputs": [],
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"source": [
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"for device in devices:\n",
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" with tf.device(device.name):\n",
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" c = tf.constant(5.0)\n",
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" x = tf.placeholder(tf.float32)\n",
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" product = c * x\n",
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"\n",
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" with tf.Session(config=tf.ConfigProto(log_device_placement=True)) as session:\n",
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" result = session.run(product, feed_dict={x: 5.0})\n",
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" assert result == 25.0, \"c * x for x == 5.0 should result in 25.0, but resulted in {} on device {} ({})\".format(\n",
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" result, device.name, device.device_type)"
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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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"source": [
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"# Benchmark"
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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": 4,
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"metadata": {
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"collapsed": false,
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"deletable": true,
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"editable": true
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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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"start /device:CPU:0 10\n",
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"start /device:GPU:0 10\n",
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"start /device:CPU:0 20\n",
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"start /device:GPU:0 20\n",
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"start /device:CPU:0 50\n",
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"start /device:GPU:0 50\n",
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"start /device:CPU:0 100\n",
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"start /device:GPU:0 100\n",
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"start /device:CPU:0 1000\n",
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"start /device:GPU:0 1000\n",
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"start /device:CPU:0 5000\n",
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"start /device:GPU:0 5000\n",
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"start /device:CPU:0 10000\n",
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"start /device:GPU:0 10000\n"
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]
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}
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],
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"source": [
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"results = defaultdict(list)\n",
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"\n",
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"matrix_sizes = list(map(int, [10, 20, 50, 100, 1000, 5000, 1e4]))\n",
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"\n",
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"for size in ProgressBar(matrix_sizes):\n",
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" for device in devices:\n",
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" print('start', device.name, size)\n",
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" with tf.device(device.name):\n",
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" A = tf.random_uniform(shape=(size, size), minval=0, maxval=1, dtype=tf.float32)\n",
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" B = tf.random_uniform(shape=(size, size), minval=0, maxval=1, dtype=tf.float32)\n",
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" product = tf.matmul(A, B)\n",
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" \n",
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" with tf.Session(config=tf.ConfigProto(log_device_placement=True)) as session:\n",
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" t0 = time.time()\n",
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" result = session.run(product)\n",
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" t1 = time.time()\n",
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" results[device.name].append(t1 - t0)"
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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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"source": [
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"# Results"
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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": 5,
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"metadata": {
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"collapsed": false,
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"deletable": true,
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"editable": true
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
|
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"<matplotlib.text.Text at 0x7f58b018b710>"
|
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]
|
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},
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"execution_count": 5,
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"metadata": {},
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"output_type": "execute_result"
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},
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{
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"data": {
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"image/png": 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\n",
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"text/plain": [
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"<matplotlib.figure.Figure at 0x7f58b025e390>"
|
|
]
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|
},
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"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"colors = 'rgbcmyk'\n",
|
|
"marks = ['-', '--', '..']\n",
|
|
"\n",
|
|
"styles = (color+mark for mark in marks for color in colors)\n",
|
|
"\n",
|
|
"for style, (name, times) in zip(styles, results.items()):\n",
|
|
" pyplot.plot(matrix_sizes, times, style, label=name)\n",
|
|
"\n",
|
|
"pyplot.legend()\n",
|
|
"\n",
|
|
"pyplot.ylabel('Calculation time')\n",
|
|
"pyplot.xlabel('Matrix size')"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 6,
|
|
"metadata": {
|
|
"collapsed": false,
|
|
"deletable": true,
|
|
"editable": true
|
|
},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/html": [
|
|
"<table border=\"1\" cellpadding=\"3\" cellspacing=\"0\" style=\"border:black; border-collapse:collapse;\"><tr><td style=\"border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;text-align:right;\"><b>matrix size</b></td><td style=\"border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">time (s) on <b>/device:CPU:0</b></td><td style=\"border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">time (s) on <b>/device:GPU:0</b></td></tr><tr><td style=\"border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;text-align:right;\"><b>10</b></td><td style=\"border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">0.0054</td><td style=\"border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">0.1705</td></tr><tr><td style=\"border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;text-align:right;\"><b>20</b></td><td style=\"border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">0.0045</td><td style=\"border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">0.0059</td></tr><tr><td style=\"border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;text-align:right;\"><b>50</b></td><td style=\"border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">0.0053</td><td style=\"border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">0.0081</td></tr><tr><td style=\"border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;text-align:right;\"><b>100</b></td><td style=\"border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">0.0103</td><td style=\"border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">0.0081</td></tr><tr><td style=\"border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;text-align:right;\"><b>1000</b></td><td style=\"border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">0.0437</td><td style=\"border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">0.0106</td></tr><tr><td style=\"border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;text-align:right;\"><b>5000</b></td><td style=\"border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">2.8766</td><td style=\"border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">0.2573</td></tr><tr><td style=\"border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;text-align:right;\"><b>10000</b></td><td style=\"border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">22.4699</td><td style=\"border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">1.6363</td></tr></table>"
|
|
],
|
|
"text/plain": [
|
|
"<ipy_table.ipy_table.IpyTable at 0x7f58b021afd0>"
|
|
]
|
|
},
|
|
"execution_count": 6,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"devices = device_lib.list_local_devices()\n",
|
|
"\n",
|
|
"\n",
|
|
"make_table([[\"matrix size\"] + [\"time (s) on <b>{}</b>\".format(device.name) for device in devices]] + [\n",
|
|
" [size] + [results[device.name][size_index] for device in devices]\n",
|
|
" for size_index, size in enumerate(matrix_sizes)\n",
|
|
"])\n",
|
|
"set_column_style(0, bold=True, align='right')"
|
|
]
|
|
}
|
|
],
|
|
"metadata": {
|
|
"hide_input": false,
|
|
"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": "3.5.2"
|
|
},
|
|
"widgets": {
|
|
"state": {
|
|
"7ba735bb37af497cac93b37f587e1d16": {
|
|
"views": [
|
|
{
|
|
"cell_index": 5
|
|
}
|
|
]
|
|
}
|
|
},
|
|
"version": "1.2.0"
|
|
}
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|
},
|
|
"nbformat": 4,
|
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"nbformat_minor": 2
|
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}
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