Forked from analyticsindiamagazine/Nvidia 1060 matmul.ipynb
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May 8, 2019 22:20
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{ | |
"cells": [ | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"# Tensorflow MatMul - GeForce Nvidia 1060 6GB" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 1, | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"name": "stdout", | |
"output_type": "stream", | |
"text": [ | |
"[name: \"/device:CPU:0\"\n", | |
"device_type: \"CPU\"\n", | |
"memory_limit: 268435456\n", | |
"locality {\n", | |
"}\n", | |
"incarnation: 11791682687431765861\n", | |
", name: \"/device:GPU:0\"\n", | |
"device_type: \"GPU\"\n", | |
"memory_limit: 4971180851\n", | |
"locality {\n", | |
" bus_id: 1\n", | |
" links {\n", | |
" }\n", | |
"}\n", | |
"incarnation: 10419784556444299121\n", | |
"physical_device_desc: \"device: 0, name: GeForce GTX 1060 6GB, pci bus id: 0000:01:00.0, compute capability: 6.1\"\n", | |
"]\n" | |
] | |
} | |
], | |
"source": [ | |
"from tensorflow.python.client import device_lib\n", | |
"print(device_lib.list_local_devices())" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 2, | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"name": "stdout", | |
"output_type": "stream", | |
"text": [ | |
"\n", | |
" 8000 x 8000 matmul took: 0.29 sec, 3588.87 G ops/sec\n" | |
] | |
} | |
], | |
"source": [ | |
"import os\n", | |
"import sys\n", | |
"os.environ[\"CUDA_VISIBLE_DEVICES\"]=\"1\"\n", | |
"import tensorflow as tf\n", | |
"import time\n", | |
"\n", | |
"n = 8000\n", | |
"dtype = tf.float32\n", | |
"with tf.device(\"/GPU:0\"):\n", | |
" matrix1 = tf.Variable(tf.ones((n, n), dtype=dtype))\n", | |
" matrix2 = tf.Variable(tf.ones((n, n), dtype=dtype))\n", | |
" product = tf.matmul(matrix1, matrix2)\n", | |
"\n", | |
"config = tf.ConfigProto(graph_options=tf.GraphOptions(optimizer_options=tf.OptimizerOptions(opt_level=tf.OptimizerOptions.L0)))\n", | |
"\n", | |
"with tf.Session(config=config) as sess1:\n", | |
" sess1.run(tf.global_variables_initializer())\n", | |
" iters = 10\n", | |
" start = time.time()\n", | |
" for i in range(iters):\n", | |
" sess1.run(product) \n", | |
"end = time.time()\n", | |
"ops = n**3 + (n-1)*n**2 # n^2*(n-1) additions, n^3 multiplications\n", | |
"elapsed = (end - start)\n", | |
"rate = iters*ops/elapsed/10**9\n", | |
"print('\\n %d x %d matmul took: %.2f sec, %.2f G ops/sec' % (n, n,\n", | |
" elapsed/iters,\n", | |
" rate,))" | |
] | |
} | |
], | |
"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": "3.6.3" | |
} | |
}, | |
"nbformat": 4, | |
"nbformat_minor": 2 | |
} |
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