Created
November 19, 2018 18:09
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Script for comparing performance of CPU & GPU on a simple MLP
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| #!/usr/bin/env python3 | |
| # (this script also requires tensorflow-gpu to be installed & working) | |
| # increase hidden size/batch size to close gap between CPU and GPU | |
| DISABLE_GPU = True | |
| HIDDEN_UNITS = 64 | |
| BATCH_SIZE = 64 | |
| if DISABLE_GPU: | |
| import os | |
| os.environ['CUDA_VISIBLE_DEVICES'] = '' | |
| from tensorflow.contrib import keras | |
| import numpy as np | |
| import timeit | |
| model = keras.models.Sequential([ | |
| keras.layers.Dense(HIDDEN_UNITS, input_dim=128), | |
| keras.layers.Dense(HIDDEN_UNITS), | |
| keras.layers.Dense(1) | |
| ]) | |
| model.compile(loss='mse', optimizer='adam') | |
| X = np.random.random((BATCH_SIZE, 128)) | |
| y = np.random.random((BATCH_SIZE, 1)) | |
| # warmup | |
| model.train_on_batch(X, y) | |
| # time it | |
| elapsed = timeit.timeit('model.train_on_batch(X, y)', globals=globals(), number=1000) | |
| print('Took %.3gs' % elapsed) |
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