Test PlaidML performance via simple VGG model

test_plaidml_vgg.py

#!/usr/bin/env python

import numpy as np
import os
import time

os.environ['KERAS_BACKEND'] = 'plaidml.keras.backend'

import keras
import keras.applications as kapp
from keras.datasets import cifar10

(x_train, y_train_cats), (x_test, y_test_cats) = cifar10.load_data()

batch_size = 8

x_train = x_train[:batch_size]
x_train = np.repeat(np.repeat(x_train, 7, axis=1), 7, axis=2)
model = kapp.VGG19()
model.compile(optimizer='sgd', loss='categorical_crossentropy', metrics=['accuracy'])

print('Running initial batch (compiling tile program)')
y = model.predict(x=x_train, batch_size=batch_size)

print('Timing inference...')
start = time.time()
for i in range(10):
    y = model.predict(x=x_train, batch_size=batch_size)
print('Ran in {} seconds'.format(time.time() - start))