tensorflow mnist example from https://www.tensorflow.org/overview

mnist.py

import tensorflow as tf
import time
import multiprocessing as mp
mnist = tf.keras.datasets.mnist

(x_train, y_train),(x_test, y_test) = mnist.load_data()
x_train, x_test = x_train / 255.0, x_test / 255.0

model = tf.keras.models.Sequential([
  tf.keras.layers.Flatten(input_shape=(28, 28)),
  tf.keras.layers.Dense(128, activation='relu'),
  tf.keras.layers.Dropout(0.2),
  tf.keras.layers.Dense(10, activation='softmax')
])

model.compile(optimizer='adam',
              loss='sparse_categorical_crossentropy',
              metrics=['accuracy'])

start = time.time()
epochs = 5
model.fit(x_train, y_train, epochs=epochs, workers=mp.cpu_count(), use_multiprocessing=True)
end = time.time()
print('Run epochs: {}'.format(epochs))
print('Total run time: {} s'.format(end-start))
print('Avg run time: {} s/epoch'.format( (end-start)/epochs ))
model.evaluate(x_test, y_test)