keras_multi_gpu.py

import tensorflow as tf
from tensorflow import keras
from tensorflow.keras.applications import Xception
import numpy as np

num_samples = 1000
height = 224
width = 224
num_classes = 1000

strategy = tf.distribute.MirroredStrategy()
with strategy.scope():
    parallel_model = Xception(weights=None,
                     input_shape=(height, width, 3),
                     classes=num_classes)
    parallel_model.compile(loss='categorical_crossentropy', optimizer='rmsprop')

# Generate dummy data.
x = np.random.random((num_samples, height, width, 3))
y = np.random.random((num_samples, num_classes))

parallel_model.summary()
# This `fit` call will be distributed on 8 GPUs.
# Since the batch size is 256, each GPU will process 32 samples.
parallel_model.fit(x, y, epochs=20, batch_size=16) #batch_sized changed to 16