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keras multi gpu training
import tensorflow as tf
from keras.applications import Xception
from keras.utils import multi_gpu_model
import numpy as np
G = 8
batch_size_per_gpu = 32
batch_size = batch_size_per_gpu * G
num_samples = 1000
height = 224
width = 224
num_classes = 1000
# Instantiate the base model (or "template" model).
# We recommend doing this with under a CPU device scope,
# so that the model's weights are hosted on CPU memory.
# Otherwise they may end up hosted on a GPU, which would
# complicate weight sharing.
with tf.device('/cpu:0'):
model = Xception(weights=None,
input_shape=(height, width, 3),
classes=num_classes)
# Replicates the model on 8 GPUs.
# This assumes that your machine has 8 available GPUs.
parallel_model = multi_gpu_model(model, gpus=G)
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))
# 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=batch_size)
# Save model via the template model (which shares the same weights):
model.save('my_model.h5')
results from Multi-GPU training with Keras, Python, and deep learning on Onepanel.io
To validate this, we trained MiniGoogLeNet on the CIFAR-10 dataset with 4 V100 GPU.
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