tf.keras.utils.multi_gpu_model

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Replicates a model on different GPUs. (deprecated)

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tf.compat.v1.keras.utils.multi_gpu_model

tf.keras.utils.multi_gpu_model(
    model, gpus, cpu_merge=True, cpu_relocation=False
)

Specifically, this function implements single-machine multi-GPU data parallelism. It works in the following way:

• Divide the model's input(s) into multiple sub-batches.
• Apply a model copy on each sub-batch. Every model copy is executed on a dedicated GPU.
• Concatenate the results (on CPU) into one big batch.

E.g. if your batch_size is 64 and you use gpus=2, then we will divide the input into 2 sub-batches of 32 samples, process each sub-batch on one GPU, then return the full batch of 64 processed samples.

This induces quasi-linear speedup on up to 8 GPUs.

This function is only available with the TensorFlow backend for the time being.

Arguments
model	A Keras model instance. To avoid OOM errors, this model could have been built on CPU, for instance (see usage example below).
gpus	Integer >= 2, number of on GPUs on which to create model replicas.
cpu_merge	A boolean value to identify whether to force merging model weights under the scope of the CPU or not.
cpu_relocation	A boolean value to identify whether to create the model's weights under the scope of the CPU. If the model is not defined under any preceding device scope, you can still rescue it by activating this option.

Returns
A Keras Model instance which can be used just like the initial model argument, but which distributes its workload on multiple GPUs.

Example 1: Training models with weights merge on CPU

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

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=8)
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=256)

# Save model via the template model (which shares the same weights):
model.save('my_model.h5')

Example 2: Training models with weights merge on CPU using cpu_relocation

..
# Not needed to change the device scope for model definition:
model = Xception(weights=None, ..)

try:
    model = multi_gpu_model(model, cpu_relocation=True)
    print("Training using multiple GPUs..")
except:
    print("Training using single GPU or CPU..")

model.compile(..)
..

Example 3: Training models with weights merge on GPU (recommended for NV-link)

..
# Not needed to change the device scope for model definition:
model = Xception(weights=None, ..)

try:
    model = multi_gpu_model(model, cpu_merge=False)
    print("Training using multiple GPUs..")
except:
    print("Training using single GPU or CPU..")
model.compile(..)
..

Raises
ValueError	if the gpus argument does not match available devices.