def _clone_and_build_model(mode,
keras_model,
custom_objects,
features=None,
labels=None):
"""Clone and build the given keras_model.
Args:
mode: training mode.
keras_model: an instance of compiled keras model.
custom_objects: Dictionary for custom objects.
features:
labels:
Returns:
The newly built model.
"""
# Set to True during training, False for inference.
K.set_learning_phase(mode == model_fn_lib.ModeKeys.TRAIN)
# Clone keras model.
input_tensors = None if features is None else _create_ordered_io(
keras_model, features)
if custom_objects:
with CustomObjectScope(custom_objects):
model = models.clone_model(keras_model, input_tensors=input_tensors)
else:
model = models.clone_model(keras_model, input_tensors=input_tensors)
# Compile/Build model
if mode is model_fn_lib.ModeKeys.PREDICT and not model.built:
model.build()
else:
optimizer_config = keras_model.optimizer.get_config()
optimizer = keras_model.optimizer.__class__.from_config(optimizer_config)
optimizer.iterations = training_util.get_or_create_global_step()
# Get list of outputs.
if labels is None:
target_tensors = None
elif isinstance(labels, dict):
target_tensors = _create_ordered_io(keras_model, labels, is_input=False)
else:
target_tensors = [
_cast_tensor_to_floatx(
sparse_tensor_lib.convert_to_tensor_or_sparse_tensor(labels))
]
model.compile(
optimizer,
keras_model.loss,
metrics=keras_model.metrics,
loss_weights=keras_model.loss_weights,
sample_weight_mode=keras_model.sample_weight_mode,
weighted_metrics=keras_model.weighted_metrics,
target_tensors=target_tensors)
if isinstance(model, models.Sequential):
model = model.model
return model
def _clone_and_build_model(mode,
keras_model,
custom_objects,
features=None,
labels=None):
"""Clone and build the given keras_model.
Args:
mode: training mode.
keras_model: an instance of compiled keras model.
custom_objects: Dictionary for custom objects.
features: Dict of tensors.
labels: Dict of tensors, or single tensor instance.
Returns:
The newly built model.
"""
# Set to True during training, False for inference.
K.set_learning_phase(mode == model_fn_lib.ModeKeys.TRAIN)
# Get list of inputs.
if features is None:
input_tensors = None
else:
input_tensors = _create_ordered_io(keras_model,
estimator_io=features,
is_input=True)
# Get list of outputs.
if labels is None:
target_tensors = None
elif isinstance(labels, dict):
target_tensors = _create_ordered_io(keras_model,
estimator_io=labels,
is_input=False)
else:
target_tensors = [_convert_tensor(labels)]
if keras_model._is_graph_network:
if custom_objects:
with CustomObjectScope(custom_objects):
model = models.clone_model(keras_model,
input_tensors=input_tensors)
else:
model = models.clone_model(keras_model,
input_tensors=input_tensors)
else:
model = keras_model
_in_place_subclassed_model_reset(model)
if input_tensors is not None:
model._set_inputs(input_tensors)
# Compile/Build model
if mode is model_fn_lib.ModeKeys.PREDICT:
if isinstance(model, models.Sequential):
model.build()
else:
if isinstance(keras_model.optimizer, optimizers.TFOptimizer):
optimizer = keras_model.optimizer
else:
optimizer_config = keras_model.optimizer.get_config()
optimizer = keras_model.optimizer.__class__.from_config(
optimizer_config)
optimizer.iterations = training_util.get_or_create_global_step()
model.compile(optimizer,
keras_model.loss,
metrics=keras_model.metrics,
loss_weights=keras_model.loss_weights,
sample_weight_mode=keras_model.sample_weight_mode,
weighted_metrics=keras_model.weighted_metrics,
target_tensors=target_tensors)
return model
def _clone_and_build_model(mode,
keras_model,
custom_objects,
features=None,
labels=None):
"""Clone and build the given keras_model.
Args:
mode: training mode.
keras_model: an instance of compiled keras model.
custom_objects: Dictionary for custom objects.
features: Dict of tensors.
labels: Dict of tensors, or single tensor instance.
Returns:
The newly built model.
"""
# Set to True during training, False for inference.
K.set_learning_phase(mode == model_fn_lib.ModeKeys.TRAIN)
# Get list of inputs.
if features is None:
input_tensors = None
else:
input_tensors = _create_ordered_io(keras_model,
estimator_io=features,
is_input=True)
# Get list of outputs.
if labels is None:
target_tensors = None
elif isinstance(labels, dict):
target_tensors = _create_ordered_io(keras_model,
estimator_io=labels,
is_input=False)
else:
target_tensors = [
_convert_tensor(labels)
]
if keras_model._is_graph_network:
if custom_objects:
with CustomObjectScope(custom_objects):
model = models.clone_model(keras_model, input_tensors=input_tensors)
else:
model = models.clone_model(keras_model, input_tensors=input_tensors)
else:
model = keras_model
_in_place_subclassed_model_reset(model)
if input_tensors is not None:
model._set_inputs(input_tensors)
# Compile/Build model
if mode is model_fn_lib.ModeKeys.PREDICT:
if isinstance(model, models.Sequential):
model.build()
else:
if isinstance(keras_model.optimizer, optimizers.TFOptimizer):
optimizer = keras_model.optimizer
else:
optimizer_config = keras_model.optimizer.get_config()
optimizer = keras_model.optimizer.__class__.from_config(optimizer_config)
optimizer.iterations = training_util.get_or_create_global_step()
model.compile(
optimizer,
keras_model.loss,
metrics=keras_model.metrics,
loss_weights=keras_model.loss_weights,
sample_weight_mode=keras_model.sample_weight_mode,
weighted_metrics=keras_model.weighted_metrics,
target_tensors=target_tensors)
return model
def multi_gpu_model(model, gpus, cpu_merge=True, cpu_relocation=False):
"""Replicates a model on different GPUs.
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
```python
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
```python
..
# 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)
```python
..
# 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.
"""
# pylint: disable=g-import-not-at-top
from tensorflow.python.keras._impl.keras.layers.core import Lambda
from tensorflow.python.keras._impl.keras.layers.merge import concatenate
if isinstance(gpus, (list, tuple)):
if len(gpus) <= 1:
raise ValueError('For multi-gpu usage to be effective, '
'call `multi_gpu_model` with `len(gpus) >= 2`. '
'Received: `gpus=%s`' % gpus)
num_gpus = len(gpus)
target_gpu_ids = gpus
else:
if gpus <= 1:
raise ValueError('For multi-gpu usage to be effective, '
'call `multi_gpu_model` with `gpus >= 2`. '
'Received: `gpus=%s`' % gpus)
num_gpus = gpus
target_gpu_ids = range(num_gpus)
target_devices = ['/cpu:0'] + ['/gpu:%d' % i for i in target_gpu_ids]
available_devices = _get_available_devices()
available_devices = [
_normalize_device_name(name) for name in available_devices
]
for device in target_devices:
if device not in available_devices:
raise ValueError(
'To call `multi_gpu_model` with `gpus=%s`, '
'we expect the following devices to be available: %s. '
'However this machine only has: %s. '
'Try reducing `gpus`.' %
(gpus, target_devices, available_devices))
def get_slice(data, i, parts):
"""Slice an array into `parts` slices and return slice `i`.
Arguments:
data: array to slice.
i: index of slice to return.
parts: number of slices to make.
Returns:
Slice `i` of `data`.
"""
shape = array_ops.shape(data)
batch_size = shape[:1]
input_shape = shape[1:]
step = batch_size // parts
if i == num_gpus - 1:
size = batch_size - step * i
else:
size = step
size = array_ops.concat([size, input_shape], axis=0)
stride = array_ops.concat([step, input_shape * 0], axis=0)
start = stride * i
return array_ops.slice(data, start, size)
# Relocate the model definition under CPU device scope if needed
if cpu_relocation:
from tensorflow.python.keras._impl.keras.models import clone_model # pylint: disable=g-import-not-at-top
with ops.device('/cpu:0'):
model = clone_model(model)
all_outputs = []
for i in range(len(model.outputs)):
all_outputs.append([])
# Place a copy of the model on each GPU,
# each getting a slice of the inputs.
for i, gpu_id in enumerate(target_gpu_ids):
with ops.device('/gpu:%d' % gpu_id):
with ops.name_scope('replica_%d' % gpu_id):
inputs = []
# Retrieve a slice of the input.
for x in model.inputs:
input_shape = tuple(x.get_shape().as_list())[1:]
slice_i = Lambda(get_slice,
output_shape=input_shape,
arguments={
'i': i,
'parts': num_gpus
})(x)
inputs.append(slice_i)
# Apply model on slice
# (creating a model replica on the target device).
outputs = model(inputs)
if not isinstance(outputs, list):
outputs = [outputs]
# Save the outputs for merging back together later.
for o in range(len(outputs)):
all_outputs[o].append(outputs[o])
# Merge outputs under expected scope.
with ops.device('/cpu:0' if cpu_merge else '/gpu:%d' % target_gpu_ids[0]):
merged = []
for name, outputs in zip(model.output_names, all_outputs):
merged.append(concatenate(outputs, axis=0, name=name))
return Model(model.inputs, merged)