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Synchronous training across multiple replicas on one machine.

Inherits From: Strategy

This strategy is typically used for training on one machine with multiple GPUs. For TPUs, use tf.distribute.experimental.TPUStrategy. To use MirroredStrategy with multiple workers, please refer to tf.distribute.experimental.MultiWorkerMirroredStrategy.

For example, a variable created under a MirroredStrategy is a MirroredVariable. If no devices are specified in the constructor argument of the strategy then it will use all the available GPUs. If no GPUs are found, it will use the available CPUs. Note that TensorFlow treats all CPUs on a machine as a single device, and uses threads internally for parallelism.

strategy = tf.distribute.MirroredStrategy()
with strategy.scope():
  x = tf.Variable(1.)
    0: <tf.Variable 'Variable:0' shape=() dtype=float32, numpy=1.0>

While using distribution strategies, all the variable creation should be done within the strategy's scope. This will replicate the variables across all the replicas and keep them in sync using an all-reduce algorithm.

Variables created inside a MirroredStrategy which is wrapped with a tf.function are still MirroredVariables.

x = []
@tf.function  # Wrap the function with tf.function.
def create_variable():
  if not x:
strategy = tf.distribute.MirroredStrategy()
with strategy.scope():
  print (x[0])
    0: <tf.Variable 'Variable:0' shape=() dtype=float32, numpy=1.0>

experimental_distribute_dataset can be used to distribute the dataset across the replicas when writing your own training loop. If you are using .fit and .compile methods available in tf.keras, then tf.keras will handle the distribution for you.

For example:

my_strategy = tf.distribute.MirroredStrategy()
with my_strategy.scope():
  def distribute_train_epoch(dataset):
    def replica_fn(input):
      # process input and return result
      return result

    total_result = 0
    for x in dataset:
      per_replica_result =, args=(x,))
      total_result += my_strategy.reduce(tf.distribute.ReduceOp.SUM,
                                         per_replica_result, axis=None)
    return total_result

  dist_dataset = my_strategy.experimental_distribute_dataset(dataset)
  for _ in range(EPOCHS):
    train_result = distribute_train_epoch(dist_dataset)

devices a list of device strings such as ['/gpu:0', '/gpu:1']. If None, all available GPUs are used. If no GPUs are found, CPU is used.
cross_device_ops optional, a descedant of CrossDeviceOps. If this is not set, NcclAllReduce() will be used by default. One would customize this if NCCL isn't available or if a special implementation that exploits the particular hardware is available.

extended tf.distribute.StrategyExtended with additional methods.
num_replicas_in_sync Returns number of replicas over which gradients are aggregated.



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Adds annotation that tensor will be assigned to a logical device.

# Initializing TPU system with 2 logical devices and 4 replicas.
resolver = tf.distribute.cluster_resolver.TPUClusterResolver(tpu='')
topology = tf.tpu.experimental.initialize_tpu_system(resolver)
device_assignment =
    computation_shape=[1, 1, 2],
strategy = tf.distribute.experimental.TPUStrategy(
    resolver, device_assignment=device_assignment)
iterator = iter(inputs)

def step_fn(inputs):
  output = tf.add(inputs, inputs)

  // Add operation will be executed on logical device 0.
  output = strategy.experimental_assign_to_logical_device(output, 0)
  return output, args=(next(iterator),))

tensor Input tensor to annotate.
logical_device_id Id of the logical core to which the tensor will be assigned.

ValueError The logical device id presented is not consistent with total number of partitions specified by the device assignment.

Annotated tensor with idential value as tensor.


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Distributes a instance provided via dataset.

The returned distributed dataset can be iterated over similar to how regular datasets can. NOTE: Currently, the user cannot add any more transformations to a distributed dataset.

The following is an example:

strategy = tf.distribute.MirroredStrategy()

# Create a dataset
dataset = dataset_ops.Dataset.TFRecordDataset([
  "/a/1.tfr", "/a/2.tfr", "/a/3.tfr", "/a/4.tfr"])

# Distribute that dataset
dist_dataset = strategy.experimental_distribute_dataset(dataset)

# Iterate over the distributed dataset
for x in dist_dataset:
  # process dataset elements, args=(x,))

We will assume that the input dataset is batched by the global batch size. With this assumption, we will make a best effort to divide each batch across all the replicas (one or more workers).

In a multi-worker setting, we will first attempt to distribute the dataset by attempting to detect whether the dataset is being created out of ReaderDatasets (e.g. TFRecordDataset, TextLineDataset, etc.) and if so, attempting to shard the input files. Note that there has to be at least one input file per worker. If you have less than one input file per worker, we suggest that you should disable distributing your dataset using the method below.

If that attempt is unsuccessful (e.g. the dataset is created from a Dataset.range), we will shard the dataset evenly at the end by appending a .shard operation to the end of the processing pipeline. This will cause the entire preprocessing pipeline for all the data to be run on every worker, and each worker will do redundant work. We will print a warning if this method of sharding is selected.

You can disable dataset sharding across workers using the auto_shard_policy option in

Within each worker, we will also split the data among all the worker devices (if more than one a present), and this will happen even if multi-worker sharding is disabled using the method above.

If the above batch splitting and dataset sharding logic is undesirable, please use experimental_distribute_datasets_from_function instead, which does not do any automatic splitting or sharding.

You can also use the element_spec property of the distributed dataset returned by this API to query the tf.TypeSpec of the elements returned by the iterator. This can be used to set the input_signature property of a tf.function.

strategy = tf.distribute.MirroredStrategy()

# Create a dataset
dataset = dataset_ops.Dataset.TFRecordDataset([
  "/a/1.tfr", "/a/2.tfr", "/a/3.tfr", "/a/4.tfr"])

# Distribute that dataset
dist_dataset = strategy.experimental_distribute_dataset(dataset)

def train_step(inputs):
  # train model with inputs

# Iterate over the distributed dataset
for x in dist_dataset:
  # process dataset elements, args=(x,))

dataset that will be sharded across all replicas using the rules stated above.

A "distributed Dataset", which acts like a except it produces "per-replica" values.


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Distributes instances created by calls to dataset_fn.

dataset_fn will be called once for each worker in the strategy. Each replica on that worker will dequeue one batch of inputs from the local Dataset (i.e. if a worker has two replicas, two batches will be dequeued from the Dataset every step).

This method can be used for several purposes. For example, where experimental_distribute_dataset is unable to shard the input files, this method might be used to manually shard the dataset (avoiding the slow fallback behavior in experimental_distribute_dataset). In cases where the dataset is infinite, this sharding can be done by creating dataset replicas that differ only in their random seed. experimental_distribute_dataset may also sometimes fail to split the batch across replicas on a worker. In that case, this method can be used where that limitation does not exist.

The dataset_fn should take an tf.distribute.InputContext instance where information about batching and input replication can be accessed:

def dataset_fn(input_context):
  batch_size = input_context.get_per_replica_batch_size(global_batch_size)
  d =[[1.]]).repeat().batch(batch_size)
  return d.shard(
      input_context.num_input_pipelines, input_context.input_pipeline_id)

inputs = strategy.experimental_distribute_datasets_from_function(dataset_fn)

for batch in inputs:
  replica_results =, args=(batch,))

To query the tf.TypeSpec of the elements in the distributed dataset returned by this API, you need to use the element_spec property of the distributed iterator. This tf.TypeSpec can be used to set the input_signature property of a tf.function.

# If you want to specify `input_signature` for a `tf.function` you must
# first create the iterator.
iterator = iter(inputs)

def replica_fn_with_signature(inputs):
  # train the model with inputs

for _ in range(steps):,

dataset_fn A function taking a tf.distribute.InputContext instance and returning a

A "distributed Dataset", which acts like a except it produces "per-replica" values.


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Generates tf.distribute.DistributedValues from value_fn.

This function is to generate tf.distribute.DistributedValues to pass into run, reduce, or other methods that take distributed values when not using datasets.

value_fn The function to run to generate values. It is called for each replica with tf.distribute.ValueContext as the sole argument. It must return a Tensor or a type that can be converted to a Tensor.

A tf.distribute.DistributedValues containing a value for each replica.

Example usage:

  1. Return constant value per replica:
strategy = tf.distribu