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A parameter server DistributionStrategy.

Inherits From: Strategy

*** contrib version ***

This strategy class works for both local training and between-graph replicated training for multiple workers. If cluster_spec is specified, either passed in to init() method or parsed from the "TF_CONFIG" environment variable, variables and updates to those variables are assigned to parameter servers and other operations are assigned to workers. If cluster_spec is not set, it becomes local training where variables are assigned to local CPU or the only GPU. When each worker has more than one GPU, operations will be replicated on these GPUs. In both cases, operations are replicated but variables are not and these workers share a common view for which parameter server a variable is assigned to.

This class assumes between-graph replication will be used and works on a graph for a particular worker. Note that each graph and worker is independent. This means that while each worker will synchronously compute a single gradient update across all GPUs, updates between workers proceed asynchronously. Operations that occur only on the first replica (such as incrementing the global step), will occur on the first replica of every worker.

It is expected to call call_for_each_replica(fn, ...) for any operations which potentially can be replicated across replicas (i.e. multiple GPUs) even if there is only CPU or one GPU. When defining the fn, extra caution needs to be taken:

1) Always use tf.compat.v1.get_variable instead of tf.Variable which is not able to refer to the same variable on different replicas.

2) It is generally not recommended to open a device scope under the strategy's scope. A device scope (i.e. calling tf.device) will be merged with or override the device for operations but will not change the device for variables.

3) It is also not recommended to open a colocation scope (i.e. calling tf.compat.v1.colocate_with) under the strategy's scope. For colocating variables, use strategy.extended.colocate_vars_with instead. Colocation of ops will possibly create conflicts of device assignment.

num_gpus_per_worker number of local GPUs or GPUs per worker, the default is 0 meaning CPU only.

ValueError if cluster_spec is given but task_type or task_id is not.

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



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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
  strategy.experimental_run_v2(train_step, 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. In this case, consider using experimental_distribute_datasets_from_function instead.

You can disable dataset sharding across workers using the auto_shard 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.

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 = strategy.experimental_run_v2(replica_fn, args=(batch,))

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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Returns the list of all local per-replica values contained in value.

value A value returned by experimental_run(), experimental_run_v2(), extended.call_for_each_replica(), or a variable created in scope.

A tuple of values contained in value. If value represents a single value, this returns (value,).


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Makes a for input provided via a numpy array.

This avoids adding numpy_input as a large constant in the graph, and copies the data to the machine or machines that will be processing the input.

Note that you will likely need to use tf.distribute.Strategy.experimental_distribute_dataset with the returned dataset to further distribute it with the strategy.


numpy_input = np.ones([10], dtype=np.float32)
dataset = strategy.experimental_make_numpy_dataset(numpy_input)
dist_dataset = strategy.experimental_distribute_dataset(dataset)

numpy_input A nest of NumPy input arrays that will be converted into a dataset. Note that lists of Numpy arrays are stacked, as that is normal behavior.
session (TensorFlow v1.x graph execution only) A session used for initialization.

A representing numpy_input.


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Runs ops in fn on each replica, with inputs from input_iterator.

DEPRECATED: This method is not available in TF 2.x. Please switch to using experimental_run_v2 instead.

When eager execution is enabled, executes ops specified by fn on each replica. Otherwise, builds a graph to execute the ops on each replica.

Each replica will take a single, different input from the inputs provided by one get_next call on the input iterator.

fn may call tf.distribute.get_replica_context() to access members such as replica_id_in_sync_group.

fn The function to run. The inputs to the function must match the outputs of input_iterator.get_next(). The output must be a tf.nest of Tensors.
input_iterator (Optional) input iterator from which the inputs are taken.

Merged return value of fn across replicas. The structure of the return value is the same as the return value from fn. Each element in the structure can either be PerReplica (if the values are unsynchronized), Mirrored (if the values are kept in sync), or Tensor (if running on a single replica).


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Run fn on each replica, with the given arguments.

Executes ops specified by fn on each replica. If args or kwargs have "per-replica" values, such as those produced by a "distributed Dataset", when fn is executed on a particular replica, it will be executed with the component of those "per-replica" values that correspond to that replica.

fn may call tf.distribute.get_replica_context() to access members such as all_reduce.

All arguments in args or kwargs should either be nest of tensors or per-replica objects containing tensors or composite tensors.

fn The function to run. The output must be a tf.nest of Tensors.
args (Optional) Positional arguments to fn.
kwargs (Optional) Keyword arguments to fn.

Merged return value of fn across replicas. The structure of the return value is the same as the return value from fn. Each element in the structure can either be "per-replica" Tensor objects or Tensors (for example, if running on a single replica).


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Makes an iterator for input provided via dataset.

Data from the given dataset will be distributed evenly across all the compute replicas. We will assume that the input dataset is batched by the per-replica batch size.

The user could also use make_input_fn_iterator if they want to customize which input is fed to which replica/worker etc.

dataset that will be distributed evenly across all replicas.

An tf.distribute.InputIterator which returns inputs for each step of the computation. User should call initialize on the returned iterator.


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Returns an iterator split across replicas created from an input function.

DEPRECATED: This method is not available in TF 2.x.

The input_fn should take an tf.distribute.InputContext object where information about batching and input sharding can be accessed:

def input_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,
with strategy.scope():
  iterator = strategy.make_input_fn_iterator(input_fn)
  replica_results = strategy.experimental_run(replica_fn, iterator)

The returned by input_fn should have a per-replica batch size, which may be computed using input_context.get_per_replica_batch_size.

input_fn A function taking a tf.distribute.InputContext object and returning a
replication_mode an enum value of tf.distribute.InputReplicationMode. Only PER_WORKER is supported currently, which means there will be a single call to input_fn per worker. Replicas will dequeue from the local on their worker.

An iterator object that should first be .initialize()-ed. It may then either be passed to strategy.experimental_run() or you can iterator.get_next() to get the next value to pass to strategy.extended.call_for_each_replica().


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Reduce value across replicas.

Given a per-replica value returned by experimental_run_v2, say a per-example loss, the batch will be divided across all the replicas. This function allows you to aggregate across replicas and optionally also across batch elements. For example, if you have a global batch size of 8 and 2 replicas, values for examples [0, 1, 2, 3] will be on replica 0 and [4, 5, 6, 7] will be on replica 1. By default, reduce will just aggregate across replicas, returning [0+4, 1+5, 2+6, 3+7]. This is useful when each replica is computing a scalar or some other value that doesn't have a "batch" dimension (like a gradient). More often you will want to aggregate across the global batch, which you can get by specifying the batch dimension as the axis, typically axis=0. In this case it would return a scalar 0+1+2+3+4+5+6+7.

If there is a last partial batch, you will need to specify an axis so that the resulting shape is consistent across replicas. So if the last batch has size 6 and it is divided into [0, 1, 2, 3] and [4, 5], you would get a shape mismatch unless you specify axis=0. If you specify tf.distribute.ReduceOp.MEAN, using axis=0 will use the correct denominator of 6. Contrast this with computing reduce_mean to get a scalar value on each replica and this function to average those means, which will weigh some values 1/8 and others 1/4.

reduce_op A tf.distribute.ReduceOp value specifying how values should be combined.
value A "per replica" value, e.g. returned by experimental_run_v2 to be combined into a single tensor.
axis Specifies the dimension to reduce along within each replica's tensor. Should typically be set to the batch dimension, or None to only reduce across replicas (e.g. if the tensor has no batch dimension).

A Tensor.


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Returns a context manager selecting this Strategy as current.

Inside a with strategy.scope(): code block, this thread will use a variable creator set by strategy, and will enter its "cross-replica context".

A context manager.


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Returns a copy of config_proto modified for use with this strategy.

DEPRECATED: This method is not available in TF 2.x.

The updated config has something needed to run a strategy, e.g. configuration to run collective ops, or device filters to improve distributed training performance.

config_proto a tf.ConfigProto object.

The updated copy of the config_proto.