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Class ParameterServerStrategy

Inherits From: Strategy

Defined in tensorflow/contrib/distribute/python/

A parameter server DistributionStrategy.

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 paramater 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.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.colocate_with) under the strategy's scope. For colocating variables, use distribution.colocate_vars_with instead. Colocation of ops will possibly create conflicts of device assignment.



Initializes this strategy.


  • 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.



tf.distribute.StrategyExtended with additional methods.


Returns number of replicas over which gradients are aggregated.






Any final actions to be done at the end of all computations.

In eager mode, it executes any finalize actions as a side effect. In graph mode, it creates the finalize ops and returns them.

For example, TPU shutdown ops.


A list of ops to execute.



Any initialization to be done before running any computations.

In eager mode, it executes any initialization as a side effect. In graph mode, it creates the initialization ops and returns them.

For example, TPU initialize_system ops.


A list of ops to execute.



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

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.

IMPORTANT: Depending on the DistributionStrategy being used, and whether eager execution is enabled, fn may be called one or more times (once for each replica).


  • fn: 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).



Makes an iterator for input provided via input_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 global batch size. With this assumption, we will make a best effort to divide each batch across all the replicas (one or more workers). If this effort fails, an error will be thrown, and the user should instead use make_input_fn_iterator which provides more control to the user, and does not try to divide a batch across replicas.

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.



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

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

def input_fn(input_context):
  d =[[1.]]).repeat()
  return d.shard(input_context.num_input_pipelines,
with strategy.scope():
  iterator = strategy.make_input_fn_iterator(
  replica_results = strategy.extended.call_for_each_replica(
      replica_fn, iterator.get_next())



An iterator object that can be initialized and fetched next element.



Reduce value across replicas.


  • reduce_op: A tf.distribute.ReduceOp value specifying how values should be combined.
  • value: A "per replica" value to be combined into a single tensor.


A Tensor.



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.



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

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.



The updated copy of the config_proto.