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An asynchronous multi-worker parameter server tf.distribute strategy.

Inherits From: Strategy

This strategy requires two roles: workers and parameter servers. Variables and updates to those variables will be assigned to parameter servers and other operations are assigned to workers.

When each worker has more than one GPU, operations will be replicated on all GPUs. Even though operations may be replicated, variables are not and each worker shares a common view for which parameter server a variable is assigned to.

By default it uses TFConfigClusterResolver to detect configurations for multi-worker training. This requires a 'TF_CONFIG' environment variable and the 'TF_CONFIG' must have a cluster spec.

This class assumes each worker is running the same code independently, but parameter servers are running a standard server. 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) 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.

2) 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 device assignment conflicts.

For Example:

strategy = tf.distribute.experimental.ParameterServerStrategy()
run_config = tf.estimator.RunConfig(
estimator = tf.estimator.Estimator(config=run_config)

cluster_resolver Optional tf.distribute.cluster_resolver.ClusterResolver object. Defaults to a tf.distribute.cluster_resolver.TFConfigClusterResolver.

cluster_resolver Returns the cluster resolver associated with this strategy.

In general, when using a multi-worker tf.distribute strategy such as tf.distribute.experimental.MultiWorkerMirroredStrategy or tf.distribute.TPUStrategy(), there is a tf.distribute.cluster_resolver.ClusterResolver associated with the strategy used, and such an insta