tf.contrib.cluster_resolver.UnionClusterResolver

Class UnionClusterResolver

Inherits From: ClusterResolver

Aliases:

  • Class tf.contrib.cluster_resolver.UnionClusterResolver
  • Class tf.contrib.cluster_resolver.python.training.UnionClusterResolver

Defined in tensorflow/python/distribute/cluster_resolver/cluster_resolver.py.

Performs a union on underlying ClusterResolvers.

This class performs a union given two or more existing ClusterResolvers. It merges the underlying ClusterResolvers, and returns one unified ClusterSpec when cluster_spec is called. The details of the merge function is documented in the cluster_spec function.

For additional Cluster Resolver properties such as task type, task index, rpc layer, environment, etc..., we will return the value from the first ClusterResolver in the union.

__init__

__init__(
    *args,
    **kwargs
)

Initializes a UnionClusterResolver with other ClusterResolvers.

Args:

  • *args: ClusterResolver objects to be unionized.
  • **kwargs: rpc_layer - (Optional) Override value for the RPC layer used by TensorFlow. task_type - (Optional) Override value for the current task type. task_index - (Optional) Override value for the current task index.

Raises:

  • TypeError: If any argument is not a subclass of ClusterResolvers.
  • ValueError: If there are no arguments passed.

Properties

environment

Returns the current environment which TensorFlow is running in.

rpc_layer

task_index

task_type

Methods

cluster_spec

cluster_spec()

Returns a union of all the ClusterSpecs from the ClusterResolvers.

Returns:

A ClusterSpec containing host information merged from all the underlying ClusterResolvers.

Raises:

  • KeyError: If there are conflicting keys detected when merging two or more dictionaries, this exception is raised.

If all underlying ClusterSpecs expose the set of workers as lists, we will concatenate the lists of workers, starting with the list of workers from the first ClusterResolver passed into the constructor.

If any of the ClusterSpecs expose the set of workers as a dict, we will treat all the sets of workers as dicts (even if they are returned as lists) and will only merge them into a dict if there is no conflicting keys. If there is a conflicting key, we will raise a KeyError.

master

master(
    task_type=None,
    task_index=None,
    rpc_layer=None
)

Returns the master address to use when creating a session.

This usually returns the master from the first ClusterResolver passed in, but you can override this by specifying the task_type and task_index.

Args:

  • task_type: (Optional) The type of the TensorFlow task of the master.
  • task_index: (Optional) The index of the TensorFlow task of the master.
  • rpc_layer: (Optional) The RPC protocol for the given cluster.

Returns:

The name or URL of the session master.

num_accelerators

num_accelerators(
    task_type=None,
    task_index=None,
    accelerator_type='GPU',
    config_proto=None
)

Returns the number of accelerator cores per worker.

This returns the number of accelerator cores (such as GPUs and TPUs) available per worker. If workers only has CPU cores available, then this should return 0. This method will query the master for this information if it is not otherwise known.

Optionally, we allow callers to specify the task_type, task_index, and rpc_layer, if they want to target a specific TensorFlow process to query the number of accelerators. This is to support heterogenous environments, where the number of accelerators cores per host is different.

Args:

  • task_type: (Optional) The type of the TensorFlow task of the machine we want to query.
  • task_index: (Optional) The index of the TensorFlow task of the machine we want to query.
  • accelerator_type: (Optional) The type of accelerator we are trying to query (defaults to 'GPU').
  • config_proto: (Optional) Configuration for starting a new session to query how many accelerator cores it has.