tf.train.ClusterSpec

class tf.train.ClusterSpec

See the guide: Training > Distributed execution

Represents a cluster as a set of "tasks", organized into "jobs".

A tf.train.ClusterSpec represents the set of processes that participate in a distributed TensorFlow computation. Every tf.train.Server is constructed in a particular cluster.

To create a cluster with two jobs and five tasks, you specify the mapping from job names to lists of network addresses (typically hostname-port pairs).

cluster = tf.train.ClusterSpec({"worker": ["worker0.example.com:2222",
                                           "worker1.example.com:2222",
                                           "worker2.example.com:2222"],
                                "ps": ["ps0.example.com:2222",
                                       "ps1.example.com:2222"]})

Each job may also be specified as a sparse mapping from task indices to network addresses. This enables a server to be configured without needing to know the identity of (for example) all other worker tasks:

cluster = tf.train.ClusterSpec({"worker": {1: "worker1.example.com:2222"},
                                "ps": ["ps0.example.com:2222",
                                       "ps1.example.com:2222"]})

Properties

jobs

Returns a list of job names in this cluster.

Returns:

A list of strings, corresponding to the names of jobs in this cluster.

Methods

__init__(cluster)

Creates a ClusterSpec.

Args:

• cluster: A dictionary mapping one or more job names to (i) a list of network addresses, or (ii) a dictionary mapping integer task indices to network addresses; or a tf.train.ClusterDef protocol buffer.

Raises:

• TypeError: If cluster is not a dictionary mapping strings to lists of strings, and not a tf.train.ClusterDef protobuf.

as_cluster_def()

Returns a tf.train.ClusterDef protocol buffer based on this cluster.

as_dict()

Returns a dictionary from job names to their tasks.

For each job, if the task index space is dense, the corresponding value will be a list of network addresses; otherwise it will be a dictionary mapping (sparse) task indices to the corresponding addresses.

Returns:

A dictionary mapping job names to lists or dictionaries describing the tasks in those jobs.

job_tasks(job_name)

Returns a mapping from task ID to address in the given job.

NOTE: For backwards compatibility, this method returns a list. If the given job was defined with a sparse set of task indices, the length of this list may not reflect the number of tasks defined in this job. Use the tf.train.ClusterSpec.num_tasks method to find the number of tasks defined in a particular job.

Args:

• job_name: The string name of a job in this cluster.

Returns:

A list of task addresses, where the index in the list corresponds to the task index of each task. The list may contain None if the job was defined with a sparse set of task indices.

Raises:

• ValueError: If job_name does not name a job in this cluster.

num_tasks(job_name)

Returns the number of tasks defined in the given job.

Args:

• job_name: The string name of a job in this cluster.

Returns:

The number of tasks defined in the given job.

Raises:

• ValueError: If job_name does not name a job in this cluster.

task_address(job_name, task_index)

Returns the address of the given task in the given job.

Args:

• job_name: The string name of a job in this cluster.
• task_index: A non-negative integer.

Returns:

The address of the given task in the given job.

Raises:

• ValueError: If job_name does not name a job in this cluster, or no task with index task_index is defined in that job.

task_indices(job_name)

Returns a list of valid task indices in the given job.

Args:

• job_name: The string name of a job in this cluster.

Returns:

A list of valid task indices in the given job.

Raises:

• ValueError: If job_name does not name a job in this cluster, or no task with index task_index is defined in that job.

Defined in tensorflow/python/training/server_lib.py.