tf.contrib.tpu.RunConfig

Class RunConfig

Inherits From: RunConfig

Defined in tensorflow/contrib/tpu/python/tpu/tpu_config.py.

RunConfig with TPU support.

Properties

cluster

cluster_spec

device_fn

Returns the device_fn.

If device_fn is not None, it overrides the default device function used in Estimator. Otherwise the default one is used.

evaluation_master

global_id_in_cluster

The global id in the training cluster.

All global ids in the training cluster are assigned from an increasing sequence of consecutive integers. The first id is 0.

  cluster = {'chief': ['host0:2222'],
             'ps': ['host1:2222', 'host2:2222'],
             'worker': ['host3:2222', 'host4:2222', 'host5:2222']}

Nodes with task type worker can have id 0, 1, 2. Nodes with task type ps can have id, 0, 1. So, task_id is not unique, but the pair (task_type, task_id) can uniquely determine a node in the cluster.

Global id, i.e., this field, is tracking the index of the node among ALL nodes in the cluster. It is uniquely assigned. For example, for the cluster spec given above, the global ids are assigned as:

  task_type  | task_id  |  global_id
  --------------------------------
  chief      | 0        |  0
  worker     | 0        |  1
  worker     | 1        |  2
  worker     | 2        |  3
  ps         | 0        |  4
  ps         | 1        |  5

Returns:

An integer id.

is_chief

keep_checkpoint_every_n_hours

keep_checkpoint_max

log_step_count_steps

master

model_dir

num_ps_replicas

num_worker_replicas

save_checkpoints_secs

save_checkpoints_steps

save_summary_steps

service

Returns the platform defined (in TF_CONFIG) service dict.

session_config

task_id

task_type

tf_random_seed

tpu_config

train_distribute

Returns the optional tf.contrib.distribute.DistributionStrategy object.

Methods

__init__

__init__(
    tpu_config=None,
    evaluation_master=None,
    master=None,
    cluster=None,
    **kwargs
)

Constructs a RunConfig.

Args:

  • tpu_config: the TPUConfig that specifies TPU-specific configuration.
  • evaluation_master: a string. The address of the master to use for eval. Defaults to master if not set.
  • master: a string. The address of the master to use for training.
  • cluster: a ClusterResolver
  • **kwargs: keyword config parameters.

Raises:

  • ValueError: if cluster is not None and the provided session_config has a cluster_def already.

replace

replace(**kwargs)