tf.contrib.tpu.RunConfig

Class `RunConfig`

Inherits From: RunConfig

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

RunConfig with TPU support.

`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.

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.

`eval_distribute`

Optional tf.contrib.distribute.DistributionStrategy for evaluation.

`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`

`protocol`

Returns the optional protocol value.

`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`

Optional tf.contrib.distribute.DistributionStrategy for training.

Methods