tf.compat.v1.estimator.tpu.RunConfig

RunConfig with TPU support.

Inherits From: RunConfig

tf.compat.v1.estimator.tpu.RunConfig(
    tpu_config=None,
    evaluation_master=None,
    master=None,
    cluster=None,
    **kwargs
)

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.

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

checkpoint_save_graph_def

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.distribute.Strategy for evaluation.
evaluation_master

experimental_max_worker_delay_secs

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

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

session_creation_timeout_secs

task_id

task_type

tf_random_seed

tpu_config

train_distribute Optional tf.distribute.Strategy for training.

Methods

replace

View source

replace(
    **kwargs
)

Returns a new instance of RunConfig replacing specified properties.

Only the properties in the following list are allowed to be replaced:

  • model_dir,
  • tf_random_seed,
  • save_summary_steps,
  • save_checkpoints_steps,
  • save_checkpoints_secs,
  • session_config,
  • keep_checkpoint_max,
  • keep_checkpoint_every_n_hours,
  • log_step_count_steps,
  • train_distribute,
  • device_fn,
  • protocol.
  • eval_distribute,
  • experimental_distribute,
  • experimental_max_worker_delay_secs,

In addition, either save_checkpoints_steps or save_checkpoints_secs can be set (should not be both).

Args
**kwargs keyword named properties with new values.

Raises
ValueError If any property name in kwargs does not exist or is not allowed to be replaced, or both save_checkpoints_steps and save_checkpoints_secs are set.

Returns
a new instance of RunConfig.