tf.estimator.RunConfig

Class RunConfig

Defined in tensorflow/python/estimator/run_config.py.

This class specifies the configurations for an Estimator run.

Properties

cluster_spec

evaluation_master

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

Methods

__init__

__init__(
    model_dir=None,
    tf_random_seed=None,
    save_summary_steps=100,
    save_checkpoints_steps=_USE_DEFAULT,
    save_checkpoints_secs=_USE_DEFAULT,
    session_config=None,
    keep_checkpoint_max=5,
    keep_checkpoint_every_n_hours=10000,
    log_step_count_steps=100
)

Constructs a RunConfig.

All distributed training related properties cluster_spec, is_chief, master , num_worker_replicas, num_ps_replicas, task_id, and task_type are set based on the TF_CONFIG environment variable, if the pertinent information is present. The TF_CONFIG environment variable is a JSON object with attributes: cluster and task.

cluster is a JSON serialized version of ClusterSpec's Python dict from server_lib.py, mapping task types (usually one of the TaskType enums) to a list of task addresses.

task has two attributes: type and index, where type can be any of the task types in cluster. WhenTF_CONFIG` contains said information, the following properties are set on this class:

• cluster_spec is parsed from TF_CONFIG['cluster']. Defaults to {}. If present, must have one and only one node in the chief attribute of cluster_spec.
• task_type is set to TF_CONFIG['task']['type']. Must set if cluster_spec is present; must be worker (the default value) if cluster_spec is not set.
• task_id is set to TF_CONFIG['task']['index']. Must set if cluster_spec is present; must be 0 (the default value) if cluster_spec is not set.
• master is determined by looking up task_type and task_id in the cluster_spec. Defaults to ''.
• num_ps_replicas is set by counting the number of nodes listed in the ps attribute of cluster_spec. Defaults to 0.
• num_worker_replicas is set by counting the number of nodes listed in the worker and chief attributes of cluster_spec. Defaults to 1.
• is_chief is determined based on task_type and cluster.

There is a special node with task_type as evaluator, which is not part of the (training) cluster_spec. It handles the distributed evaluation job.

Example of non-chief node:

cluster = {'chief': ['host0:2222'],
           'ps': ['host1:2222', 'host2:2222'],
           'worker': ['host3:2222', 'host4:2222', 'host5:2222']}
os.environ['TF_CONFIG'] = json.dumps(
    {'cluster': cluster,
     'task': {'type': 'worker', 'index': 1}})
config = ClusterConfig()
assert config.master == 'host4:2222'
assert config.task_id == 1
assert config.num_ps_replicas == 2
assert config.num_worker_replicas == 4
assert config.cluster_spec == server_lib.ClusterSpec(cluster)
assert config.task_type == 'worker'
assert not config.is_chief

Example of chief node:

cluster = {'chief': ['host0:2222'],
           'ps': ['host1:2222', 'host2:2222'],
           'worker': ['host3:2222', 'host4:2222', 'host5:2222']}
os.environ['TF_CONFIG'] = json.dumps(
    {'cluster': cluster,
     'task': {'type': 'chief', 'index': 0}})
config = ClusterConfig()
assert config.master == 'host0:2222'
assert config.task_id == 0
assert config.num_ps_replicas == 2
assert config.num_worker_replicas == 4
assert config.cluster_spec == server_lib.ClusterSpec(cluster)
assert config.task_type == 'chief'
assert config.is_chief

Example of evaluator node (evaluator is not part of training cluster):

cluster = {'chief': ['host0:2222'],
           'ps': ['host1:2222', 'host2:2222'],
           'worker': ['host3:2222', 'host4:2222', 'host5:2222']}
os.environ['TF_CONFIG'] = json.dumps(
    {'cluster': cluster,
     'task': {'type': 'evaluator', 'index': 0}})
config = ClusterConfig()
assert config.master == ''
assert config.evaluator_master == ''
assert config.task_id == 0
assert config.num_ps_replicas == 0
assert config.num_worker_replicas == 0
assert config.cluster_spec == {}
assert config.task_type == 'evaluator'
assert not config.is_chief

N.B.: If save_checkpoints_steps or save_checkpoints_secs is set, keep_checkpoint_max might need to be adjusted accordingly, especially in distributed training. For example, setting save_checkpoints_secs as 60 without adjusting keep_checkpoint_max (defaults to 5) leads to situation that checkpoint would be garbage collected after 5 minutes. In distributed training, the evaluation job starts asynchronously and might fail to load or find the checkpoint due to race condition.

Args:

• model_dir: directory where model parameters, graph, etc are saved. If None, will use a default value set by the Estimator.
• tf_random_seed: Random seed for TensorFlow initializers. Setting this value allows consistency between reruns.
• save_summary_steps: Save summaries every this many steps.
• save_checkpoints_steps: Save checkpoints every this many steps. Can not be specified with save_checkpoints_secs.
• save_checkpoints_secs: Save checkpoints every this many seconds. Can not be specified with save_checkpoints_steps. Defaults to 600 seconds if both save_checkpoints_steps and save_checkpoints_secs are not set in constructor. If both save_checkpoints_steps and save_checkpoints_secs are None, then checkpoints are disabled.
• session_config: a ConfigProto used to set session parameters, or None.
• keep_checkpoint_max: The maximum number of recent checkpoint files to keep. As new files are created, older files are deleted. If None or 0, all checkpoint files are kept. Defaults to 5 (that is, the 5 most recent checkpoint files are kept.)
• keep_checkpoint_every_n_hours: Number of hours between each checkpoint to be saved. The default value of 10,000 hours effectively disables the feature.
• log_step_count_steps: The frequency, in number of global steps, that the global step/sec will be logged during training.

Raises:

• ValueError: If both save_checkpoints_steps and save_checkpoints_secs are set.

replace

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,

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.