tf.contrib.learn.RunConfig

Class `RunConfig`

Inherits From: RunConfig

Defined in tensorflow/contrib/learn/python/learn/estimators/run_config.py.

This class specifies the configurations for an Estimator run.

This class is a deprecated implementation of tf.estimator.RunConfig interface.

`init`

__init__(
    master=None,
    num_cores=0,
    log_device_placement=False,
    gpu_memory_fraction=1,
    tf_random_seed=None,
    save_summary_steps=100,
    save_checkpoints_secs=_USE_DEFAULT,
    save_checkpoints_steps=None,
    keep_checkpoint_max=5,
    keep_checkpoint_every_n_hours=10000,
    log_step_count_steps=100,
    protocol=None,
    evaluation_master='',
    model_dir=None,
    session_config=None
)

Constructor. (deprecated)

The superclass ClusterConfig may set properties like cluster_spec, is_chief, master (if None in the args), num_ps_replicas, task_id, and task_type based on the TF_CONFIG environment variable. See ClusterConfig for more details.

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:

master: TensorFlow master. Defaults to empty string for local.
num_cores: Number of cores to be used. If 0, the system picks an appropriate number (default: 0).
log_device_placement: Log the op placement to devices (default: False).
gpu_memory_fraction: Fraction of GPU memory used by the process on each GPU uniformly on the same machine.
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_secs: Save checkpoints every this many seconds. Can not be specified with save_checkpoints_steps.
save_checkpoints_steps: Save checkpoints every this many steps. Can not be specified with save_checkpoints_secs.
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.
evaluation_master: the master on which to perform evaluation.
model_dir: directory where model parameters, graph etc are saved. If None, will use model_dir property in TF_CONFIG environment variable. If both are set, must have same value. If both are None, see Estimator about where the model will be saved.
session_config: a ConfigProto used to set session parameters, or None. Note - using this argument, it is easy to provide settings which break otherwise perfectly good models. Use with care.
protocol: An optional argument which specifies the protocol used when starting server. None means default to grpc.

Properties

`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.

`environment`

`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_config`

`tf_random_seed`

`train_distribute`

Optional tf.contrib.distribute.DistributionStrategy for training.

Methods

`tf.contrib.learn.RunConfig.get_task_id`

get_task_id()

Returns task index from TF_CONFIG environmental variable.

If you have a ClusterConfig instance, you can just access its task_id property instead of calling this function and re-parsing the environmental variable.

Returns:

TF_CONFIG['task']['index']. Defaults to 0.

`tf.contrib.learn.RunConfig.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,
train_distribute,
device_fn,
protocol.
eval_distribute,
experimental_distribute,

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.

`tf.contrib.learn.RunConfig.uid`

uid(
    *args,
    **kwargs
)

Generates a 'Unique Identifier' based on all internal fields. (experimental)

Caller should use the uid string to check RunConfig instance integrity in one session use, but should not rely on the implementation details, which is subject to change.

Args:

whitelist: A list of the string names of the properties uid should not include. If None, defaults to _DEFAULT_UID_WHITE_LIST, which includes most properties user allowes to change.

Returns:

A uid string.

tf.contrib.learn.RunConfig

Class RunConfig

__init__

Args:

Properties

cluster_spec

device_fn

environment

eval_distribute

evaluation_master

global_id_in_cluster

Returns:

is_chief

keep_checkpoint_every_n_hours

keep_checkpoint_max

log_step_count_steps

master

model_dir

num_ps_replicas

num_worker_replicas

protocol

save_checkpoints_secs

save_checkpoints_steps

save_summary_steps

service

session_config

task_id

task_type

tf_config

tf_random_seed

train_distribute

Methods

tf.contrib.learn.RunConfig.get_task_id

Returns:

tf.contrib.learn.RunConfig.replace

Args:

Raises:

Returns:

tf.contrib.learn.RunConfig.uid

Args:

Returns:

Class `RunConfig`

`init`

`cluster_spec`

`device_fn`

`environment`

`eval_distribute`

`evaluation_master`

`global_id_in_cluster`

`is_chief`

`keep_checkpoint_every_n_hours`

`keep_checkpoint_max`

`log_step_count_steps`

`master`

`model_dir`

`num_ps_replicas`

`num_worker_replicas`

`protocol`

`save_checkpoints_secs`

`save_checkpoints_steps`

`save_summary_steps`

`service`

`session_config`

`task_id`

`task_type`

`tf_config`

`tf_random_seed`

`train_distribute`

`tf.contrib.learn.RunConfig.get_task_id`

`tf.contrib.learn.RunConfig.replace`

`tf.contrib.learn.RunConfig.uid`