contrib.learn.RunConfig

tf.contrib.learn.RunConfig

class tf.contrib.learn.RunConfig

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

See the guide: Learn (contrib) > Graph actions

This class specifies the configurations for an Estimator run.

This class is the implementation of ${tf.estimator.RunConfig} interface.

If you're a Google-internal user using command line flags with learn_runner.py (for instance, to do distributed training or to use parameter servers), you probably want to use learn_runner.EstimatorConfig instead.

Properties

cluster_spec

environment

evaluation_master

is_chief

keep_checkpoint_every_n_hours

keep_checkpoint_max

master

model_dir

num_ps_replicas

num_worker_replicas

save_checkpoints_secs

save_checkpoints_steps

save_summary_steps

session_config

task_id

task_type

tf_config

tf_random_seed

Methods

__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,
    evaluation_master='',
    model_dir=None,
    session_config=None
)

Constructor.

Note that 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.

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.
• 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.

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.

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,

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.

uid

uid(
    *args,
    **kwargs
)

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

THIS FUNCTION IS EXPERIMENTAL. It may change or be removed at any time, and without warning.

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 properites user allowes to change.

Returns:

A uid string.