tf.contrib.learn.RunConfig

class tf.contrib.learn.RunConfig

See the guide: Learn (contrib) > Graph actions

This class specifies the configurations for an Estimator run.

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

num_ps_replicas

save_checkpoints_secs

save_checkpoints_steps

save_summary_steps

task_id

task_type

tf_config

tf_random_seed

Methods

__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=600, save_checkpoints_steps=None, keep_checkpoint_max=5, keep_checkpoint_every_n_hours=10000, evaluation_master='')

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.

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.

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