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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.
- - -
#### `tf.contrib.learn.RunConfig.__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='')` {#RunConfig.__init__}
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:
* <b>`master`</b>: TensorFlow master. Defaults to empty string for local.
* <b>`num_cores`</b>: Number of cores to be used. If 0, the system picks an
appropriate number (default: 0).
* <b>`log_device_placement`</b>: Log the op placement to devices (default: False).
* <b>`gpu_memory_fraction`</b>: Fraction of GPU memory used by the process on
each GPU uniformly on the same machine.
* <b>`tf_random_seed`</b>: Random seed for TensorFlow initializers.
Setting this value allows consistency between reruns.
* <b>`save_summary_steps`</b>: Save summaries every this many steps.
* <b>`save_checkpoints_secs`</b>: Save checkpoints every this many seconds. Can not
be specified with `save_checkpoints_steps`.
* <b>`save_checkpoints_steps`</b>: Save checkpoints every this many steps. Can not be
specified with `save_checkpoints_secs`.
* <b>`keep_checkpoint_max`</b>: 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.)
* <b>`keep_checkpoint_every_n_hours`</b>: Number of hours between each checkpoint
to be saved. The default value of 10,000 hours effectively disables
the feature.
* <b>`evaluation_master`</b>: the master on which to perform evaluation.
- - -
#### `tf.contrib.learn.RunConfig.cluster_spec` {#RunConfig.cluster_spec}
- - -
#### `tf.contrib.learn.RunConfig.environment` {#RunConfig.environment}
- - -
#### `tf.contrib.learn.RunConfig.evaluation_master` {#RunConfig.evaluation_master}
- - -
#### `tf.contrib.learn.RunConfig.get_task_id()` {#RunConfig.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.is_chief` {#RunConfig.is_chief}
- - -
#### `tf.contrib.learn.RunConfig.keep_checkpoint_every_n_hours` {#RunConfig.keep_checkpoint_every_n_hours}
- - -
#### `tf.contrib.learn.RunConfig.keep_checkpoint_max` {#RunConfig.keep_checkpoint_max}
- - -
#### `tf.contrib.learn.RunConfig.master` {#RunConfig.master}
- - -
#### `tf.contrib.learn.RunConfig.num_ps_replicas` {#RunConfig.num_ps_replicas}
- - -
#### `tf.contrib.learn.RunConfig.save_checkpoints_secs` {#RunConfig.save_checkpoints_secs}
- - -
#### `tf.contrib.learn.RunConfig.save_checkpoints_steps` {#RunConfig.save_checkpoints_steps}
- - -
#### `tf.contrib.learn.RunConfig.save_summary_steps` {#RunConfig.save_summary_steps}
- - -
#### `tf.contrib.learn.RunConfig.task_id` {#RunConfig.task_id}
- - -
#### `tf.contrib.learn.RunConfig.task_type` {#RunConfig.task_type}
- - -
#### `tf.contrib.learn.RunConfig.tf_config` {#RunConfig.tf_config}
- - -
#### `tf.contrib.learn.RunConfig.tf_random_seed` {#RunConfig.tf_random_seed}
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