tf.estimator.RunConfig

This class specifies the configurations for an Estimator run.

Used in the notebooks

Used in the guide Used in the tutorials

model_dir directory where model parameters, graph, etc are saved. If PathLike object, the path will be resolved. 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). If a saver is passed to the estimator, this argument will be ignored.
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 and the loss will be logged during training. Also controls the frequency that the global steps / s will be logged (and written to summary) during training.
train_distribute An optional instance of tf.distribute.Strategy. If specified, then Estimator will distribute the user's model during training, according to the policy specified by that strategy. Setting experimental_distribute.train_distribute is preferred.
device_fn A callable invoked for every Operation that takes the Operation and returns the device string. If None, defaults to the device function returned by tf.train.replica_device_setter with round-robin strategy.
protocol An optional argument which specifies the protocol used when starting server. None means default to grpc.
eval_distribute An optional instance of tf.distribute.Strategy. If specified, then Estimator will distribute the user's model during evaluation, according to the policy specified by that strategy. Setting experimental_distribute.eval_distribute is preferred.
experimental_distribute An optional tf.contrib.distribute.DistributeConfig object specifying DistributionStrategy-related configuration. The train_distribute and eval_distribute can be passed as parameters to RunConfig or set in experimental_distribute but not both.
experime