Supported RunConfig Options

model_dir

Model directory. Default value: None.

If model_dir set in NPURunConfig is different from that in NPUEstimator, an error is reported.

If either NPURunConfig or NPUEstimator is configured with model_dir, the configured path applies.

If neither NPURunConfig nor NPUEstimator is configured with model_dir, a model_dir_xxxxxxxxxx directory is created in the current script execution path to save the model file.

tf_random_seed

Seed of the initialization variable. Default value: None.

save_summary_steps

Interval (in steps) for saving the summary. Defaults to 0.

Applies only to the scenario where iterations_per_loop = 1. If iterations_per_loop > 1, the configured value may not be saved. For details about how to save information, see "Log and Summary Operators."

save_checkpoints_steps

Interval (in steps) for saving the checkpoints. Default value: None.

• This option is mutually exclusive with save_checkpoints_secs.
• If save_checkpoints_steps and save_checkpoints_secs are set to None, the checkpoints are saved every 100 steps.
• If the value of iterations_per_loop is greater than 1, set save_checkpoints_steps to a positive integer multiple of iterations_per_loop. Failure to do so may lead to checkpoint data not saved as defined by save_checkpoints_steps.

To save the checkpoint data on only a specific device, modify the training script as follows:

Original TensorFlow code:

self._classifier=tf.estimator.Estimator(
  model_fn=cnn_model_fn,
  model_dir=self._model_dir,
  config=tf.estimator.RunConfig(
      save_checkpoints_steps=50 if hvd.rank() == 0 else None,
      keep_checkpoint_max=1))

Code after porting:

self._classifier=NPUEstimator(
  model_fn=cnn_model_fn,
  model_dir=self._model_dir,
  config=tf.estimator.NPURunConfig(
      save_checkpoints_steps=50 if get_rank_id() == 0 else 0,
      keep_checkpoint_max=1))

save_checkpoints_secs

Interval (in seconds) for saving the checkpoints. Default value: None.

This option is mutually exclusive with save_checkpoints_steps.

session_config

ConfigProto object of session configuration. Default value: None.

keep_checkpoint_max

Maximum number of checkpoint files that can be stored. Defaults to 5.

keep_checkpoint_every_n_hours

Checkpoint file saving duration in hours. Defaults to 10000. This function can be disabled.

To use this function, set keep_checkpoint_max to a large value.

log_step_count_steps

Interval (in steps) for recording global_step and loss values. Defaults to 100.

Applies only to the scenario where iterations_per_loop = 1. If iterations_per_loop > 1, the configured value may not be saved. For details about how to save information, see "Log and Summary Operators."

train_distribute

Distributed training enable. The distributed configuration is specified by experimental_distribute.

This option is used only by TensorFlow Adapter. You are advised not to set it.

device_fn

Function of the Device field of each operation.

protocol

(Optional) Protocol used to start the server. If the option is empty, the gRPC is used by default.

eval_distribute

Distributed evaluation enable. The distributed configuration is specified by experimental_distribute.

This option is used only by TensorFlow Adapter. You are advised not to set it.

experimental_distribute

Distributed configuration.