Runner

class opennmt.Runner(model, config, auto_config=None, mixed_precision=False, jit_compile=False, seed=None)

Class for running and exporting models.

Inherits from: builtins.object

__init__(model, config, auto_config=None, mixed_precision=False, jit_compile=False, seed=None)

Initializes the runner parameters.

Parameters

• model – A opennmt.models.Model instance to run or a callable that returns such instance.

• config – The run configuration.

• auto_config – If True, use automatic configuration values defined by model. If not set, the parameter is read from the run configuration.

• mixed_precision – Enable mixed precision.

• jit_compile – Compile the model with XLA when possible.

• seed – The random seed to set.

Raises

TypeError – if model is not a opennmt.models.Model instance or a callable.

property model

The opennmt.models.Model executed by this runner.

property model_dir

The active model directory.

train(num_devices=1, with_eval=False, checkpoint_path=None, hvd=None, return_summary=False, fallback_to_cpu=True, continue_from_checkpoint=False)

Runs the training loop.

Parameters

• num_devices – Number of devices to use for training.

• with_eval – Enable evaluation during training.

• checkpoint_path – The checkpoint path to load the model weights from.

• hvd – Optional Horovod module.

• return_summary – Return a summary of the training from this function.

• fallback_to_cpu – If no GPU is detected, allow the training to run on CPU.

• continue_from_checkpoint – Continue training from the checkpoint passed to checkpoint_path. Otherwise only the model weights are loaded.

Returns

The path to the final model directory and, if return_summary is set, a dictionary with various training statistics.

evaluate(features_file=None, labels_file=None, checkpoint_path=None)

Runs evaluation.

Parameters

• features_file – The input features file to evaluate. If not set, will load eval_features_file from the data configuration.

• labels_file – The output labels file to evaluate. If not set, will load eval_labels_file from the data configuration.

• checkpoint_path – The checkpoint path to load the model weights from.

Returns

A dict of evaluation metrics.

average_checkpoints(output_dir, max_count=8, checkpoint_paths=None)

Averages checkpoints.

Parameters

• output_dir – The directory that will contain the averaged checkpoint.

• max_count – The maximum number of checkpoints to average.

• checkpoint_paths – The list of checkpoints to average. If not set, the last max_count checkpoints of the current model directory are averaged.

Returns

The path to the directory containing the averaged checkpoint.

update_vocab(output_dir, src_vocab=None, tgt_vocab=None)

Updates model vocabularies.

Parameters

• output_dir – Directory where the update checkpoint will be saved.

• src_vocab – Path to the new source vocabulary.

• tgt_vocab – Path to the new tagret vocabulary.

Returns

Path to the new checkpoint directory.

infer(features_file, predictions_file=None, checkpoint_path=None, log_time=False)

Runs inference.

Parameters

• features_file – The file(s) to infer from.

• predictions_file – If set, predictions are saved in this file, otherwise they are printed on the standard output.

• checkpoint_path – Path to a specific checkpoint to load. If None, the latest is used.

• log_time – If True, several time metrics will be printed in the logs at the end of the inference loop.

export(export_dir, checkpoint_path=None, exporter=None)

Exports a model.

Parameters

• export_dir – The export directory.

• checkpoint_path – The checkpoint path to export. If None, the latest is used.

• exporter – A opennmt.utils.Exporter instance. Defaults to opennmt.utils.SavedModelExporter.

score(features_file, predictions_file=None, checkpoint_path=None, output_file=None)

Scores existing predictions.

Parameters

• features_file – The input file.

• predictions_file – The predictions file to score.

• checkpoint_path – Path to specific checkpoint to load. If None, the latest is used.

• output_file – The file where the scores are saved. Otherwise, they will be printed on the standard output.