tff.learning.programs.train_model

Runs specified rounds of training and optionally evaluates the model.

tff.learning.programs.train_model(
    *,
    train_process,
    train_data_source,
    train_per_round_clients,
    train_total_rounds,
    program_state_manager,
    model_output_manager,
    train_metrics_manager=None,
    evaluation_manager,
    evaluation_periodicity
) -> bool

This method will create an initial training state and repeatedly call train_process.next, advancing the state of the training process. Depending on the configuration of evaluation_manager, asynchronous evaluation loops will be spawned and executed in parallel.

This method will save the initial state (result of train_process.initialize) using program_state_manager. If the state manager is configured to keep the first version (e.g. tff.program.FileStateProgramManager's keep_first parameter), then round zero (the initialization) will be retained so that future experiments can use the same starting point.

Args
`train_process`	A `tff.learning.templates.LearningProcess` to run for training.
`train_data_source`	A `tff.program.FederatedDataSource` which returns client data used during training.
`train_per_round_clients`	The number of clients per round of training.
`train_total_rounds`	Total number of rounds of training.
`program_state_manager`	A `tff.program.ProgramStateManager` used to save program state for fault tolerance.
`model_output_manager`	A `tff.program.ReleaseManager` to release the model, the results can be used for building inference models after training, or warm-starting future training loops.
`train_metrics_manager`	A `tff.program.ReleaseManager` to release metrics of training. Use `tff.program.GroupingReleaseManager` to supply multiple release managers.
`evaluation_manager`	An `EvaluationManager` used to create a state manager for each evaluation loop that is forked off from the training loop.
`evaluation_periodicity`	Either a integer number of rounds or `datetime.timedelta` to await before sending a new training checkpoint to `evaluation_manager.start_evaluation`. Note that the last training round will always be evaluated even if it does not satisfy the periodicity.

tff.learning.programs.train_model

Args