tff.learning.programs.train_model_with_vizier

Trains and tunes a federated model using Vizier.

tff.learning.programs.train_model_with_vizier(
    *,
    study,
    total_trials,
    num_parallel_trials=1,
    update_hparams,
    train_model_program_logic,
    train_process_factory,
    train_data_source,
    total_rounds,
    num_clients,
    program_state_manager_factory,
    model_output_manager_factory,
    train_metrics_manager_factory=None,
    evaluation_manager_factory,
    evaluation_periodicity
)

Args
`study`	The Vizier study to use to to tune `train_model_program_logic`.
`total_trials`	The number of Vizier trials.
`num_parallel_trials`	The number of Vizier trials to be evaluated in parallel. Default is 1.
`update_hparams`	A `tff.Computation` to use to update the models hparams using a trials parameters.
`train_model_program_logic`	The program logic to use for training and evaluating the model.
`train_process_factory`	A factory for creating `tff.learning.templates.LearningProcess` to run for training.
`train_data_source`	A `tff.program.FederatedDataSource` which returns client data used during training.
`total_rounds`	The number of rounds of training.
`num_clients`	The number of clients per round of training.
`program_state_manager_factory`	A factory for creating `tff.program.ProgramStateManager`s for each trail.
`model_output_manager_factory`	A factory for creating `tff.program.ReleaseManager`s used to release the model.
`train_metrics_manager_factory`	A factory for creating `tff.program.ReleaseManager`s used to release training metrics for each trail.
`evaluation_manager_factory`	A factory for creating `tff.learning.programs.EvaluationManager`s for each trail.
`evaluation_periodicity`	Either a integer number of rounds or `datetime.timedelta` to await before sending a new training checkpoint to `evaluation_manager.start_evaluation`.

tff.learning.programs.train_model_with_vizier

Args