def main_worker(args, unknown_args):
"""Runs main worker thread from model training."""
args, config = parse_args_uargs(args, unknown_args)
set_global_seed(args.seed)
prepare_cudnn(args.deterministic, args.benchmark)
config.setdefault("distributed_params", {})["apex"] = args.apex
config.setdefault("distributed_params", {})["amp"] = args.amp
expdir = Path(args.expdir)
# optuna objective
def objective(trial: optuna.trial):
trial, trial_config = _process_trial_config(trial, config.copy())
experiment, runner, trial_config = prepare_config_api_components(
expdir=expdir, config=trial_config)
# @TODO: here we need better solution.
experiment._trial = trial # noqa: WPS437
if experiment.logdir is not None and get_rank() <= 0:
dump_environment(trial_config, experiment.logdir, args.configs)
dump_code(args.expdir, experiment.logdir)
runner.run_experiment(experiment)
return runner.best_valid_metrics[runner.main_metric]
# optuna direction
direction = ("minimize" if config.get("stages", {}).get(
"stage_params", {}).get("minimize_metric", True) else "maximize")
# optuna study
study_params = config.pop("study_params", {})
# optuna sampler
sampler_params = study_params.pop("sampler_params", {})
optuna_sampler_type = sampler_params.pop("sampler", None)
optuna_sampler = (optuna.samplers.__dict__[optuna_sampler_type](
**sampler_params) if optuna_sampler_type is not None else None)
# optuna pruner
pruner_params = study_params.pop("pruner_params", {})
optuna_pruner_type = pruner_params.pop("pruner", None)
optuna_pruner = (optuna.pruners.__dict__[optuna_pruner_type](
**pruner_params) if optuna_pruner_type is not None else None)
study = optuna.create_study(
direction=direction,
storage=args.storage or study_params.pop("storage", None),
study_name=args.study_name or study_params.pop("study_name", None),
sampler=optuna_sampler,
pruner=optuna_pruner,
)
study.optimize(
objective,
n_trials=args.n_trials,
timeout=args.timeout,
n_jobs=args.n_jobs or 1,
gc_after_trial=args.gc_after_trial,
show_progress_bar=args.show_progress_bar,
)
def main_worker(args, unknown_args):
"""Runs main worker thread from model training."""
args, config = parse_args_uargs(args, unknown_args)
set_global_seed(args.seed)
prepare_cudnn(args.deterministic, args.benchmark)
config.setdefault("distributed_params", {})["apex"] = args.apex
config.setdefault("distributed_params", {})["amp"] = args.amp
experiment, runner, config = prepare_config_api_components(expdir=Path(
args.expdir),
config=config)
if experiment.logdir is not None and get_rank() <= 0:
dump_environment(config, experiment.logdir, args.configs)
dump_code(args.expdir, experiment.logdir)
runner.run_experiment(experiment)