def main_worker(args, unknown_args):
"""Runs main worker thread from model training."""
args, config = utils.parse_args_uargs(args, unknown_args)
utils.set_global_seed(args.seed)
utils.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 = utils.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 utils.get_rank() <= 0:
utils.dump_environment(trial_config, experiment.logdir,
args.configs)
utils.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 sampler
sampler_params = config.pop("optuna_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 = config.pop("optuna_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,
study_name=args.study_name,
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):
args, config = utils.parse_args_uargs(args, unknown_args)
utils.set_global_seed(args.seed)
utils.prepare_cudnn(args.deterministic, args.benchmark)
config.setdefault("distributed_params", {})["apex"] = args.apex
Experiment, Runner = utils.import_experiment_and_runner(Path(args.expdir))
runner_params = config.get("runner_params", {})
experiment = Experiment(config)
runner = Runner(**runner_params)
if experiment.logdir is not None and get_rank() <= 0:
utils.dump_environment(config, experiment.logdir, args.configs)
utils.dump_code(args.expdir, experiment.logdir)
runner.run_experiment(experiment)
def main(args, unknown_args):
"""Run the ``catalyst-dl run`` script"""
args, config = utils.parse_args_uargs(args, unknown_args)
utils.set_global_seed(args.seed)
utils.prepare_cudnn(args.deterministic, args.benchmark)
Experiment, Runner = utils.import_experiment_and_runner(Path(args.expdir))
runner_params = config.pop("runner_params", {}) or {}
experiment = Experiment(config)
runner = Runner(**runner_params)
if experiment.logdir is not None:
utils.dump_environment(config, experiment.logdir, args.configs)
utils.dump_code(args.expdir, experiment.logdir)
check_run = safitty.get(config, "args", "check", default=False)
runner.run_experiment(experiment, check=check_run)
def main_worker(args, unknown_args):
"""Runs main worker thread from model training."""
args, config = utils.parse_args_uargs(args, unknown_args)
utils.set_global_seed(args.seed)
utils.prepare_cudnn(args.deterministic, args.benchmark)
config.setdefault("distributed_params", {})["apex"] = args.apex
config.setdefault("distributed_params", {})["amp"] = args.amp
experiment, runner, config = utils.prepare_config_api_components(
expdir=Path(args.expdir), config=config
)
if experiment.logdir is not None and utils.get_rank() <= 0:
utils.dump_environment(config, experiment.logdir, args.configs)
utils.dump_code(args.expdir, experiment.logdir)
runner.run_experiment(experiment)
def main_worker(args, unknown_args):
"""@TODO: Docs. Contribution is welcome."""
args, config = utils.parse_args_uargs(args, unknown_args)
utils.set_global_seed(args.seed)
utils.prepare_cudnn(args.deterministic, args.benchmark)
config.setdefault("distributed_params", {})["apex"] = args.apex
experiment_fn, runner_fn = utils.import_experiment_and_runner(
Path(args.expdir))
if experiment_fn is None:
experiment_params = config.get("experiment_params", {})
experiment = experiment_params.get("experiment", "Experiment")
experiment_fn = EXPERIMENTS.get(experiment)
runner_params = config.get("runner_params", {})
experiment = experiment_fn(config)
runner = runner_fn(**runner_params)
if experiment.logdir is not None and get_rank() <= 0:
utils.dump_environment(config, experiment.logdir, args.configs)
utils.dump_code(args.expdir, experiment.logdir)
runner.run_experiment(experiment)