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Update Ray Argument #376

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Jun 21, 2024
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2 changes: 1 addition & 1 deletion requirements_parameter_search.txt
Original file line number Diff line number Diff line change
@@ -1,4 +1,4 @@
bayesian-optimization # ray[tune]
optuna # ray[tune]
ray[tune]
ray[tune]>=2.5.0
grpcio>=1.48.2 # Fix issue: https://github.com/ray-project/ray/issues/22518
39 changes: 26 additions & 13 deletions search_params.py
Original file line number Diff line number Diff line change
Expand Up @@ -262,6 +262,7 @@ def retrain_best_model(exp_name, best_config, best_log_dir, retrain):
def main():
parser = argparse.ArgumentParser()
parser.add_argument(
"-c",
"--config",
help="Path to configuration file (default: %(default)s). Please specify a config with all arguments in LibMultiLabel/main.py::get_config.",
)
Expand Down Expand Up @@ -302,7 +303,7 @@ def main():
config = init_search_params_spaces(config, parameter_columns, prefix="")
parser.set_defaults(**config)
config = AttributeDict(vars(parser.parse_args()))
# no need to include validation during parameter search
# Validation sets are mandatoray during parameter search
config.merge_train_val = False
config.mode = "min" if config.val_metric == "Loss" else "max"

Expand Down Expand Up @@ -343,20 +344,32 @@ def main():
Path(config.config).stem if config.config else config.model_name,
datetime.now().strftime("%Y%m%d%H%M%S"),
)
analysis = tune.run(
tune.with_parameters(train_libmultilabel_tune, **data),
search_alg=init_search_algorithm(config.search_alg, metric=f"val_{config.val_metric}", mode=config.mode),
scheduler=scheduler,
local_dir=config.result_dir,
num_samples=config.num_samples,
resources_per_trial={"cpu": config.cpu_count, "gpu": config.gpu_count},
progress_reporter=reporter,
config=config,
name=exp_name,

tuner = tune.Tuner(
tune.with_resources(
tune.with_parameters(train_libmultilabel_tune, **data),
resources={"cpu": config.cpu_count, "gpu": config.gpu_count},
),
param_space=config,
tune_config=tune.TuneConfig(
scheduler=scheduler,
num_samples=config.num_samples,
search_alg=init_search_algorithm(
search_alg=config.search_alg,
metric=f"val_{config.val_metric}",
mode=config.mode,
),
),
run_config=ray_train.RunConfig(
name=exp_name,
storage_path=config.result_dir,
progress_reporter=reporter,
),
)
results = tuner.fit()
# Save best model after parameter search.
best_trial = analysis.get_best_trial(metric=f"val_{config.val_metric}", mode=config.mode, scope="all")
retrain_best_model(exp_name, best_trial.config, best_trial.local_path, retrain=not config.no_retrain)
best_result = results.get_best_result(metric=f"val_{config.val_metric}", mode=config.mode, scope="all")
retrain_best_model(exp_name, best_result.config, best_result.path, retrain=not config.no_retrain)


if __name__ == "__main__":
Expand Down
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