def experiment(pattern, expt_conf): for i, param in enumerate(hyperparameters.keys()): expt_conf[param] = pattern[i] expt_conf['model_path'] = str( expt_dir / f"{'_'.join([str(p).replace('/', '-') for p in pattern])}.pth") expt_conf[ 'log_id'] = f"{'_'.join([str(p).replace('/', '-') for p in pattern])}" wav_path = Path( expt_conf['manifest_path']).resolve().parents[1] / 'wav' load_func = set_load_func(wav_path, expt_conf['sample_rate'], expt_conf['n_waves']) with mlflow.start_run(): mlflow.set_tag('target', expt_conf['target']) result_series, val_pred = typical_train(expt_conf, load_func, label_func, dataset_cls, groups, val_metrics) mlflow.log_params({ hyperparameter: value for hyperparameter, value in zip(hyperparameters.keys(), pattern) }) mlflow.log_artifacts(expt_dir) return result_series, val_pred
def experiment(pattern, cfg): for i, param in enumerate(hyperparameters.keys()): cfg = set_hyperparameter(cfg, param, pattern[i]) cfg.train.model.model_path = str( expt_dir / f"{'_'.join([str(p).replace('/', '-') for p in pattern])}.pth") cfg.train.log_id = f"{'_'.join([str(p).replace('/', '-') for p in pattern])}" with mlflow.start_run(): result_series, val_pred, _ = typical_train(cfg, load_func, label_func, process_func, dataset_cls, groups) mlflow.log_params({ hyperparameter: value for hyperparameter, value in zip(hyperparameters.keys(), pattern) }) return result_series, val_pred
def experiment(pattern, expt_conf): for i, param in enumerate(hyperparameters.keys()): expt_conf[param] = pattern[i] expt_conf['model_path'] = str( expt_dir / f"{'_'.join([str(p).replace('/', '-') for p in pattern])}.pth") expt_conf[ 'log_id'] = f"{'_'.join([str(p).replace('/', '-') for p in pattern])}" with mlflow.start_run(): result_series, val_pred, _ = typical_train(expt_conf, load_func, label_func, process_func, dataset_cls, groups) mlflow.log_params({ hyperparameter: value for hyperparameter, value in zip(hyperparameters.keys(), pattern) }) mlflow.log_artifacts(expt_dir) return result_series, val_pred