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