Introduce optuna.artifacts to the PyTorch checkpoint example
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kAIto47802
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optuna.artifacts to the PyTorch checkpoint example
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@nabenabe0928 Could you review this PR? |
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not522
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Oct 9, 2024
pytorch/pytorch_checkpoint.py
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| checkpoint = torch.load(checkpoint_path) | ||
| if trial_number is not None: | ||
| study = optuna.load_study(study_name="pytorch_checkpoint", storage="sqlite:///example.db") | ||
| artifact_id = study.trials[trial_number].user_attrs["artifact_id"] |
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If the process is terminated before the first checkpoint, the artifact will not be saved, so check if it exists.
pytorch/pytorch_checkpoint.py
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| @@ -159,9 +158,15 @@ def objective(trial): | |||
| "optimizer_state_dict": optimizer.state_dict(), | |||
| "accuracy": accuracy, | |||
| }, | |||
| tmp_checkpoint_path, | |||
| "./tmp_model.pt", | |||
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Could you change the path of checkpoint for each trial? If we run this script with multi-process, the saved models can be broken by other processes.
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Co-authored-by: Naoto Mizuno <gobou522@gmail.com>
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Motivation
Currently, the PyTorch checkpoint example is using local file system to save and manage checkpoints, not yet reflecting the recent
optuna.artifactsfunctionalities.Description of the changes
optuna.artifacts.