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Every model must have non-regression tests comparing outputs against reference data saved in .pth files, using realistic tensor shapes and pytest parameterization.
Open-source deep-learning framework for building, training, and fine-tuning deep learning models using state-of-the-art Physics-ML methods
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Every model must have non-regression tests comparing outputs against reference data saved in .pth files, using realistic tensor shapes and pytest parameterization.
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Every model must have tests that load from checkpoint files (.mdlus), verify attributes, and compare outputs against reference data to ensure serialization works correctly.
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Avoid string-based class selection with many options (>3 choices) in model constructors; prefer dependency injection with instances for better type safety and clearer APIs.
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Avoid splatted kwargs (kwargs) in model constructors; use explicit Dict parameters instead to prevent naming conflicts and make APIs clearer.
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Use checkminversion() to check optional dependencies without importing, and @requireversion decorator to protect version-specific features; pyproject.toml is the single source of truth for dependencies.