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- Added comprehensive specification documents for the Mac M2 base pipeline implementation, including requirements, data model, and implementation plan. - Created OpenAPI contract for training and evaluation endpoints, detailing request and response schemas. - Developed a detailed quickstart guide for environment setup, testing, and running training and evaluation commands. - Established a research document outlining best practices for PyTorch on MPS and pytest marker naming conventions. - Implemented a data model defining key entities such as Run, EnvStamp, PredictionArtifact, and TensorBoardLog. - Introduced a task management document to organize implementation phases and user stories for the project. - Created initial smoke tests for model training and validation, ensuring compatibility with the specified pipeline.
…ort, and output path utilities
…p and environment management
…ion notes, and checkpoint validation
…rove error handling in training and evaluation scripts
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