Model Training as Code
TL;DR Model training workflows have traditionally lived in notebooks and ad-hoc scripts, making them difficult to version, reproduce, and integrate with modern development practices. Treating model training as code means applying software engineering principles – version control, testing, CI/CD, and infrastructure as code – to machine learning pipelines. AI coding assistants like Cursor and GitHub Copilot excel at generating boilerplate training code, but they require careful validation. When you ask Copilot to scaffold a PyTorch training loop with mixed precision and gradient accumulation, verify the generated code against official documentation before running expensive GPU jobs. Continue.dev’s context awareness helps by pulling in your existing model definitions and data loaders, reducing the chance of incompatible generated code. ...
