The annotated PyTorch training loop
The article provides a detailed annotated walkthrough of a standard PyTorch training loop, explaining key components like the model, data loader, optimizer, loss function, and the training iteration steps including forward pass, backward pass, and parameter updates.
Background
- PyTorch is an open-source deep-learning framework created by Meta (Facebook's parent company). It is used by researchers and engineers to build and train neural networks (models that learn patterns from data). The "training loop" is the core code that iterates over data, makes predictions, calculates errors, and updates the model's internal parameters.