wandb

Why we included this project

Most ML teams end up with a mess of scattered training logs, and Weights & Biases gives them one place to keep track of what actually happened. A few lines of Python logging code will send metrics, hyperparameters, gradients, and model files to a shared dashboard, where runs can be compared side by side and discussed with collaborators. It integrates with the usual training frameworks like PyTorch, TensorFlow, and Hugging Face, so it tends to drop into an existing pipeline without a rewrite. The tool also handles hyperparameter sweeps, artifact versioning, and Weave for debugging and evaluating LLM apps, which makes it useful beyond just tracking. For teams that care about reproducible runs and being able to look back at how a model evolved, it's a well-integrated and practical choice.