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Weights & Biases integrates with popular machine learning frameworks, cloud platforms, and workflow orchestration tools to help you track experiments, log metrics, and manage models seamlessly.

PyTorch Lightning

Integrate Weights & Biases with your PyTorch Lightning code to add experiment tracking to your pipeline.

HuggingFace Transformers

Optimize HuggingFace Transformer models with Weights & Biases for experiment tracking and model management.

Keras

Use Weights & Biases and Keras for machine learning experiment tracking, dataset versioning, and project collaboration.

YOLOv5

Use the “You Only Look Once” (aka YOLOv5) real-time object detection framework and Weights & Biases to track model metrics, inspect model outputs, and restart interrupted runs.
If the library you use is not supported natively, you can still integrate Weights & Biases using the Python SDK. See Add Weights & Biases to any library for best practices and implementation guidance.
Last modified on September 29, 2026