The system of record for
your model training
Track, compare, and visualize your ML models with 5 lines of code
Quickly and easily implement experiment logging by adding just a few lines to your script and start logging results. Our lightweight integration works with any Python script.
Visualize and compare every experiment
See model metrics stream live into interactive graphs and tables. It is easy to see how your latest ML model is performing compared to previous experiments, no matter where you are training your models.
Quickly find and re-run previous model checkpoints
W&B's experiment tracking saves everything you need to reproduce models later— the latest git commit, hyperparameters, model weights, and even sample test predictions. You can save experiment files and datasets directly to W&B or store pointers to your own storage.
from transformers import DebertaV2ForQuestionAnswering
Monitor your CPU and GPU usage
Visualize live metrics like GPU utilization to identify training bottlenecks and avoid wasting expensive resources.
Debug performance in real time
See how your model is performing and identify problem areas during training. We support rich media including images, video, audio, and 3D objects.
Dataset versioning with deduplication 100GB free storage
Automatically version logged datasets, with diffing and deduplication handled by Weights & Biases, behind the scenes.
Check the latest training model and results on desktop and mobile. Use collaborative hosted projects to coordinate across your team.
The Science of Debugging with W&B Reports
By Sarah Jane of Latent Space
We use Weights & Biases as a way to share results and learnings such that we can build on top of each other's work. The W&B Reports feature has been one of the most critical...
Seamlessly share progress across projects
Manage team projects with a lightweight system of record. It's easy to hand off projects when every experiment is automatically well documented and saved centrally.