How is W&B different from TensorBoard?

W&B integrates with TensorBoard and improves experiment tracking tools. The founders created W&B to address common frustrations faced by TensorBoard users. Key improvements include:

1. Model Reproducibility: W&B facilitates experimentation, exploration, and model reproduction. It captures metrics, hyperparameters, code versions, and saves model checkpoints to ensure reproducibility.

2. Automatic Organization: W&B streamlines project handoffs and vacations by providing an overview of all attempted models, which saves time by preventing the re-execution of old experiments.

3. Quick Integration: Integrate W&B into your project in five minutes. Install the free open-source Python package and add a few lines of code. Logged metrics and records appear with each model run.

4. Centralized Dashboard: Access a consistent dashboard regardless of where training occurs—locally, on lab clusters, or cloud spot instances. Eliminate the need to manage TensorBoard files across different machines.

5. Robust Filtering Table: Search, filter, sort, and group results from various models efficiently. Easily identify the best-performing models for different tasks, an area where TensorBoard often struggles with larger projects.

6. Collaboration Tools: W&B enhances collaboration for complex machine learning projects. Share project links and utilize private teams for result sharing. Create reports with interactive visualizations and markdown descriptions for work logs or presentations.