The Science of Debugging with W&B Reports


At Latent Space, we're working on building the first fully AI-rendered 3D engine. In order to do so, we're pushing the frontier of generative modeling with a focused group of researchers and engineers.

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 items for us as a team, since we treat reports as if they were mini-research papers to showcase results (sometimes a collection of reports actually becomes a paper!). In particular, using Reports has been helpful in our daily process to quickly identify issues and debug our models.

In this report, we'll go through how we use W&B Reports to quickly identify, communicate, and iteratively debug our models. You'll see a couple of the qualitative and quantitative metrics we observe when training a toy generative model and how one of the runs differs from the baseline in both metrics. By the end of the report, you'll get a lens into how we use W&B Logging in addition to Reports to diagnose and treat our models, as well as broadcast the results.

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