Sayak Paul
Sayak works on diffusion models at Hugging Face. His day-to-day includes training and babysitting diffusion models for images and videos, working on the diffusers library, and collaborating on applied research ideas. Off the work, he likes to binge-watch Suits and ICML tutorials.
Session
In this talk, we will cover how to write effective test cases for machine learning (ML) libraries that are used by hundreds of thousands of users on a regular basis. Tests, despite their well-established need for trust and foolproofing, often get less prioritized. Later, this can wreak havoc on massive codebases, with a high likelihood of introducing breaking changes and other unpleasant situations. This talk deals with our approach to testing our ML libraries, which serve a wide user base. We will cover a wide variety of topics, including the mindset and the necessity of minimal-yet-sufficient testing, all the way up to sharing some practical examples of end-to-end test suites.