Machine learning PhD students are in a unique position: they often need to run large-scale experiments to conduct state-of-the-art research but they don't have the support of the platform teams that industrial ML engineers can rely on. As former PhD students ourselves, we recount our hands-on experience with these challenges and explain how open-source tools like Determined would have made grad school a lot less painful. When we started graduate school as PhD students at Carnegie Mellon University (CMU), we thought the challenge laid in having novel ideas, testing hypotheses, and presenting research. Instead, the most difficult part was building out the tooling and infrastructure needed to run deep learning experiments. While industry labs like Google Brain and FAIR have teams of engineers to provide this kind of support, independent researchers and graduate students are left to manage on their own.
May-24-2022, 18:28:10 GMT