Integrated development environments (IDEs) are prevalent code-writing and debugging tools. However, they have yet to be widely adopted for launching machine learning (ML) experiments. This work aims to fill this gap by introducing JetTrain, an IDE-integrated tool that delegates specific tasks from an IDE to remote computational resources. A user can write and debug code locally and then seamlessly run it remotely using on-demand hardware. We argue that this approach can lower the entry barrier for ML training problems and increase experiment throughput.
@article{arxiv.2402.10857,
title = {JetTrain: IDE-Native Machine Learning Experiments},
author = {Artem Trofimov and Mikhail Kostyukov and Sergei Ugdyzhekov and Natalia Ponomareva and Igor Naumov and Maksim Melekhovets},
journal= {arXiv preprint arXiv:2402.10857},
year = {2024}
}