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.
Attila Szatmári Szegedi Tudományegyetem, Qusay Idrees Sarhan Department of Software Engineering, University of Szeged, Péter Attila Soha Department of Software Engineering, University of Szeged, Gergő Balogh Department of Software Engineering, University of Szeged, Árpád Beszédes Department of Software Engineering, University of Szeged
Niklas Krieger Institute of Software Engineering, University of Stuttgart, Sandro Speth Institute of Software Engineering, University of Stuttgart, Steffen Becker University of Stuttgart
Tim Kräuter Western Norway University of Applied Sciences, Patrick Stünkel Western Norway University of Applied Sciences, Adrian Rutle Western Norway University of Applied Sciences, Yngve Lamo Western Norway University of Applied Sciences