JetTrain: IDE-Native Machine Learning Experiments
This addresses the problem of inefficient ML experiment workflows for developers using IDEs, but it is incremental as it adapts existing remote execution concepts to IDE integration.
The paper tackles the problem of launching machine learning experiments from integrated development environments (IDEs) by introducing JetTrain, a tool that delegates tasks from an IDE to remote computational resources, enabling users to write and debug code locally and run it remotely on-demand, which aims to lower the entry barrier and increase experiment throughput.
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.