# JetTrain: IDE-Native Machine Learning Experiments

arXiv: https://arxiv.org/abs/2402.10857 · HTML: https://mlsanity.com/paper/2402.10857/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 14 references that are in the map.
- First posted on arXiv 16 Feb 2024.

## Builds on (most cited first, 2 of 2)

- [Ray: A Distributed Framework for Emerging AI Applications](https://mlsanity.com/paper/1712.05889/) (2017)
- [Reasonable Scale Machine Learning with Open-Source Metaflow](https://mlsanity.com/paper/2303.11761/) (2023)

## Built on by (most cited first, 1 of 1)

- [In-IDE Toolkit for Developers of AI-Based Features](https://mlsanity.com/paper/2605.14612/) (2026)

## Abstract (arXiv)

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.