JetTrain: IDE-Native Machine Learning Experiments
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2024
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866917591352606720 |
|---|---|
| author | Trofimov, Artem Kostyukov, Mikhail Ugdyzhekov, Sergei Ponomareva, Natalia Naumov, Igor Melekhovets, Maksim |
| author_facet | Trofimov, Artem Kostyukov, Mikhail Ugdyzhekov, Sergei Ponomareva, Natalia Naumov, Igor Melekhovets, Maksim |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_10857 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | JetTrain: IDE-Native Machine Learning Experiments Trofimov, Artem Kostyukov, Mikhail Ugdyzhekov, Sergei Ponomareva, Natalia Naumov, Igor Melekhovets, Maksim Software Engineering Machine Learning 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. |
| title | JetTrain: IDE-Native Machine Learning Experiments |
| topic | Software Engineering Machine Learning |
| url | https://arxiv.org/abs/2402.10857 |