# Guide: Running long ML training jobs without interruptions

**URL:** <https://renku.discourse.group/t/guide-running-long-ml-training-jobs-without-interruptions/1215>\
**Category:** RenkuLab\
**Created:** [23 April 2026 12:24 UTC](https://renku.discourse.group/t/guide-running-long-ml-training-jobs-without-interruptions/1215 "2026-04-23T12:24:09Z")\
**Posts on this page:** 1\
**Page:** 1

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**Author:** ![elisabetc](https://yyz2.discourse-cdn.com/free1/user_avatar/renku.discourse.group/elisabetc/32/811_2.png) [@elisabetc](https://renku.discourse.group/u/elisabetc)\
**Post date:** [23 April 2026 12:24 UTC](https://renku.discourse.group/t/guide-running-long-ml-training-jobs-without-interruptions/1215/1 "2026-04-23T12:24:09Z")

</div>

If you have ever experienced a long-running training process die because of a Renku timeout, this guide is for you!

While Renku sessions stay active during CPU usage, the front-end application (JupyterLab/VSCode) may still stop long-running processes. To prevent this, we recommend using **tmux** , which is installed by default in all Renku [global environments and code-based environments](https://docs.renkulab.io/en/latest/docs/users/sessions/guides/environments/#creating-an-environment-for-your-session).

🧪 **Workflow**

1. Open your session terminal in Renku.
2. Start a new tmux session: `tmux`
3. Run your script: e.g. `python train.py`

You can now safely close your browser window. Your model will keep training in the background until completion.

📖 **Full documentation:** [Handle long training runs](https://docs.renkulab.io/en/latest/docs/users/sessions/guides/handle-long-training-runs/)