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kratsg merged 2 commits into from
Mar 18, 2026
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docs: add mlcpu to jupyter documentation #95

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add mlcpu
kratsg Mar 18, 2026
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style: pre-commit fixes
pre-commit-ci[bot] Mar 18, 2026
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10 changes: 6 additions & 4 deletions docs/uchicago/jupyter.md
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Expand Up @@ -71,7 +71,9 @@ documentation, see the
You can install additional packages directly from your notebook with
[`pixi`](https://pixi.prefix.dev/latest/). The `ml_platform` image organizes
packages under features. For ML-related packages, use the `ml` feature (`-f ml`)
and install them with the `ml` environment (`-e ml`).
and install them with the `ml` environment (`-e ml`). If you are not using a
GPU-node, you can use the `mlcpu` environment which has the same set of packages
without the `cuda` system requirement.

**Example**: installing the GPU-version of `pytorch` along with `torchvision`
and `xgboost` available on [conda-forge](https://conda-forge.org/packages/), you
Expand Down Expand Up @@ -274,16 +276,16 @@ pixi install -e my-env
Choose the right approach for your needs to keep your environments manageable
and maintainable:

**When to use the simple approach** (add to `ml` feature):
**When to use the simple approach** (add to `ml` or `mlcpu` feature):

- You need just a few additional packages
- Packages are compatible with the existing `ml` environment
- Packages are compatible with the existing `ml` or `mlcpu` environment
- You're doing exploratory work or quick prototyping
- You don't need strict version control

**When to create a custom environment:**

- You need specific package versions that conflict with `ml`
- You need specific package versions that conflict with `ml` or `mlcpu`
- You're working on a long-term project with specific dependencies
- You want to isolate different projects from each other
- You're collaborating and need reproducible environments
Expand Down
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