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Add multiple machines from one project example #605

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74 changes: 72 additions & 2 deletions docs/design_proposals/multi_environment_proposal.md
Original file line number Diff line number Diff line change
Expand Up @@ -195,8 +195,9 @@ pixi run test
```

## Important links
- Initial writeup of the proposal: https://gist.github.com/0xbe7a/bbf8a323409be466fe1ad77aa6dd5428
- GitHub project: https://github.com/orgs/prefix-dev/projects/10

- Initial writeup of the proposal: [GitHub Gist by 0xbe7a](https://gist.github.com/0xbe7a/bbf8a323409be466fe1ad77aa6dd5428)
- GitHub project: [#10](https://github.com/orgs/prefix-dev/projects/10)

## Real world example use cases
??? tip "Polarify test setup"
Expand Down Expand Up @@ -345,3 +346,72 @@ pixi run test
EXPOSE 8080
CMD ["/usr/local/bin/pixi", "run", "--env", "prod", "serve"]
```

??? tip "Multiple machines from one project"
This is an example for an ML project that should be executable on a machine that supports `cuda` and `mlx`. It should also be executable on machines that don't support `cuda` or `mlx`, we use the `cpu` feature for this.
```toml title="pixi.toml"
[project]
name = "my-ml-project"
description = "A project that does ML stuff"
authors = ["Your Name <[email protected]>"]
channels = ["conda-forge", "pytorch"]
platforms = [] # there is no platform that supports all features

[tasks]
train-model = "python train.py"
evaluate-model = "python test.py"

[dependencies]
python = "3.11.*"
pytorch = {version = ">=2.0.1", channel = "pytorch"}
torchvision = {version = ">=0.15", channel = "pytorch"}
polars = ">=0.20,<0.21"
matplotlib-base = ">=3.8.2,<3.9"
ipykernel = ">=6.28.0,<6.29"

[feature.cuda]
platforms = ["win-64", "linux-64"]
channels = [{name = "nvidia", priority = "-1"}, "pytorch"]
system-requirements = {cuda = "12.1"}

[feature.cuda.tasks]
train-model = "python train.py --cuda"
evaluate-model = "python test.py --cuda"

[feature.cuda.dependencies]
pytorch-cuda = {version = "12.1.*", channel = "pytorch"}

[feature.mlx]
platforms = ["osx-arm64"]

[feature.mlx.tasks]
train-model = "python train.py --mlx"
evaluate-model = "python test.py --mlx"

[feature.mlx.dependencies]
mlx = ">=0.5.0,<0.6.0"

[feature.cpu]
platforms = ["win-64", "linux-64", "osx-64", "osx-arm64"]

[environments]
cuda = ["cuda"]
mlx = ["mlx"]
default = ["cpu"]
```

```shell title="Running the project on a cuda machine"
pixi run train-model --env cuda
# will execute `python train.py --cuda`
# fails if not on linux-64 or win-64 with cuda 12.1
```

```shell title="Running the project with mlx"
pixi run train-model --env mlx
# will execute `python train.py --mlx`
# fails if not on osx-arm64
```

```shell title="Running the project on a machine without cuda or mlx"
pixi run train-model
```
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