[Mac] Unable to find installation candidates for torch

[ALonelySheep]

Hi Lucas, thanks for this tutorial! But I encountered this error when trying to replicate your installation process:

Package operations: 1 install, 1 update, 0 removals

  - Updating torch (2.2.2 -> 2.2.2+cpu): Failed

  RuntimeError

  Unable to find installation candidates for torch (2.2.2+cpu)

I have installed torch by adding torch = "^2.2.2" in toml file before trying your method. Do you have any idea of what is happening here?

Thanks

[ALonelySheep]

The only significant difference in our toml file is that I'm using python=3.8 instead of 3.10

[lucaspar]

hey, so the repo's TOML already has an entry for torch, you shouldn't need to add it. Just change the versions in the original TOML to match v2.2 instead (it's 2.3).

[lucaspar]

I created a new branch that works on Python 3.8.

See the TOML file.

[ALonelySheep]

Hey Lucas, thanks you for taking time to address this. The issue seems to be system specific.
I'm using MacOS and this structure seems to work

torch = [
    { version = "~2.2.2", source = "pypi", platform = "darwin", markers = "extra=='cpu' and extra!='gpu'" },
    { version = "~2.2.2+cpu", source = "pytorch_cpu", platform = "linux", markers = "extra=='cpu' and extra!='gpu'" },
    { version = "~2.2.2+cpu", source = "pytorch_cpu", platform = "win32", markers = "extra=='cpu' and extra!='gpu'" },
]

[ALonelySheep]

Also as a caveat, if we add another package which depends on torch, its possible to break the hack mentioned in this repo. I think at its core, this is because Poetry does not support conflicting dependencies. More discussion can be found here: python-poetry/poetry#6419

[lucaspar]

right, I thought platform fields were enough for macs; are you also installing it in an environment with NVIDIA cards that you need the GPU markers?

As for dependencies that have torch itself as a requirement, that's something I haven't explored yet, thanks for bringing it up.

[ALonelySheep]

Yes, I'm trying to create a consistent TOML file which can work on both dev and production machines.

[ALonelySheep]

Sadly, I feel like I've wasted a lot of time on poetry, but the solution I ended up with is very unreliable. In the end, I opted to use a bash script to rename the correct file. I hope Poetry can have better support for ML environments in the future.

[lucaspar]

True, it seems all we have now are workarounds.

This is not even a "Poetry" issue: AFAIK no Python package manager today reliably replicates an environment across different platforms with conditional hardware acceleration. Conda's environment.yaml files perhaps get the closest, but without a proper .lock file I've had them fail miserably when it comes to reproducibility.

[pschoen-itsc]

Solution that works for me

torch = [
    { version = "==2.4.0", source = "pypi", markers = "sys_platform=='darwin'" },
    { version = "==2.4.0", source = "pytorch-cpu", markers = "sys_platform!='darwin' and extra=='cpu' and extra!='cuda'" }
]

[tool.poetry.group.cuda]
optional = true

[tool.poetry.group.cuda.dependencies]
torch = { version = "==2.4.0", markers = "sys_platform!='darwin' and extra=='cuda' and extra!='cpu'" }

pypi source is my own proxy repo of the public pypi, but that should not make a difference.
That is also my primary source, so the cuda torch should be also pulled from there.
I don't know for sure if everything is needed to make this work, but this does work.