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0 pointsibic1y ago0 comments

I'm not sure if I got your issue, but I can do platform-dependent `index` `pytorch` installation using the following snippet in `pyproject.toml` and `uv sync` just handles it accordingly.

[tool.uv.sources] torch = [{ index = "pytorch-cu124", marker = "sys_platform == 'win32'" }]

0 comments

satvikpendem1y ago

Some Windows machines have compatible GPUs while others don't, so this doesn't necessarily help. What is really required is querying the OS for what type of compute unit it has and then installing the right version of an ML library, but I'm not sure that will be done.

fluidcruft1y ago

Even without query, just setting an environment variable or having remember which extras are already applied to the already synced .venv some way.

j / k navigate · click thread line to collapse

0 pointsibic1y ago0 comments

I'm not sure if I got your issue, but I can do platform-dependent `index` `pytorch` installation using the following snippet in `pyproject.toml` and `uv sync` just handles it accordingly.

[tool.uv.sources] torch = [{ index = "pytorch-cu124", marker = "sys_platform == 'win32'" }]

0 comments

satvikpendem1y ago

fluidcruft1y ago

Even without query, just setting an environment variable or having remember which extras are already applied to the already synced .venv some way.

j / k navigate · click thread line to collapse