Ehh, is it cool and time savings that it figured it out? Yes. But the solution was to get a “better” version prebuilt wheel package of PyTorch. This is a relatively “easy” problem to solve (figuring out this was the problem does take time). But it’s (probably, I can’t afford one) going to be painful when you want to upgrade the cuda version or specify a specific version. Unlike a typical PC, you’re going to need to build a new image and flash it. I would be more impressed when a LLM can do this end to end for you.
Pytorch + CUDA is a headache I've seen a lot of people have at my uni, and one I've never had to deal with thanks to uv. Good tooling really does go a long way in these things.
Although, I must say that for certain docker pass through cases, the debugging logs just aren't as detailed
Yup. The beauty of it is that the underlying ai accelerator/hardware is completely abstracted away. There’s a CoreML ONNX execution provider, though I haven’t used it.
No more fighting with hardcoded cuda:0 everywhere.
The only pain point is that you’ll often have to manually convert a PyTorch model from huggingface to onnx unless it’s very popular.
