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Comment by SubiculumCode | Hacker News Reader

As an aside, since I was trying to install CuPy the other day and was having issues.

Open projects on github often (at least superficially) require specific versions of Cuda Toolkit (and all the specialty nvidia packages e.g. cudann), Tensorflow, etc, and changing the default versions of these for each little project, or step in a processing chain, is ridiculous.

pyenv et al have really made local, project specific versions of python packages much easier to manage. But I haven't seen a similar type solution for cuda toolkit and associated packages, and the solutions I've encountered seem terribly hacky..but I'm sure though that this is a common issue, so what do people do?

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As a maintainer of CuPy and also as a user of several GPU-powered Python libraries, I empathize with the frustrations and difficulties here. Indeed, one thing CuPy values is to make the installation step as easy and universal as possible. We strive to keep the binary package footprint small (currently less than 100 MiB), keep dependencies to a minimum, support wide variety of platforms including Windows and aarch64, and do not require a specific CUDA Toolkit version.

If anyone reading this message has encountered a roadblock while installing CuPy, please reach out. I'd be glad to help you.

You can jam the argument cudatoolkit=1.2.3 when creating conda environments.

NB I’m using Miniforge.

One way to do it is to explicitly add the link to say, the pytorch+CUDA wheel from the pytorch repos in your requirements.txt instead of using the normal pypi package. Which also sucks because you then have to do some other tweaks to make your requirements.txt portable across different platforms...

(and you can't just add another index for pip to look for if you want to use python build so it has to be explicitly linked to the right wheel, which absolutely sucks especially since you cannot get the CUDA version from pypi)

Yes, you need to install the right version or Cupy hangs forever when installing via pip:

    pip install cupy-cuda12x

Ugh… docker containers. I also wish there was a simpler way but I don’t think there is.

this is not what I wanted to hear. NOT AT ALL. Please whisper sweet lies into my ears.

At the moment I’m working on a system to quickly replicate academic deep learning repos (papers) at scale. At least Amazon has a catalogue of prebuilt containers with cuda/pytorch combos. I still occasionally have an issue where the container works on my 3090 test bench but not on the T4 cloud node…

conda provides cudatoolkit and associated packages. Does this solve the situation?

The condos 200-employee threshold licence change is problematic for some.

As long as you stay out of the "defaults" and "anaconda" repos, you're not subject to that license. For my needs conda-forge and bioconda have everything. I'm not sure about the nvidia repo but I assume it's similar.

Actually all CUDA Toolkit libs are already available through the conda-forge channel: https://anaconda.org/conda-forge/cuda-cudart, https://anaconda.org/conda-forge/libcublas, etc.

Actually yes it does....except I seem to remember that it doesn't go back that far in cuda versions. I can't seem to find it again right now.

in real life everyone just uses containers, might not be the answer you want to hear though

I'm okay with containers generally, I think. Is this a situation where you put your code into the container and run it, or does the code make calls to the container's gpu?

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