Thank you very much. The previously mentioned file does indeed run succesfully now. I did want to enable cudatoolkit I'm pretty sure. This is what I am now trying to get running:

from numba import cuda
import numpy as np

@cuda.jit
def cudakernel0(array):
    for i in range(array.size):
        array[i] += 0.5

array = np.array([0, 1], np.float32)
print('Initial array:', array)

print('Kernel launch: cudakernel0[1, 1](array)')
cudakernel0[1, 1](array)

print('Updated array:',array)

Which has this as the first error:

Initial array: [0. 1.]
Kernel launch: cudakernel0[1, 1](array)
/nix/store/7m7c6crkdbzmzcrbwa4l4jqgnwj8m92b-python3.9-numba-0.59.1/lib/python3.9/site-packages/numba/cuda/dispatcher.py:536: NumbaPerformanceWarning: Grid size 1 will likely result in GPU under-utilization due to low occupancy.
  warn(NumbaPerformanceWarning(msg))
Traceback (most recent call last):
  File "/nix/store/7m7c6crkdbzmzcrbwa4l4jqgnwj8m92b-python3.9-numba-0.59.1/lib/python3.9/site-packages/numba/cuda/cudadrv/nvvm.py", line 139, in __new__
    inst.driver = open_cudalib('nvvm')
  File "/nix/store/7m7c6crkdbzmzcrbwa4l4jqgnwj8m92b-python3.9-numba-0.59.1/lib/python3.9/site-packages/numba/cuda/cudadrv/libs.py", line 64, in open_cudalib
    return ctypes.CDLL(path)
  File "/nix/store/2j0l3b15gas78h9akrsfyx79q02i46hc-python3-3.9.19/lib/python3.9/ctypes/__init__.py", line 374, in __init__
    self._handle = _dlopen(self._name, mode)
OSError: libnvvm.so: cannot open shared object file: No such file or directory

However, I do have the related env vars set:

[nix-shell:/tmp/cuda]$ echo $NUMBAPRO_NVVM
/nix/store/0c8nf26hx9x9jxgj0s9bq10xg75nbfv0-cuda-merged-12.2/nvvm/lib64/libnvvm.so

[nix-shell:/tmp/cuda]$ echo $NUMBAPRO_LIBDEVICE
/nix/store/0c8nf26hx9x9jxgj0s9bq10xg75nbfv0-cuda-merged-12.2/nvvm/libdevice

A stackoverflow says those are outdated and CUDA_HOME needs to be set. I do set it to the same as CUDA_PATH, but it doesn't seem to help. http://numba.pydata.org/numba-doc/latest/cuda/overview.html#setting-cuda-installation-path talks about ignoring minor version paths, but I don't think that is when I directly set it. I also don't have any non-minor version paths in /nix/store/

This is now my shell.nix:

with import <nixpkgs> {
  config = {
    allowUnfree = true;
    cudaSupport = true;
  };
};

pkgs.mkShell {
   name = "cuda-env-shell";
   buildInputs = [
     git gitRepo gnupg autoconf curl
     procps gnumake util-linux m4 gperf unzip
     libGLU libGL
     xorg.libXi xorg.libXmu freeglut
     xorg.libXext xorg.libX11 xorg.libXv xorg.libXrandr zlib 
     ncurses5 stdenv.cc binutils
     python39
     python39Packages.numpy
     python39Packages.numba
     libstdcxx5
     cudaPackages.cudatoolkit
   ];
   shellHook = ''
      export CUDA_PATH=${pkgs.cudaPackages.cudatoolkit}
      export CUDA_HOME=${pkgs.cudatoolkit}
      export EXTRA_CCFLAGS="-I/usr/include"
      export NUMBAPRO_NVVM=${pkgs.cudatoolkit}/nvvm/lib64/libnvvm.so
      export NUMBAPRO_LIBDEVICE=${pkgs.cudatoolkit}/nvvm/libdevice
   '';          
}

I'm sorry if its a lot to ask, but I would really like to learn about this and get it working. Do you see anything wrong with my stuff still?

CUDA_PATH, but it doesn't seem to help. http://numba.pydata.org/numba-doc/latest/cuda/overview.html#setting-cuda-installation-path talks

Hmm are those used at build time or at runtime for jit?

In reply to @jeroenvb3:matrix.org

Thank you very much. The previously mentioned file does indeed run succesfully now. I did want to enable cudatoolkit I'm pretty sure. This is what I am now trying to get running:

from numba import cuda
import numpy as np

@cuda.jit
def cudakernel0(array):
    for i in range(array.size):
        array[i] += 0.5

array = np.array([0, 1], np.float32)
print('Initial array:', array)

print('Kernel launch: cudakernel0[1, 1](array)')
cudakernel0[1, 1](array)

print('Updated array:',array)

Which has this as the first error:

Initial array: [0. 1.]
Kernel launch: cudakernel0[1, 1](array)
/nix/store/7m7c6crkdbzmzcrbwa4l4jqgnwj8m92b-python3.9-numba-0.59.1/lib/python3.9/site-packages/numba/cuda/dispatcher.py:536: NumbaPerformanceWarning: Grid size 1 will likely result in GPU under-utilization due to low occupancy.
  warn(NumbaPerformanceWarning(msg))
Traceback (most recent call last):
  File "/nix/store/7m7c6crkdbzmzcrbwa4l4jqgnwj8m92b-python3.9-numba-0.59.1/lib/python3.9/site-packages/numba/cuda/cudadrv/nvvm.py", line 139, in __new__
    inst.driver = open_cudalib('nvvm')
  File "/nix/store/7m7c6crkdbzmzcrbwa4l4jqgnwj8m92b-python3.9-numba-0.59.1/lib/python3.9/site-packages/numba/cuda/cudadrv/libs.py", line 64, in open_cudalib
    return ctypes.CDLL(path)
  File "/nix/store/2j0l3b15gas78h9akrsfyx79q02i46hc-python3-3.9.19/lib/python3.9/ctypes/__init__.py", line 374, in __init__
    self._handle = _dlopen(self._name, mode)
OSError: libnvvm.so: cannot open shared object file: No such file or directory

However, I do have the related env vars set:

[nix-shell:/tmp/cuda]$ echo $NUMBAPRO_NVVM
/nix/store/0c8nf26hx9x9jxgj0s9bq10xg75nbfv0-cuda-merged-12.2/nvvm/lib64/libnvvm.so

[nix-shell:/tmp/cuda]$ echo $NUMBAPRO_LIBDEVICE
/nix/store/0c8nf26hx9x9jxgj0s9bq10xg75nbfv0-cuda-merged-12.2/nvvm/libdevice

A stackoverflow says those are outdated and CUDA_HOME needs to be set. I do set it to the same as CUDA_PATH, but it doesn't seem to help. http://numba.pydata.org/numba-doc/latest/cuda/overview.html#setting-cuda-installation-path talks about ignoring minor version paths, but I don't think that is when I directly set it. I also don't have any non-minor version paths in /nix/store/

This is now my shell.nix:

with import <nixpkgs> {
  config = {
    allowUnfree = true;
    cudaSupport = true;
  };
};

pkgs.mkShell {
   name = "cuda-env-shell";
   buildInputs = [
     git gitRepo gnupg autoconf curl
     procps gnumake util-linux m4 gperf unzip
     libGLU libGL
     xorg.libXi xorg.libXmu freeglut
     xorg.libXext xorg.libX11 xorg.libXv xorg.libXrandr zlib 
     ncurses5 stdenv.cc binutils
     python39
     python39Packages.numpy
     python39Packages.numba
     libstdcxx5
     cudaPackages.cudatoolkit
   ];
   shellHook = ''
      export CUDA_PATH=${pkgs.cudaPackages.cudatoolkit}
      export CUDA_HOME=${pkgs.cudatoolkit}
      export EXTRA_CCFLAGS="-I/usr/include"
      export NUMBAPRO_NVVM=${pkgs.cudatoolkit}/nvvm/lib64/libnvvm.so
      export NUMBAPRO_LIBDEVICE=${pkgs.cudatoolkit}/nvvm/libdevice
   '';          
}

I'm sorry if its a lot to ask, but I would really like to learn about this and get it working. Do you see anything wrong with my stuff still?

Do you see anything wrong with my stuff still?

I'm still unsure if all of the buildInputs are actually required for whatever your task is. Aside from that, it's probably best that you create a python wrapper (python3.withPackages (ps: with ps; [ numba ... ])) rather than rely on mkShell or whatever

If you build cudaPackages.cudatoolkit you'll see that it's actually a symlink farm which includes a lot of stuff you don't need. In particular:

❯ readlink -f result/nvvm/lib64/libnvvm.so.4.0.0
/nix/store/fby2d6b4jgfb8awwjhzdrd13r8vx7ilw-cuda_nvcc-12.2.140-bin/nvvm/lib64/libnvvm.so.4.0.0

...it would've been enough for you to use ${getBin cudaPackages.cuda_nvcc}/nvvm/lib64/libnvvm.so which is much smaller (whoops, that's a bug, we definitely didn't want this to end up in .bin, so expect this to change)

I would think runtime for jit, since there is a @cuda.jit operator on my python code. This is now my shell.nix, removed extra buildInputs, and used the wrapper, which had to be python39 to even get back to where I was:

  config = {
    allowUnfree = true;
    cudaSupport = true;
  };
};

let
  pythonEnv = pkgs.python39.withPackages(ps: with ps; [ numba numpy ]);
in
pkgs.mkShell {
   name = "cuda-env-shell";
   buildInputs = [
      pythonEnv
     libstdcxx5
     cudaPackages.cudatoolkit
   ];
   shellHook = ''
      export CUDA_PATH=${pkgs.cudaPackages.cudatoolkit}
      export CUDA_HOME=${pkgs.cudatoolkit}
      export EXTRA_CCFLAGS="-I/usr/include"
      export NUMBAPRO_NVVM=${pkgs.cudatoolkit}/nvvm/lib64/libnvvm.so
      export NUMBAPRO_LIBDEVICE=${pkgs.cudatoolkit}/nvvm/libdevice
      export LD_LIBRARY_PATH=${pkgs.cudatoolkit}/nvvm/lib64/libnvvm.so:$CUDA_HOME
      # source venv/bin/activate
      python test2.py
   '';          
}

If I go into python repl I can import numpy and numba, but I still get the same error when running an actual cuda script. This was the python code:

from numba import cuda
import numpy as np

@cuda.jit
def cudakernel0(array):
    for i in range(array.size):
        array[i] += 0.5

array = np.array([0, 1], np.float32)
print('Initial array:', array)

print('Kernel launch: cudakernel0[1, 1](array)')
cudakernel0[1, 1](array)

print('Updated array:',array)

I can run:

from numba import cuda
print(cuda.detect())

Also I doubt the linking of nvvm.so helps, since numba is now relying on the CUDA_HOME env var (https://numba.pydata.org/numba-doc/latest/cuda/overview.html).
What do you mean to use ${getBin...}, where could I use that?

export LD_LIBRARY_PATH=${pkgs.cudatoolkit}/nvvm/lib64/libnvvm.so:$CUDA_HOME

I'm pretty sure gilbc treats LD_LIBRARY_PATH as a list of directories only (so you'd want LD_LIBRARY_PATH=${pkgs.cudatoolkit}/nvvm/lib64/ instead)

Also I doubt the linking of nvvm.so helps, since numba is now relying on the CUDA_HOME env var

The error you posted earlier suggests that they just dlopen("libnvvm.so", ...)

In reply to @ss:someonex.net

export LD_LIBRARY_PATH=${pkgs.cudatoolkit}/nvvm/lib64/libnvvm.so:$CUDA_HOME

I'm pretty sure gilbc treats LD_LIBRARY_PATH as a list of directories only (so you'd want LD_LIBRARY_PATH=${pkgs.cudatoolkit}/nvvm/lib64/ instead)

That was true yes, it seems it does find it now. However it claims:

No supported GPU compute capabilities found. Please check your cudatoolkit version matches your CUDA version

nvcc --version gives 12.2, and nvidia-smi gives 12.4. This should be compatible as far as I'm aware. I'll see if I can get more info on this, unless you know that this is not supposed to be compatible and I need to adjust one of the versions.

In reply to @jeroenvb3:matrix.org

That was true yes, it seems it does find it now. However it claims:

No supported GPU compute capabilities found. Please check your cudatoolkit version matches your CUDA version

nvcc --version gives 12.2, and nvidia-smi gives 12.4. This should be compatible as far as I'm aware. I'll see if I can get more info on this, unless you know that this is not supposed to be compatible and I need to adjust one of the versions.

In reply to @ss:someonex.net
Could you gist a reproducing code and a full log?

https://gitlab.com/jeroenvb3/cuda-setup

I added the nix config, the python code, the output, and my nvidia driver info. If you need more system info please tell but I'd think this is all that will be unique. Thanks!