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IJulia CUDA does not run via jupyter notebooks on Linux Desktop #1015

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@pjgoodall

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@pjgoodall

Julia CUDA does not run via jupyter on Ubuntu Desktop

I have spent two days re-configuring system to try and get this working. I discovered this problem when trying to follow the Juliacon21-gpu_workshop I have had a discussion on discourse.julialang.com/GPU

It think this issue is related: libcuda.so.1 is missing with conda env tf-gpu #11743

I believe my evidence demonstrates the jupyter notebook using julia package IJulia is not using the same method to find libcuda as the repl.

I can execute PyTorch GPU access from a python 3.9 notebook, so i think that points to IJulia doing something unconventional.

Evidence

in the repl:

j% julia
               _
   _       _ _(_)_     |  Documentation: https://docs.julialang.org
  (_)     | (_) (_)    |
   _ _   _| |_  __ _   |  Type "?" for help, "]?" for Pkg help.
  | | | | | | |/ _` |  |
  | | |_| | | | (_| |  |  Version 1.6.2 (2021-07-14)
 _/ |\__'_|_|_|\__'_|  |  Official https://julialang.org/ release
|__/                   |

julia> using Libdl

julia> Libdl.find_library("libcuda")
"libcuda"

In the notebook with a freshly reset kernel
I get an empty string as a result - indication of failure to find the library

With jupyterlab installed locally

% conda create -n jupyter-julia python=3.9
% conda activate jupyter-julia
% mamba install -c conda-forge jupyterlab 

Check julia in this environment

(jupyter-julia) 
% julia --version
julia version 1.6.2
julia> using Libdl
julia> Libdl.find_library("libcuda")
julia> using Libdl

julia> Libdl.find_library("libcuda")
"libcuda"

julia> using CUDA

julia> CUDA.versioninfo()
CUDA toolkit 11.3.1, artifact installation
CUDA driver 11.4.0
NVIDIA driver 470.57.2

Libraries: 
- CUBLAS: 11.5.1
- CURAND: 10.2.4
- CUFFT: 10.4.2
- CUSOLVER: 11.1.2
- CUSPARSE: 11.6.0
- CUPTI: 14.0.0
- NVML: 11.0.0+470.57.2
- CUDNN: 8.20.0 (for CUDA 11.3.0)
- CUTENSOR: 1.3.0 (for CUDA 11.2.0)

Toolchain:
- Julia: 1.6.2
- LLVM: 11.0.1
- PTX ISA support: 3.2, 4.0, 4.1, 4.2, 4.3, 5.0, 6.0, 6.1, 6.3, 6.4, 6.5, 7.0
- Device capability support: sm_35, sm_37, sm_50, sm_52, sm_53, sm_60, sm_61, sm_62, sm_70, sm_72, sm_75, sm_80

1 device:
  0: NVIDIA GeForce GTX 1050 Ti (sm_61, 3.337 GiB / 3.938 GiB available)

From the newly installed Jupyterlab system with a 1.6 kernel,

using Libdl
Libdl.find_library("libcuda")
""

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