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cuda_path is set but cuda wasn't able to be loaded #37

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Karl48071 opened this issue Mar 15, 2023 · 6 comments
Open

cuda_path is set but cuda wasn't able to be loaded #37

Karl48071 opened this issue Mar 15, 2023 · 6 comments

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@Karl48071
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After doing the install on windows (CUDA, cudnn, onnxruntime-gpu), I get the following error when running deface...
CUDA_PATH is set but CUDA wasn't able to be loaded.

I've watched countless videos on setting correct PATHs and so forth and am continuing to have the issue.

@mdraw
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mdraw commented Apr 13, 2023

This may be related to microsoft/onnxruntime#13576
If you haven't tried that already I'd suggest installing it in a conda environment with python, cuda and cudnn already installed.

@mdraw
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mdraw commented Jul 3, 2023

I've recently tested deface on Windows and found that the new DirectML execution provider also works quite well. It should work on any GPU since it is based on the built-in Direct3D12 API of Windows. I don't know how it compares to CUDA in terms of speed but if you are still having issues with CUDA it may be worth a shot. You can install it with

$ pip install onnx onnxruntime-directml

@HaukeCornell
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HaukeCornell commented Aug 4, 2023

I had the same issue (windows). I tried multiple versions, and environment setups. I only got it running by installing a new anaconda environment and using anacondas supplied version of cuDDN and CUDA.

@StealUrKill
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After doing the install on windows (CUDA, cudnn, onnxruntime-gpu), I get the following error when running deface... CUDA_PATH is set but CUDA wasn't able to be loaded.

I've watched countless videos on setting correct PATHs and so forth and am continuing to have the issue.

python -m pip install --upgrade setuptools pip
python -m pip install deface
pip install nvidia-pyindex
pip install tf2onnx
pip install onnx onnxruntime-gpu ## if gpu with cuda exist
pip install onnxruntime-openvino ## intels acceleration

replace the v11.4 with the verison you have installed....IIRC 11.8 is max that this tool can use at the moment.

python -m pip install "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.4\graphsurgeon\graphsurgeon-0.4.5-py2.py3-none-any.whl"
python -m pip install "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.4\uff\uff-0.6.9-py2.py3-none-any.whl"
python -m pip install "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.4\onnx_graphsurgeon\onnx_graphsurgeon-0.3.12-py2.py3-none-any.whl"
python -m pip install "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.4\python\tensorrt-8.2.3.0-cp39-none-win_amd64.whl"

to use tensorrt

python import tensorrt

@StealUrKill
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StealUrKill commented Oct 4, 2023

you need cuda 11.X - the 8.9.4 works for me so far
https://developer.nvidia.com/rdp/cudnn-archive

if you want tensor as will the 8.6.16 GA version works
https://developer.nvidia.com/nvidia-tensorrt-8x-download

you will need the latest cuda 11.X toolkit --- its large at 3gb
https://developer.nvidia.com/cuda-toolkit-archive

@vertexgamer
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I've recently tested deface on Windows and found that the new DirectML execution provider also works quite well. It should work on any GPU since it is based on the built-in Direct3D12 API of Windows. I don't know how it compares to CUDA in terms of speed but if you are still having issues with CUDA it may be worth a shot. You can install it with

$ pip install onnx onnxruntime-directml

thanks, this worked

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