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tinny-cuda wont install. CUDA not found
(nerfstudio) root@home:/home/sinicyn/Downloads# pip install git+https://github.com/NVlabs/tiny-cuda-nn/#subdirectory=bindings/torch Collecting git+https://github.com/NVlabs/tiny-cuda-nn/#subdirectory=bindings/torch Cloning https://github.com/NVlabs/tiny-cuda-nn/ to /tmp/pip-req-build-b8w1_isc Running command git clone --filter=blob:none --quiet https://github.com/NVlabs/tiny-cuda-nn/ /tmp/pip-req-build-b8w1_isc Resolved https://github.com/NVlabs/tiny-cuda-nn/ to commit 14053e9a87ebf449d32bda335c0363dd4f5667a4 Running command git submodule update --init --recursive -q Preparing metadata (setup.py) ... error error: subprocess-exited-with-error
× python setup.py egg_info did not run successfully.
│ exit code: 1
╰─> [10 lines of output]
/root/anaconda3/envs/nerfstudio/lib/python3.8/site-packages/torch/cuda/init.py:83: UserWarning: CUDA initialization: Unexpected error from cudaGetDeviceCount(). Did you run some cuda functions before calling NumCudaDevices() that might have already set an error? Error 803: system has unsupported display driver / cuda driver combination (Triggered internally at ../c10/cuda/CUDAFunctions.cpp:109.)
return torch._C._cuda_getDeviceCount() > 0
Traceback (most recent call last):
File "
note: This error originates from a subprocess, and is likely not a problem with pip. error: metadata-generation-failed
× Encountered error while generating package metadata. ╰─> See above for output.
note: This is an issue with the package mentioned above, not pip. hint: See above for details.
Where did you install CUDA_HOME and CUDA_PATH?
I'm trying to install in windows via conda Installed CUDA 11.3 now these errors
![image](https://user-images.githubusercontent.com/93261532/211033575-7a9b3d24-5245-438e-8497-2bf021d250c9.png)
I'm trying to install in windows via conda Installed CUDA 11.3 now these errors
![]()
Can I see a screenshot of your system and user environment variables
I'm trying to install in windows via conda Installed CUDA 11.3 now these errors
Can I see a screenshot of your system and user environment variables
![image](https://user-images.githubusercontent.com/93261532/211165251-6acd8294-8df4-4c27-854b-8dca93da4dc7.png)
![image](https://user-images.githubusercontent.com/93261532/211165277-508b5304-7a38-4e38-9b60-13183f3b3d79.png)
![image](https://user-images.githubusercontent.com/93261532/211165285-649fbbaf-3b0d-470e-b009-b3e33d52b4fe.png)
![image](https://user-images.githubusercontent.com/93261532/211165301-621e4aa8-93a8-4e77-866b-e12e166a55d9.png)
Try making a CUDA_HOME env variable with the same value as CUDA_PATH in both system and user variables (this way you can do it on both admin and user accounts)
Try making a CUDA_HOME env variable with the same value as CUDA_PATH in both system and user variables (this way you can do it on both admin and user accounts)
now these errors:
Try installing ninja like in this issue https://github.com/NVlabs/tiny-cuda-nn/issues/169#issuecomment-1291990467
I also ran into this problem and tried many solutions. I found the solution here #1177 and installed tiny-cuda-nn thanks to this comment.
To document what I have done to get nerfstudio via conda to work properly:
-
Installed CUDA 11.7 and VS 2019 and deinstalled all other versions
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Created a conda envoriment
conda create --name nerfstudio -y python=3.8
conda activate nerfstudio
python -m pip install --upgrade pip
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Installed pytorch
pip install torch==1.12.1+cu113 torchvision==0.13.1+cu113 -f https://download.pytorch.org/whl/torch_stable.html
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Git cloned tiny-cuda-nn via the repo into miniconda3\envs\nerfstudio\Lib\site-packages
git clone --recursive https://github.com/nvlabs/tiny-cuda-nn
cd tiny-cuda-nn
cmake . -B build
cmake --build build --config RelWithDebInfo -j
(cmake commands run in VS developer command prompt) -
Installed nerfstudio
pip install nerfstudio
-
Reinstalled tiny-cuda-nn via pip
pip install git+https://github.com/NVlabs/tiny-cuda-nn/#subdirectory=bindings/torch
How tiny-cuda-nn was installed may not makes sense, but that's what worked in the end. Nerfstudio is now fully functional.
Closing, feel free to reopen if it is still and issue.
This one worked for me but I had to install CUDA 12.1.1 before runing cmake . -B build