Is a WSL2 + nvidia-docker environment supported?

Hello,

I tried to install stable-dreamfusion in a WSL2 + nvidia-docker environment, the following error occurred. The procedure of Build image in Docker/README.md has been executed.

 => CACHED [13/16] RUN pip3 install git+https://github.com/NVlabs/nvdiffrast/                                      0.0s
 => ERROR [14/16] RUN pip3 install git+https://github.com/NVlabs/tiny-cuda-nn/#subdirectory=bindings/torch        18.3s
------
 > [14/16] RUN pip3 install git+https://github.com/NVlabs/tiny-cuda-nn/#subdirectory=bindings/torch:
#17 1.107 Collecting git+https://github.com/NVlabs/tiny-cuda-nn/#subdirectory=bindings/torch
#17 1.107   Cloning https://github.com/NVlabs/tiny-cuda-nn/ to /tmp/pip-req-build-1p1uv4so
#17 1.107   Running command git clone -q https://github.com/NVlabs/tiny-cuda-nn/ /tmp/pip-req-build-1p1uv4so
#17 6.520   Running command git submodule update --init --recursive -q
#17 18.23     ERROR: Command errored out with exit status 1:
#17 18.23      command: /usr/bin/python3 -c 'import sys, setuptools, tokenize; sys.argv[0] = '"'"'/tmp/pip-req-build-1p1uv4so/bindings/torch/setup.py'"'"'; __file__='"'"'/tmp/pip-req-build-1p1uv4so/bindings/torch/setup.py'"'"';f=getattr(tokenize, '"'"'open'"'"', open)(__file__);code=f.read().replace('"'"'\r\n'"'"', '"'"'\n'"'"');f.close();exec(compile(code, __file__, '"'"'exec'"'"'))' egg_info --egg-base /tmp/pip-req-build-1p1uv4so/bindings/torch/pip-egg-info
#17 18.23          cwd: /tmp/pip-req-build-1p1uv4so/bindings/torch
#17 18.23     Complete output (7 lines):
#17 18.23     Traceback (most recent call last):
#17 18.23       File "<string>", line 1, in <module>
#17 18.23       File "/tmp/pip-req-build-1p1uv4so/bindings/torch/setup.py", line 30, in <module>
#17 18.23         raise EnvironmentError("Unknown compute capability. Specify the target compute capabilities in the TCNN_CUDA_ARCHITECTURES environment variable or install PyTorch with the CUDA backend to detect it automatically.")
#17 18.23     OSError: Unknown compute capability. Specify the target compute capabilities in the TCNN_CUDA_ARCHITECTURES environment variable or install PyTorch with the CUDA backend to detect it automatically.
#17 18.23     No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
#17 18.23     Building PyTorch extension for tiny-cuda-nn version 1.6
#17 18.23     ----------------------------------------
#17 18.31 ERROR: Command errored out with exit status 1: python setup.py egg_info Check the logs for full command output.
------
executor failed running [/bin/sh -c pip3 install git+https://github.com/NVlabs/tiny-cuda-nn/#subdirectory=bindings/torch]: exit code: 1

[pureexe]

Maybe out of the topic for WSL.

But I’m running this repo on Windows by

1. Install Visual studio with “Desktop development with C++”

2. Once you have finished installing Visual studio. Windows Terminal (wt.exe) will introduce a new option, Developer PowerShell for VS 20xx please switch to this shell instead of your regular shell for using its build tools.

3. follow the installation guide on the README.txt.

pip install -r requirements.txt
pip install git+https://github.com/NVlabs/nvdiffrast/
pip install git+https://github.com/NVlabs/tiny-cuda-nn/#subdirectory=bindings/torch
pip install git+https://github.com/openai/CLIP.git

4. To install an extension such as raymarching. Windows won’t recognize bash. So, you have to input its content line by line.

pip install ./raymarching
pip install ./shencoder
pip install ./freqencoder
pip install ./gridencoder

5. Done. Fill your prompt and run!

You will get output video after 100 epochs or depend on your --iters parameter

https://user-images.githubusercontent.com/1303847/196019884-bdfe16a7-6aaf-431b-99f7-fb692c5cab79.mp4

[y0himba]

Do you have the CUDA variables in your path?