tinycuda installation fails

[sandrahal]

Im trying to install nerfstudio. facing this error while installing tinycuda. on linux on a shared hpc cluster

$ pip install ninja 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-p2668lu9
Running command git clone --filter=blob:none --quiet https://github.com/NVlabs/tiny-cuda-nn/ /tmp/pip-req-build-p2668lu9
Resolved https://github.com/NVlabs/tiny-cuda-nn/ to commit 74ea173
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]
/tmp/pip-req-build-p2668lu9/bindings/torch/setup.py:5: DeprecationWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html
from pkg_resources import parse_version
No CUDA runtime is found, using CUDA_HOME='/cm/shared/apps/cuda11.8/toolkit/11.8.0'
Traceback (most recent call last):
File "", line 2, in
File "", line 34, in
File "/tmp/pip-req-build-p2668lu9/bindings/torch/setup.py", line 53, in
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.")
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.
Building PyTorch extension for tiny-cuda-nn version 1.7
[end of output]

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.

[Dawars]

You have to set the compute capability of the GPU on the cluster:
TCNN_CUDA_ARCHITECTURES="86" pip install -v .

Check in this list https://developer.nvidia.com/cuda-gpus

For example 8.6 becomes 86