在vast.ai的Ubuntu18.04环境安装nerfstudio时tinycudann报错求助

环境说明 #

• 运行环境：vast.ai提供的Ubuntu 18.04系统
• 已完成：Conda、PyTorch安装验证正常
• 安装依据：nerfstudio官方安装流程

报错详情 #

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-s8v5l299
  Running command git clone --filter=blob:none --quiet https://github.com/NVlabs/tiny-cuda-nn/ /tmp/pip-req-build-s8v5l299
  Resolved https://github.com/NVlabs/tiny-cuda-nn/ to commit a77dc53ed770dd8ea6f78951d5febe175d0045e9
  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
  ╰─> [9 lines of output]
      /root/miniconda3/envs/nerfstudio/lib/python3.8/site-packages/torch/cuda/__init__.py:88: 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 "<string>", line 2, in <module>
        File "<pip-setuptools-caller>", line 34, in <module>
        File "/tmp/pip-req-build-s8v5l299/bindings/torch/setup.py", line 49, in <module>
          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.

解决方法 #

1. 确认GPU计算能力 #

执行命令查看当前实例的GPU型号：

nvidia-smi

根据GPU型号查询对应的CUDA计算能力（例如RTX 3090对应86，A100对应80，V100对应70）。

2. 设置环境变量指定计算架构 #

在安装前设置TCNN_CUDA_ARCHITECTURES环境变量，示例（以A100为例）：

export TCNN_CUDA_ARCHITECTURES=80

若需兼容多个架构，用分号分隔：

export TCNN_CUDA_ARCHITECTURES=70;80;86

3. 重新安装tiny-cuda-nn #

执行安装命令：

pip install git+https://github.com/NVlabs/tiny-cuda-nn/#subdirectory=bindings/torch

4. 修复CUDA驱动兼容问题（可选） #

若仍出现驱动不兼容警告，需匹配CUDA与NVIDIA驱动版本：

• 查看当前CUDA版本：nvcc --version
• 安装对应版本的NVIDIA驱动（例如CUDA 11.7要求驱动版本≥450.80.02）

内容的提问来源于stack exchange，提问作者piorkoo