我们使用 Conda 安装 pytorch 和 CUDA 环境之后,要用 Conda 的CUDA环境进行某个库编译时,出现了bug:
bash
/mnt/data/home/xxxx/miniforge3/envs/GAGAvatar/compiler_compat/ld: cannot find -lcudart: No such file or directory
collect2: error: ld returned 1 exit status
error: command '/mnt/data/home/xxxx/miniforge3/envs/GAGAvatar/bin/g++' failed with exit code 1
[end of output]
note: This error originates from a subprocess, and is likely not a problem with pip.
ERROR: Failed building wheel for diff_gaussian_rasterization_32d
Running setup.py clean for diff_gaussian_rasterization_32d
Failed to build diff_gaussian_rasterization_32d
ERROR: ERROR: Failed to build installable wheels for some pyproject.toml based projects (diff_gaussian_rasterization_32d)
❯ which nvcc
/mnt/data/home/xxxx/miniforge3/envs/GAGAvatar/bin/nvcc
❯ echo $CUDA_HOME
/mnt/data/home/xxxx/miniforge3/envs/GAGAvatar
❯ echo $PATH
/home/xxxx/local/bin:/home/xxxx/local/bin:/mnt/data/home/xxxx/miniforge3/envs/GAGAvatar/bin:/mnt/data/home/xxxx/miniforge3/condabin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/usr/games:/usr/local/games:/snap/bin
❯ echo $LD_LIBRARY_PATH
/mnt/data/home/xxxx/miniforge3/envs/GAGAvatar/lib:
去探查发现,这里的软链接出了问题:
bash
❯ ls /mnt/data/home/xxxx/miniforge3/envs/GAGAvatar/lib
libcudart.so -> libcudart.so.12.1.55
libcudart.so.12
libcudart.so.12.1.105
继续探究发现,安装Pytorch时会安装 cuda-cudart=12.1.105
以下是按照Pytorch时会安装的所有以 pytorch
、 nvidia
为 channel 的包:
bash
+ pytorch-mutex 1.0 cuda pytorch Cached
+ libcublas 12.1.0.26 0 nvidia Cached
+ libcufft 11.0.2.4 0 nvidia Cached
+ libcusolver 11.4.4.55 0 nvidia Cached
+ libcusparse 12.0.2.55 0 nvidia Cached
+ libnpp 12.0.2.50 0 nvidia Cached
+ cuda-cudart 12.1.105 0 nvidia Cached
+ cuda-nvrtc 12.1.105 0 nvidia Cached
+ libnvjitlink 12.1.105 0 nvidia Cached
+ libnvjpeg 12.1.1.14 0 nvidia Cached
+ cuda-cupti 12.1.105 0 nvidia Cached
+ cuda-nvtx 12.1.105 0 nvidia Cached
+ ffmpeg 4.3 hf484d3e_0 pytorch Cached
+ libjpeg-turbo 2.0.0 h9bf148f_0 pytorch Cached
+ cuda-version 12.6 3 nvidia Cached
+ libcurand 10.3.7.77 0 nvidia Cached
+ libcufile 1.11.1.6 0 nvidia Cached
+ cuda-opencl 12.6.77 0 nvidia Cached
+ cuda-libraries 12.1.0 0 nvidia Cached
+ cuda-runtime 12.1.0 0 nvidia Cached
+ pytorch-cuda 12.1 ha16c6d3_6 pytorch Cached
+ pytorch 2.4.1 py3.12_cuda12.1_cudnn9.1.0_0 pytorch Cached
+ torchtriton 3.0.0 py312 pytorch Cached
+ torchaudio 2.4.1 py312_cu121 pytorch Cached
+ torchvision 0.19.1 py312_cu121 pytorch Cached
而这是安装 cuda-toolkit-12.1.0
的包:
bash
+ cuda-documentation 12.1.55 0 nvidia/label/cuda-12.1.0 Cached
+ cuda-nvml-dev 12.1.55 0 nvidia/label/cuda-12.1.0 Cached
+ libnvvm-samples 12.1.55 0 nvidia/label/cuda-12.1.0 Cached
+ cuda-cccl 12.1.55 0 nvidia/label/cuda-12.1.0 Cached
+ cuda-driver-dev 12.1.55 0 nvidia/label/cuda-12.1.0 Cached
+ cuda-profiler-api 12.1.55 0 nvidia/label/cuda-12.1.0 Cached
+ cuda-cudart 12.1.55 0 nvidia/label/cuda-12.1.0 Cached
+ cuda-nvrtc 12.1.55 0 nvidia/label/cuda-12.1.0 21MB
+ cuda-opencl 12.1.56 0 nvidia/label/cuda-12.1.0 11kB
+ libcublas 12.1.0.26 0 nvidia/label/cuda-12.1.0 Cached
+ libcufft 11.0.2.4 0 nvidia/label/cuda-12.1.0 Cached
+ libcufile 1.6.0.25 0 nvidia/label/cuda-12.1.0 782kB
+ libcurand 10.3.2.56 0 nvidia/label/cuda-12.1.0 54MB
+ libcusolver 11.4.4.55 0 nvidia/label/cuda-12.1.0 Cached
+ libcusparse 12.0.2.55 0 nvidia/label/cuda-12.1.0 Cached
+ libnpp 12.0.2.50 0 nvidia/label/cuda-12.1.0 Cached
+ libnvjitlink 12.1.55 0 nvidia/label/cuda-12.1.0 18MB
+ libnvjpeg 12.1.0.39 0 nvidia/label/cuda-12.1.0 3MB
+ cuda-cupti 12.1.62 0 nvidia/label/cuda-12.1.0 5MB
+ cuda-cuobjdump 12.1.55 0 nvidia/label/cuda-12.1.0 Cached
+ cuda-cuxxfilt 12.1.55 0 nvidia/label/cuda-12.1.0 Cached
+ cuda-nvcc 12.1.66 0 nvidia/label/cuda-12.1.0 Cached
+ cuda-nvprune 12.1.55 0 nvidia/label/cuda-12.1.0 Cached
+ cuda-gdb 12.1.55 0 nvidia/label/cuda-12.1.0 Cached
+ cuda-nvdisasm 12.1.55 0 nvidia/label/cuda-12.1.0 Cached
+ cuda-nvprof 12.1.55 0 nvidia/label/cuda-12.1.0 Cached
+ cuda-nvtx 12.1.66 0 nvidia/label/cuda-12.1.0 58kB
+ cuda-sanitizer-api 12.1.55 0 nvidia/label/cuda-12.1.0 Cached
+ cuda-nsight 12.1.55 0 nvidia/label/cuda-12.1.0 Cached
+ nsight-compute 2023.1.0.15 0 nvidia/label/cuda-12.1.0 Cached
+ cuda-cudart-dev 12.1.55 0 nvidia/label/cuda-12.1.0 Cached
+ cuda-nvrtc-dev 12.1.55 0 nvidia/label/cuda-12.1.0 Cached
+ cuda-opencl-dev 12.1.56 0 nvidia/label/cuda-12.1.0 Cached
+ libcublas-dev 12.1.0.26 0 nvidia/label/cuda-12.1.0 Cached
+ libcufft-dev 11.0.2.4 0 nvidia/label/cuda-12.1.0 Cached
+ gds-tools 1.6.0.25 0 nvidia/label/cuda-12.1.0 Cached
+ libcufile-dev 1.6.0.25 0 nvidia/label/cuda-12.1.0 Cached
+ libcurand-dev 10.3.2.56 0 nvidia/label/cuda-12.1.0 Cached
+ libcusolver-dev 11.4.4.55 0 nvidia/label/cuda-12.1.0 Cached
+ libcusparse-dev 12.0.2.55 0 nvidia/label/cuda-12.1.0 Cached
+ libnpp-dev 12.0.2.50 0 nvidia/label/cuda-12.1.0 Cached
+ libnvjitlink-dev 12.1.55 0 nvidia/label/cuda-12.1.0 Cached
+ libnvjpeg-dev 12.1.0.39 0 nvidia/label/cuda-12.1.0 Cached
+ cuda-libraries 12.1.0 0 nvidia/label/cuda-12.1.0 Cached
+ cuda-cupti-static 12.1.62 0 nvidia/label/cuda-12.1.0 Cached
+ cuda-compiler 12.1.0 0 nvidia/label/cuda-12.1.0 Cached
+ cuda-nvvp 12.1.55 0 nvidia/label/cuda-12.1.0 Cached
+ cuda-command-line-tools 12.1.0 0 nvidia/label/cuda-12.1.0 Cached
+ cuda-nsight-compute 12.1.0 0 nvidia/label/cuda-12.1.0 Cached
+ cuda-cudart-static 12.1.55 0 nvidia/label/cuda-12.1.0 Cached
+ cuda-nvrtc-static 12.1.55 0 nvidia/label/cuda-12.1.0 Cached
+ libcublas-static 12.1.0.26 0 nvidia/label/cuda-12.1.0 Cached
+ libcufft-static 11.0.2.4 0 nvidia/label/cuda-12.1.0 Cached
+ libcufile-static 1.6.0.25 0 nvidia/label/cuda-12.1.0 Cached
+ libcurand-static 10.3.2.56 0 nvidia/label/cuda-12.1.0 Cached
+ libcusolver-static 11.4.4.55 0 nvidia/label/cuda-12.1.0 Cached
+ libcusparse-static 12.0.2.55 0 nvidia/label/cuda-12.1.0 Cached
+ libnpp-static 12.0.2.50 0 nvidia/label/cuda-12.1.0 Cached
+ libnvjpeg-static 12.1.0.39 0 nvidia/label/cuda-12.1.0 Cached
+ cuda-libraries-dev 12.1.0 0 nvidia/label/cuda-12.1.0 Cached
+ cuda-libraries-static 12.1.0 0 nvidia/label/cuda-12.1.0 Cached
+ cuda-visual-tools 12.1.0 0 nvidia/label/cuda-12.1.0 Cached
+ cuda-tools 12.1.0 0 nvidia/label/cuda-12.1.0 Cached
+ cuda-toolkit 12.1.0 0 nvidia/label/cuda-12.1.0 Cached
这是安装 cuda-toolkit-12.1.1
的包:
bash
+ cuda-documentation 12.1.105 0 nvidia/label/cuda-12.1.1 91kB
+ cuda-nvml-dev 12.1.105 0 nvidia/label/cuda-12.1.1 87kB
+ libnvvm-samples 12.1.105 0 nvidia/label/cuda-12.1.1 33kB
+ cuda-cccl 12.1.109 0 nvidia/label/cuda-12.1.1 1MB
+ cuda-driver-dev 12.1.105 0 nvidia/label/cuda-12.1.1 17kB
+ cuda-profiler-api 12.1.105 0 nvidia/label/cuda-12.1.1 19kB
+ cuda-cudart 12.1.105 0 nvidia/label/cuda-12.1.1 Cached
+ cuda-nvrtc 12.1.105 0 nvidia/label/cuda-12.1.1 Cached
+ cuda-opencl 12.1.105 0 nvidia/label/cuda-12.1.1 11kB
+ libcublas 12.1.3.1 0 nvidia/label/cuda-12.1.1 367MB
+ libcufft 11.0.2.54 0 nvidia/label/cuda-12.1.1 108MB
+ libcufile 1.6.1.9 0 nvidia/label/cuda-12.1.1 783kB
+ libcurand 10.3.2.106 0 nvidia/label/cuda-12.1.1 54MB
+ libcusolver 11.4.5.107 0 nvidia/label/cuda-12.1.1 116MB
+ libcusparse 12.1.0.106 0 nvidia/label/cuda-12.1.1 177MB
+ libnpp 12.1.0.40 0 nvidia/label/cuda-12.1.1 147MB
+ libnvjitlink 12.1.105 0 nvidia/label/cuda-12.1.1 Cached
+ libnvjpeg 12.2.0.2 0 nvidia/label/cuda-12.1.1 3MB
+ cuda-cupti 12.1.105 0 nvidia/label/cuda-12.1.1 Cached
+ cuda-cuobjdump 12.1.111 0 nvidia/label/cuda-12.1.1 245kB
+ cuda-cuxxfilt 12.1.105 0 nvidia/label/cuda-12.1.1 302kB
+ cuda-nvcc 12.1.105 0 nvidia/label/cuda-12.1.1 55MB
+ cuda-nvprune 12.1.105 0 nvidia/label/cuda-12.1.1 67kB
+ cuda-gdb 12.1.105 0 nvidia/label/cuda-12.1.1 6MB
+ cuda-nvdisasm 12.1.105 0 nvidia/label/cuda-12.1.1 50MB
+ cuda-nvprof 12.1.105 0 nvidia/label/cuda-12.1.1 5MB
+ cuda-nvtx 12.1.105 0 nvidia/label/cuda-12.1.1 Cached
+ cuda-sanitizer-api 12.1.105 0 nvidia/label/cuda-12.1.1 18MB
+ cuda-nsight 12.1.105 0 nvidia/label/cuda-12.1.1 119MB
+ nsight-compute 2023.1.1.4 0 nvidia/label/cuda-12.1.1 808MB
+ cuda-cudart-dev 12.1.105 0 nvidia/label/cuda-12.1.1 381kB
+ cuda-nvrtc-dev 12.1.105 0 nvidia/label/cuda-12.1.1 12kB
+ cuda-opencl-dev 12.1.105 0 nvidia/label/cuda-12.1.1 59kB
+ libcublas-dev 12.1.3.1 0 nvidia/label/cuda-12.1.1 76kB
+ libcufft-dev 11.0.2.54 0 nvidia/label/cuda-12.1.1 14kB
+ gds-tools 1.6.1.9 0 nvidia/label/cuda-12.1.1 43MB
+ libcufile-dev 1.6.1.9 0 nvidia/label/cuda-12.1.1 13kB
+ libcurand-dev 10.3.2.106 0 nvidia/label/cuda-12.1.1 460kB
+ libcusolver-dev 11.4.5.107 0 nvidia/label/cuda-12.1.1 51kB
+ libcusparse-dev 12.1.0.106 0 nvidia/label/cuda-12.1.1 178MB
+ libnpp-dev 12.1.0.40 0 nvidia/label/cuda-12.1.1 525kB
+ libnvjitlink-dev 12.1.105 0 nvidia/label/cuda-12.1.1 15MB
+ libnvjpeg-dev 12.2.0.2 0 nvidia/label/cuda-12.1.1 13kB
+ cuda-libraries 12.1.1 0 nvidia/label/cuda-12.1.1 2kB
+ cuda-cupti-static 12.1.105 0 nvidia/label/cuda-12.1.1 12MB
+ cuda-compiler 12.1.1 0 nvidia/label/cuda-12.1.1 1kB
+ cuda-nvvp 12.1.105 0 nvidia/label/cuda-12.1.1 120MB
+ cuda-command-line-tools 12.1.1 0 nvidia/label/cuda-12.1.1 1kB
+ cuda-nsight-compute 12.1.1 0 nvidia/label/cuda-12.1.1 1kB
+ cuda-cudart-static 12.1.105 0 nvidia/label/cuda-12.1.1 948kB
+ cuda-nvrtc-static 12.1.105 0 nvidia/label/cuda-12.1.1 18MB
+ libcublas-static 12.1.3.1 0 nvidia/label/cuda-12.1.1 389MB
+ libcufft-static 11.0.2.54 0 nvidia/label/cuda-12.1.1 199MB
+ libcufile-static 1.6.1.9 0 nvidia/label/cuda-12.1.1 3MB
+ libcurand-static 10.3.2.106 0 nvidia/label/cuda-12.1.1 55MB
+ libcusolver-static 11.4.5.107 0 nvidia/label/cuda-12.1.1 76MB
+ libcusparse-static 12.1.0.106 0 nvidia/label/cuda-12.1.1 185MB
+ libnpp-static 12.1.0.40 0 nvidia/label/cuda-12.1.1 143MB
+ libnvjpeg-static 12.2.0.2 0 nvidia/label/cuda-12.1.1 3MB
+ cuda-libraries-dev 12.1.1 0 nvidia/label/cuda-12.1.1 2kB
+ cuda-libraries-static 12.1.1 0 nvidia/label/cuda-12.1.1 2kB
+ cuda-visual-tools 12.1.1 0 nvidia/label/cuda-12.1.1 1kB
+ cuda-tools 12.1.1 0 nvidia/label/cuda-12.1.1 1kB
+ cuda-toolkit 12.1.1 0 nvidia/label/cuda-12.1.1 2kB
对比发现是 cuda-12.1.1
才对的上CUDA版本12.1的Pytorch。但是我们在安装的时候,先安装CUDA版本12.1的Pytorch,再安装 cuda-12.1.1
会出现冲突问题:
bash
└─ pytorch-cuda is not installable because it requires
└─ libcublas >=12.1.0.26,<12.1.3.1 , which conflicts with any installable versions previously reported.
也就是说,该死的CUDA版本12.1的Pytorch的 libcublas
需要适配 cuda-toolkit-12.1.0
,但是其的 cuda-cudart
等库又需要适配 cuda-toolkit-12.1.1
可以看到 pytorch-cuda 强要求 libcublas >=12.1.0.26,<12.1.3.1
,我们只好迁就 pytorch,安装12.1.0的CUDA,但是呢!我们可以修改Pytorch官方给出的 nvidia
channel 为 nvidia/label/cuda-12.1.0
使用以下命令:
bash
mamba install pytorch==2.4.1 torchvision==0.19.1 torchaudio==2.4.1 pytorch-cuda=12.1 -c pytorch -c nvidia/label/cuda-12.1.0
其就会安装与我们安装的 cuda-toolkit-12.1.0
一样的一些 cuda 库了!
bash
+ pytorch-mutex 1.0 cuda pytorch Cached
+ libcublas 12.1.0.26 0 nvidia/label/cuda-12.1.0 Cached
+ libcufft 11.0.2.4 0 nvidia/label/cuda-12.1.0 Cached
+ libcusolver 11.4.4.55 0 nvidia/label/cuda-12.1.0 Cached
+ libcusparse 12.0.2.55 0 nvidia/label/cuda-12.1.0 Cached
+ libnpp 12.0.2.50 0 nvidia/label/cuda-12.1.0 Cached
+ libnvjpeg 12.1.0.39 0 nvidia/label/cuda-12.1.0 3MB
+ cuda-cudart 12.1.55 0 nvidia/label/cuda-12.1.0 Cached
+ cuda-nvrtc 12.1.55 0 nvidia/label/cuda-12.1.0 21MB
+ cuda-opencl 12.1.56 0 nvidia/label/cuda-12.1.0 11kB
+ libcufile 1.6.0.25 0 nvidia/label/cuda-12.1.0 782kB
+ libcurand 10.3.2.56 0 nvidia/label/cuda-12.1.0 54MB
+ cuda-cupti 12.1.62 0 nvidia/label/cuda-12.1.0 5MB
+ cuda-nvtx 12.1.66 0 nvidia/label/cuda-12.1.0 58kB
+ cuda-version 12.1 h1d6eff3_3 conda-forge 21kB
+ ffmpeg 4.3 hf484d3e_0 pytorch Cached
+ libjpeg-turbo 2.0.0 h9bf148f_0 pytorch Cached
+ libnvjitlink 12.1.105 hd3aeb46_0 conda-forge 16MB
+ cuda-libraries 12.1.0 0 nvidia/label/cuda-12.1.0 Cached
+ cuda-runtime 12.1.0 0 nvidia/label/cuda-12.1.0 Cached
+ pytorch-cuda 12.1 ha16c6d3_6 pytorch Cached
+ pytorch 2.4.1 py3.12_cuda12.1_cudnn9.1.0_0 pytorch Cached
+ torchtriton 3.0.0 py312 pytorch Cached
+ torchvision 0.19.1 py312_cu121 pytorch Cached
+ torchaudio 2.4.1 py312_cu121 pytorch Cached
到这里,问题就解决了:我们之后要安装 pytorch-cuda 和 cuda-toolkit 时,只需要执行以下命令(顺序应该不重要了):
bash
mamba install pytorch==2.4.1 torchvision==0.19.1 torchaudio==2.4.1 pytorch-cuda=12.1 -c pytorch -c nvidia/label/cuda-12.1.0
mamba install nvidia/label/cuda-12.1.0::cuda-toolkit -c nvidia/label/cuda-12.1.0
安装 cuda-toolkit 就相当于在安装完 pytorch-cuda 的需要的部分 cuda 库后,进行了补充安装,都是同一个 channel 的当然就不会有问题了