文章目录

conda 安装pytorch with cuda 失败问题

  • 激活环境(本例假设环境为pytorch_ser)

    PS D:\repos\PythonLearn> conda activate pytorch_ser

  • 尝试直接运行pytorch官网给出的conda安装命令,发现解析操作迟迟无法结束

    • Solving environment: failed with initial frozen solve. Retrying with flexible solve.
Collecting package metadata (repodata.json): done
....
Solving environment: ....

    • 原因可能是:

      • 我将默认的源换成清华源,而清华源的镜像没有能够满足要安装的配套组件
      • 网络环境问题,更换网络重试
      • 服务器问题,更改时段再试

使用pip安装

  • (d:\condaPythonEnvs\pytorch_ser) PS D:\repos\blogs> pip3 install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu117
Looking in indexes: https://pypi.org/simple, https://download.pytorch.org/whl/cu117
Requirement already satisfied: torch in d:\condapythonenvs\pytorch_ser\lib\site-packages (1.13.1)
Requirement already satisfied: torchvision in d:\condapythonenvs\pytorch_ser\lib\site-packages (0.14.1)
Requirement already satisfied: torchaudio in d:\condapythonenvs\pytorch_ser\lib\site-packages (0.13.1)
Requirement already satisfied: typing_extensions in d:\condapythonenvs\pytorch_ser\lib\site-packages (from torch) (4.4.0)
Requirement already satisfied: numpy in d:\condapythonenvs\pytorch_ser\lib\site-packages (from torchvision) (1.23.5)
Requirement already satisfied: requests in d:\condapythonenvs\pytorch_ser\lib\site-packages (from torchvision) (2.28.1)
Requirement already satisfied: pillow!=8.3.*,>=5.3.0 in d:\condapythonenvs\pytorch_ser\lib\site-packages (from torchvision) (9.3.0)
Requirement already satisfied: certifi>=2017.4.17 in d:\condapythonenvs\pytorch_ser\lib\site-packages (from requests->torchvision) (2022.12.7)
Requirement already satisfied: charset-normalizer<3,>=2 in d:\condapythonenvs\pytorch_ser\lib\site-packages (from requests->torchvision) (2.0.4)
Requirement already satisfied: idna<4,>=2.5 in d:\condapythonenvs\pytorch_ser\lib\site-packages (from requests->torchvision) (3.4)
Requirement already satisfied: urllib3<1.27,>=1.21.1 in d:\condapythonenvs\pytorch_ser\lib\site-packages (from requests->torchvision) (1.26.13)

  • 从上面的输出上看,pip似乎无法完成cuda组件的安装

使用conda安装pytorch with cuda

正确的安装组合@适用于安装最新版

  • 如果之前安装过cpu only 版本的pytorch,导致pytorch基础组件和cuda pytorch 组件不能够配合工作

  • 所以再在一个新的环境中重新安装cuda版pytorch

    • conda install pytorch torchvision torchaudio pytorch-cuda=11.7 -c pytorch -c nvidia
  • (d:\condaPythonEnvs\pytorch_ser) PS C:\Users\cxxu\Desktop> conda activate py310
(py310) PS C:\Users\cxxu\Desktop> conda install pytorch torchvision torchaudio pytorch-cuda=11.7 -c pytorch -c nvidia
Collecting package metadata (current_repodata.json): done
Solving environment: failed with initial frozen solve. Retrying with flexible solve.
Solving environment: failed with repodata from current_repodata.json, will retry with next repodata source.
Collecting package metadata (repodata.json): done
Solving environment: done

## Package Plan ##

  environment location: C:\Users\cxxu\miniconda3\envs\py310

  added / updated specs:
    - pytorch
    - pytorch-cuda=11.7
    - torchaudio
    - torchvision


The following packages will be downloaded:

    package                    |            build
    ---------------------------|-----------------
    pytorch-1.13.1             |py3.10_cuda11.7_cudnn8_0        1.10 GB  pytorch
    pytorch-mutex-1.0          |             cuda           3 KB  pytorch
    torchaudio-0.13.1          |      py310_cu117         4.7 MB  pytorch
    torchvision-0.14.1         |      py310_cu117         7.5 MB  pytorch
    ------------------------------------------------------------
                                           Total:        1.11 GB

The following NEW packages will be INSTALLED:

  brotlipy           anaconda/pkgs/main/win-64::brotlipy-0.7.0-py310h2bbff1b_1002
  cffi               anaconda/pkgs/main/win-64::cffi-1.15.1-py310h2bbff1b_3
  charset-normalizer anaconda/pkgs/main/noarch::charset-normalizer-2.0.4-pyhd3eb1b0_0
  cryptography       anaconda/pkgs/main/win-64::cryptography-38.0.1-py310h21b164f_0
  cuda               nvidia/win-64::cuda-11.7.1-0
  cuda-cccl          nvidia/win-64::cuda-cccl-11.7.91-0
  cuda-command-line~ nvidia/win-64::cuda-command-line-tools-11.7.1-0
  cuda-compiler      nvidia/win-64::cuda-compiler-11.7.1-0
  ....
  cuda-tools         nvidia/win-64::cuda-tools-11.7.1-0
  cuda-visual-tools  nvidia/win-64::cuda-visual-tools-11.7.1-0
  flit-core          anaconda/pkgs/main/noarch::flit-core-3.6.0-pyhd3eb1b0_0
  freetype           anaconda/pkgs/main/win-64::freetype-2.12.1-ha860e81_0
  idna               anaconda/pkgs/main/win-64::idna-3.4-py310haa95532_0
  jpeg               anaconda/pkgs/main/win-64::jpeg-9e-h2bbff1b_0
  lerc               anaconda/pkgs/main/win-64::lerc-3.0-hd77b12b_0
 ....
  pytorch-mutex      pytorch/noarch::pytorch-mutex-1.0-cuda
  requests           anaconda/pkgs/main/win-64::requests-2.28.1-py310haa95532_0
  torchaudio         pytorch/win-64::torchaudio-0.13.1-py310_cu117
  torchvision        pytorch/win-64::torchvision-0.14.1-py310_cu117
  typing_extensions  anaconda/pkgs/main/win-64::typing_extensions-4.4.0-py310haa95532_0
  urllib3            anaconda/pkgs/main/win-64::urllib3-1.26.13-py310haa95532_0
  win_inet_pton      anaconda/pkgs/main/win-64::win_inet_pton-1.1.0-py310haa95532_0
  zstd               anaconda/pkgs/main/win-64::zstd-1.5.2-h19a0ad4_0


Proceed ([y]/n)? y


Downloading and Extracting Packages
torchaudio-0.13.1    | 4.7 MB    | ############################################################################ | 100%
pytorch-mutex-1.0    | 3 KB      | ############################################################################ | 100%
pytorch-1.13.1       | 1.10 GB   | ###########################################################################9 | 100%
torchvision-0.14.1   | 7.5 MB    | ############################################################################ | 100%
                                                                                                                       GB   | ########################################################



Preparing transaction: done
Verifying transaction: done
Executing transaction: done
(py310) PS C:\Users\cxxu\Desktop>

检查cuda可用性

  • import torch as torch
import torch as th
print(th.__version__)
print(th.version.cuda)
print(th.cuda.is_available())

  • (py310) PS D:\repos\CCSER> python
Python 3.10.8 | packaged by conda-forge | (main, Nov 24 2022, 14:07:00) [MSC v.1916 64 bit (AMD64)] on win32
Type "help", "copyright", "credits" or "license" for more information.
>>> import torch as torch
>>> import torch as th
>>> print(th.__version__)
1.13.1
>>> print(th.version.cuda)
11.7
>>> print(th.cuda.is_available())
True

安装耗时

  • 安装的源用的清华源,宽带500M,在几分钟内(5分钟)可以完成安装

    • nvidia驱动版本和cuda驱动版本(CUDA Version: 12.0 )

      • cuda驱动版本要高于cuda运行时版本

      • 如果驱动版本过旧,到nvidia官方下载更新

      • 官方驱动 | NVIDIA

      • PS C:\Users\cxxu\Desktop> nvidia-smi.exe
Sun Jan  8 17:15:39 2023
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 527.56       Driver Version: 527.56       CUDA Version: 12.0     |
|-------------------------------+----------------------+----------------------+
| GPU  Name            TCC/WDDM | Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
|                               |                      |               MIG M. |
|===============================+======================+======================|
|   0  NVIDIA GeForce ... WDDM  | 00000000:02:00.0 Off |                  N/A |
| N/A   45C    P0    N/A /  N/A |      0MiB /  2048MiB |      0%      Default |
|                               |                      |                  N/A |
+-------------------------------+----------------------+----------------------+

+-----------------------------------------------------------------------------+
| Processes:                                                                  |
|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |
|        ID   ID                                                   Usage      |
|=============================================================================|
|  No running processes found                                                 |
+-----------------------------------------------------------------------------+
        • 玩具显卡,但是不影响过程演示

condarc配置文件示例

  • Using the .condarc conda configuration file — conda 23.3.0.post2+8419c02f5 documentation

  • You can find information about your .condarc file by typing conda info in your terminal or Anaconda Prompt.

    • This will give you information about your .condarc file, including where it is located.

    • PS D:\repos\blogs\python> conda info

     active environment : None
       user config file : C:\Users\cxxu\.condarc
 populated config files : C:\Users\cxxu\.condarc
          conda version : 23.1.0
    conda-build version : not installed
         python version : 3.9.5.final.0
       virtual packages : __archspec=1=x86_64
                          __cuda=12.0=0
                          __win=0=0
       base environment : C:\Users\cxxu\miniconda3  (writable)
      conda av data dir : C:\Users\cxxu\miniconda3\etc\conda
  conda av metadata url : None
           channel URLs : https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main/win-64
                          https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main/noarch
                          https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/r/win-64
                          https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/r/noarch
                          https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/msys2/win-64
                          https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/msys2/noarch
          package cache : C:\Users\cxxu\miniconda3\pkgs
                          C:\Users\cxxu\.conda\pkgs
                          C:\Users\cxxu\AppData\Local\conda\conda\pkgs
       envs directories : d:\condaPythonEnvs
                          C:\Users\cxxu\miniconda3\envs
                          C:\Users\cxxu\.conda\envs
                          C:\Users\cxxu\AppData\Local\conda\conda\envs
               platform : win-64
             user-agent : conda/23.1.0 requests/2.28.1 CPython/3.9.5 Windows/10 Windows/10.0.22621
          administrator : False
             netrc file : None
           offline mode : False

  • 本人的配置文件样例如下:

  • channels:
  - defaults
show_channel_urls: true
default_channels:
  - https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main
  - https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/r
  - https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/msys2
custom_channels:
  conda-forge: https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud
  msys2: https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud
  bioconda: https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud
  menpo: https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud
  pytorch: https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud
  pytorch-lts: https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud
  simpleitk: https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud
auto_activate_base: false

清华源🎈

阿里源

  • anaconda镜像_anaconda下载地址_anaconda安装教程-阿里巴巴开源镜像站 (aliyun.com)

  • channels:
  - defaults
show_channel_urls: true
default_channels:
  - http://mirrors.aliyun.com/anaconda/pkgs/main
  - http://mirrors.aliyun.com/anaconda/pkgs/r
  - http://mirrors.aliyun.com/anaconda/pkgs/msys2
custom_channels:
  conda-forge: http://mirrors.aliyun.com/anaconda/cloud
  msys2: http://mirrors.aliyun.com/anaconda/cloud
  bioconda: http://mirrors.aliyun.com/anaconda/cloud
  menpo: http://mirrors.aliyun.com/anaconda/cloud
  pytorch: http://mirrors.aliyun.com/anaconda/cloud
  simpleitk: http://mirrors.aliyun.com/anaconda/cloud

conda的相关使用参考

FAQ

安装完cuda依然无法调用GPU:错误的版本搭配

  • 最初本人尝试安装pytorch with cuda,发现无法安装(具体表现为:不停的解析,而无法顺利结束)

  • 于是我尝试安装一遍pytorch cpu only,发现可以顺利安装

  • 过了若干天,想体验GPU加速,重试,发现可以安装pytorch with cuda(此期间没有修改condarc配置文件)

  • 安装过程

    • (d:\condaPythonEnvs\pytorch_ser) PS D:\repos\blogs> conda install pytorch torchvision torchaudio pytorch-cuda=11.7 -c pytorch -c nvidia
Collecting package metadata (current_repodata.json): done
Solving environment: failed with initial frozen solve. Retrying with flexible solve.
Solving environment: failed with repodata from current_repodata.json, will retry with next repodata source.
Collecting package metadata (repodata.json): done
Solving environment: done

## Package Plan ##

  environment location: d:\condaPythonEnvs\pytorch_ser

  added / updated specs:
    - pytorch
    - pytorch-cuda=11.7
    - torchaudio
    - torchvision


The following packages will be downloaded:

    package                    |            build
    ---------------------------|-----------------
    cuda-11.7.1                |                0           1 KB  nvidia
    cuda-cccl-11.7.91          |                0         1.2 MB  nvidia
    cuda-command-line-tools-11.7.1|                0           1 KB  nvidia
    cuda-compiler-11.7.1       |                0           1 KB  nvidia
    cuda-cudart-11.7.99        |                0         1.4 MB  nvidia
    cuda-cudart-dev-11.7.99    |                0         711 KB  nvidia
    cuda-cuobjdump-11.7.91     |                0         2.5 MB  nvidia
    cuda-cupti-11.7.101        |                0        10.2 MB  nvidia
    cuda-cuxxfilt-11.7.91      |                0         165 KB  nvidia
    ....
    cuda-toolkit-11.7.1        |                0           1 KB  nvidia
    cuda-tools-11.7.1          |                0           1 KB  nvidia
    cuda-visual-tools-11.7.1   |                0           1 KB  nvidia
    libcublas-11.10.3.66       |                0          24 KB  nvidia
    libcublas-dev-11.10.3.66   |                0       282.4 MB  nvidia
    libcufft-10.7.2.124        |                0           6 KB  nvidia
    libcufft-dev-10.7.2.124    |                0       250.1 MB  nvidia
    libcurand-10.3.1.50        |                0           3 KB  nvidia
    libcurand-dev-10.3.1.50    |                0        50.0 MB  nvidia
    libcusolver-11.4.0.1       |                0          29 KB  nvidia
    libcusolver-dev-11.4.0.1   |                0        76.5 MB  nvidia
    libcusparse-11.7.4.91      |                0          13 KB  nvidia
    libcusparse-dev-11.7.4.91  |                0       149.6 MB  nvidia
    libnpp-11.7.4.75           |                0         294 KB  nvidia
    libnpp-dev-11.7.4.75       |                0       125.6 MB  nvidia
    libnvjpeg-11.8.0.2         |                0           4 KB  nvidia
    libnvjpeg-dev-11.8.0.2     |                0         1.7 MB  nvidia
    nsight-compute-2022.4.0.15 |                0       598.6 MB  nvidia
    pytorch-cuda-11.7          |       h67b0de4_1           3 KB  pytorch
    ------------------------------------------------------------
                                           Total:        1.82 GB

The following NEW packages will be INSTALLED:

  cuda               nvidia/win-64::cuda-11.7.1-0
  cuda-cccl          nvidia/win-64::cuda-cccl-11.7.91-0
  cuda-command-line~ nvidia/win-64::cuda-command-line-tools-11.7.1-0
  cuda-compiler      nvidia/win-64::cuda-compiler-11.7.1-0
  cuda-cudart        nvidia/win-64::cuda-cudart-11.7.99-0
  cuda-cudart-dev    nvidia/win-64::cuda-cudart-dev-11.7.99-0
  cuda-cuobjdump     nvidia/win-64::cuda-cuobjdump-11.7.91-0
  cuda-cupti         nvidia/win-64::cuda-cupti-11.7.101-0
...
  cuda-tools         nvidia/win-64::cuda-tools-11.7.1-0
  cuda-visual-tools  nvidia/win-64::cuda-visual-tools-11.7.1-0
  libcublas          nvidia/win-64::libcublas-11.10.3.66-0
  libcublas-dev      nvidia/win-64::libcublas-dev-11.10.3.66-0
  libcufft           nvidia/win-64::libcufft-10.7.2.124-0
  libcufft-dev       nvidia/win-64::libcufft-dev-10.7.2.124-0
  libcurand          nvidia/win-64::libcurand-10.3.1.50-0
  libcurand-dev      nvidia/win-64::libcurand-dev-10.3.1.50-0
  libcusolver        nvidia/win-64::libcusolver-11.4.0.1-0
  libcusolver-dev    nvidia/win-64::libcusolver-dev-11.4.0.1-0
  libcusparse        nvidia/win-64::libcusparse-11.7.4.91-0
  libcusparse-dev    nvidia/win-64::libcusparse-dev-11.7.4.91-0
  libnpp             nvidia/win-64::libnpp-11.7.4.75-0
  libnpp-dev         nvidia/win-64::libnpp-dev-11.7.4.75-0
  libnvjpeg          nvidia/win-64::libnvjpeg-11.8.0.2-0
  libnvjpeg-dev      nvidia/win-64::libnvjpeg-dev-11.8.0.2-0
  nsight-compute     nvidia/win-64::nsight-compute-2022.4.0.15-0
  pytorch-cuda       pytorch/noarch::pytorch-cuda-11.7-h67b0de4_1


Proceed ([y]/n)? y


Downloading and Extracting Packages
cuda-cudart-dev-11.7 | 711 KB    | ############################################################################################################################################### | 100%
cuda-memcheck-11.8.8 | 183 KB    | ############################################################################################################################################### | 100%
cuda-cudart-11.7.99  | 1.4 MB    | ############################################################################################################################################### | 100%
libnvjpeg-11.8.0.2   | 4 KB      | ############################################################################################################################################### | 100%
pytorch-cuda-11.7    | 3 KB      | ############################################################################################################################################### | 100%

........

####################################################################################################################5                           |  81%
cuda-cupti-11.7.101  | 10.2 MB   | ############################################################################################################################################### | 100%
cuda-demo-suite-12.0 | 4.7 MB    | ############################################################################################################################################### | 100%

历史版本的安装🎈

通道问题@Channel

  • 对于conda install命令而言,-c参数指定的Channel对于安装操作是至关重要的
  • 特别是对于复杂或大型的框架的安装,更加容易因为指定的通道不合适而导致安装失败

COMMANDS FOR VERSIONS >= 1.0.0

  • 在这里不得不吐槽以下pytorch的历史版本页面提供的命令,竟然无法工作

  • 后来对比最新版命令才发现,是Previous PyTorch Versions | PyTorch页面将-c nvidia参数错误的写成-nvida

    • 导致的一个直接问题是,conda命令是没有-nvidia这样的参数,而且会别识别为-n vidia,也就是识别为一个名为vidia的环境

      • conda install pytorch==1.13.1 torchvision==0.14.1 torchaudio==0.13.1 pytorch-cuda=11.7 -c pytorch -nvidia(是一个错误的命令)

    • 刚开始我不知道这个参数是个Channel的名称,就把它删除掉在运行,发现会报一些莫名奇妙的依赖

      • Package pytorch-cuda conflicts for:
torchaudio==0.13.1 -> pytorch-cuda[version='11.6.*|11.7.*']
pytorch-cuda=11.6
torchaudio==0.13.1 -> pytorch==1.13.1 -> pytorch-cuda[version='>=11.6,<11.7|>=11.7,<11.8']
pytorch==1.13.1 -> pytorch-cuda[version='>=11.6,<11.7|>=11.7,<11.8']

Package pytorch conflicts for:
pytorch==1.13.1
torchaudio==0.13.1 -> pytorch==1.13.1

      • 而我们自己检查发现其实依赖是没有问题的,这些版本也都是官网提供的

    • 将通道修改正确conda install pytorch==1.13.1 torchvision==0.14.1 torchaudio==0.13.1 pytorch-cuda=11.7 -c pytorch -c nvidia

      • 得到正确的反馈

      • (d:\condaPythonEnvs\d2l) PS D:\repos\blogs> conda install pytorch==1.13.1 torchvision==0.14.1 torchaudio==0.13.1 pytorch-cuda=11.7 -c pytorch -c nvidia
Collecting package metadata (current_repodata.json): done
Solving environment: failed with initial frozen solve. Retrying with flexible solve.
Collecting package metadata (repodata.json): done
Solving environment: done

## Package Plan ##

  environment location: d:\condaPythonEnvs\d2l

  added / updated specs:
    - pytorch-cuda=11.7
    - pytorch==1.13.1
    - torchaudio==0.13.1
    - torchvision==0.14.1


The following packages will be downloaded:

    package                    |            build
    ---------------------------|-----------------
    cuda-cccl-12.1.55          |                0         1.2 MB  nvidia
    libcurand-10.3.2.56        |                0           3 KB  nvidia
    libcurand-dev-10.3.2.56    |                0        50.0 MB  nvidia
    pytorch-cuda-11.7          |       h16d0643_3           7 KB  pytorch
    ------------------------------------------------------------
                                           Total:        51.2 MB

The following NEW packages will be INSTALLED:

  blas               anaconda/pkgs/main/win-64::blas-1.0-mkl
  brotlipy           anaconda/pkgs/main/win-64::brotlipy-0.7.0-py310h2bbff1b_1002
  bzip2              anaconda/pkgs/main/win-64::bzip2-1.0.8-he774522_0
	...
  cuda-cupti         nvidia/win-64::cuda-cupti-11.7.101-0
  cuda-libraries     nvidia/win-64::cuda-libraries-11.7.1-0
  cuda-libraries-dev nvidia/win-64::cuda-libraries-dev-11.7.1-0
  ...
  
  32_0
  vc                 anaconda/pkgs/main/win-64::vc-14.2-h21ff451_1
  vs2015_runtime     anaconda/pkgs/main/win-64::vs2015_runtime-14.27.29016-h5e58377_2
  wheel              anaconda/pkgs/main/win-64::wheel-0.38.4-py310haa95532_0
  win_inet_pton      anaconda/pkgs/main/win-64::win_inet_pton-1.1.0-py310haa95532_0
  wincertstore       anaconda/pkgs/main/win-64::wincertstore-0.2-py310haa95532_2
  xz                 anaconda/pkgs/main/win-64::xz-5.2.10-h8cc25b3_1
  zlib               anaconda/pkgs/main/win-64::zlib-1.2.13-h8cc25b3_0
  zstd               anaconda/pkgs/main/win-64::zstd-1.5.2-h19a0ad4_0


Proceed ([y]/n)? y


Downloading and Extracting Packages

Preparing transaction: done
Verifying transaction: done
Executing transaction: done

      • 可以看到,这次下载量很小,是因为之前我在其他环境用conda install安装过一次pytorch==1.13.1及其配套依赖,所以这次需要下载的内容比较少,其他内容可以从本地的conda缓存中读取即可

v1.13.1

Conda
OSX
# conda
conda install pytorch==1.13.1 torchvision==0.14.1 torchaudio==0.13.1 -c pytorch
Linux and Windows
# CUDA 11.6
conda install pytorch==1.13.1 torchvision==0.14.1 torchaudio==0.13.1 pytorch-cuda=11.6 -c pytorch -nvidia
# CUDA 11.7
conda install pytorch==1.13.1 torchvision==0.14.1 torchaudio==0.13.1 pytorch-cuda=11.7 -c pytorch -nvidia
# CPU Only
conda install pytorch==1.13.1 torchvision==0.14.1 torchaudio==0.13.1 cpuonly -c pytorch
Wheel
OSX
pip install torch==1.13.1 torchvision==0.14.1 torchaudio==0.13.1
Linux and Windows
# ROCM 5.2 (Linux only)
pip3 install torch torchvision torchaudio --extra-index-url
pip install torch==1.13.1+rocm5.2 torchvision==0.14.1+rocm5.2 torchaudio==0.13.1 --extra-index-url https://download.pytorch.org/whl/rocm5.2
# CUDA 11.6
pip install torch==1.13.1+cu116 torchvision==0.14.1+cu116 torchaudio==0.13.1 --extra-index-url https://download.pytorch.org/whl/cu116
# CUDA 11.7
pip install torch==1.13.1+cu117 torchvision==0.14.1+cu117 torchaudio==0.13.1 --extra-index-url https://download.pytorch.org/whl/cu117
# CPU only
pip install torch==1.13.1+cpu torchvision==0.14.1+cpu torchaudio==0.13.1 --extra-index-url https://download.pytorch.org/whl/cpu
# pytorch # conda # python
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