conda 安装pytorch with cuda 失败问题@pytorch历史版本安装问题

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
- creating and editing

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🎈

anaconda | 镜像站使用帮助 | 清华大学开源软件镜像站 | Tsinghua Open Source Mirror

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

conda发行版比较@python环境管理@conda命令的基本操作@配置conda_xuchaoxin1375的博客-CSDN博客

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🎈

Previous PyTorch Versions | PyTorch

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

posted @ 2023-01-08 17:18 xuchaoxin1375 阅读(99) 评论(0) 编辑收藏举报来源

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