郑州网站建设喝彩科技,产品开发,网站咨询聊天怎么做,seo软件优化工具软件本文介绍了conda下安装cpu、gpu版本的pytorch#xff1b;并介绍了如何设置镜像源 ubuntu环境安装pytorch的CPU版本与GPU版本
系统#xff1a;ubuntu22.04 显卡#xff1a;RTX 3050 依赖工具#xff1a;miniconda
确认环境
lsb_release -a
No LSB modules are available.… 本文介绍了conda下安装cpu、gpu版本的pytorch并介绍了如何设置镜像源 ubuntu环境安装pytorch的CPU版本与GPU版本
系统ubuntu22.04 显卡RTX 3050 依赖工具miniconda
确认环境
lsb_release -a
No LSB modules are available.
Distributor ID: Ubuntu
Description: Ubuntu 22.04.3 LTS
Release: 22.04
Codename: jammy$ nvidia-smi
Tue Feb 13 21:51:33 2024
---------------------------------------------------------------------------------------
| NVIDIA-SMI 535.154.05 Driver Version: 535.154.05 CUDA Version: 12.2 |
|-------------------------------------------------------------------------------------
| GPU Name Persistence-M | Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap | Memory-Usage | GPU-Util Compute M. |
| | | MIG M. |
||
| 0 NVIDIA GeForce RTX 3050 ... Off | 00000000:02:00.0 Off | N/A |
| N/A 48C P3 7W / 35W | 435MiB / 4096MiB | 11% Default |
| | | N/A |
----------------------------------------------------------------------------------------------------------------------------------------------------------------------------
| Processes: |
| GPU GI CI PID Type Process name GPU Memory |
| ID ID Usage |
||
| 0 N/A N/A 1854 G /usr/lib/xorg/Xorg 226MiB |
| 0 N/A N/A 2225 G /usr/bin/gnome-shell 84MiB |
| 0 N/A N/A 3199 G ...irefox/2987/usr/lib/firefox/firefox 95MiB |
| 0 N/A N/A 48808 G ...resh,SpareRendererForSitePerProcess 21MiB |
---------------------------------------------------------------------------------------$ conda --version
conda 23.11.0使用conda安装pytorchCPU版本 注意默认conda安装pytorch的是cpu版本如需要安装GPU版本的注意直接看后面 创建一个新conda环境
$ conda create -n myPyt新创建的环境不包含任何依赖可以使用conda list查看一下
开始安装pytorch当然也可以前面在创建环境的同时把依赖包一同时安装了
$ conda install pytorch为了方便验证同时安装ipython IPython 是 Python 的原生交互式 shell 的增强版可以完成许多不同寻常的任务比如帮助实现并行化计算主要使用它提供的交互性帮助比如代码着色、改进了的命令行回调、制表符完成、宏功能以及改进了的交互式帮助 # 激活环境
$ conda activate myPyt
# 安装ipython
$ conda install ipython验证一下pytorch环境
输入ipython进入交互式环境依次输入如下两条命令import torch、torch.cuda.is_available()
$ ipython
Python 3.11.7 (main, Dec 15 2023, 18:12:31) [GCC 11.2.0]
Type copyright, credits or license for more information
IPython 8.20.0 -- An enhanced Interactive Python. Type ? for help.In [1]: import torchIn [2]: torch.cuda.is_available()
Out[2]: False发现当前的版本是不cpu版本不支持cuda加速的我们查看一下依赖
$ conda list
# packages in environment at /home/bing/miniconda3/envs/myEnv:
#
# Name Version Build Channel
_libgcc_mutex 0.1 main
_openmp_mutex 5.1 1_gnu
blas 1.0 mkl
bzip2 1.0.8 h7b6447c_0
ca-certificates 2023.12.12 h06a4308_0
cffi 1.16.0 py311h5eee18b_0
filelock 3.13.1 py311h06a4308_0
fsspec 2023.10.0 py311h06a4308_0
gmp 6.2.1 h295c915_3
gmpy2 2.1.2 py311hc9b5ff0_0
intel-openmp 2023.1.0 hdb19cb5_46306
jinja2 3.1.3 py311h06a4308_0
ld_impl_linux-64 2.38 h1181459_1
libffi 3.4.4 h6a678d5_0
libgcc-ng 11.2.0 h1234567_1
libgomp 11.2.0 h1234567_1
libprotobuf 3.20.3 he621ea3_0
libstdcxx-ng 11.2.0 h1234567_1
libuuid 1.41.5 h5eee18b_0
markupsafe 2.1.3 py311h5eee18b_0
mkl 2023.1.0 h213fc3f_46344
mkl-service 2.4.0 py311h5eee18b_1
mkl_fft 1.3.8 py311h5eee18b_0
mkl_random 1.2.4 py311hdb19cb5_0
mpc 1.1.0 h10f8cd9_1
mpfr 4.0.2 hb69a4c5_1
mpmath 1.3.0 py311h06a4308_0
ncurses 6.4 h6a678d5_0
networkx 3.1 py311h06a4308_0
ninja 1.10.2 h06a4308_5
ninja-base 1.10.2 hd09550d_5
numpy 1.26.3 py311h08b1b3b_0
numpy-base 1.26.3 py311hf175353_0
openssl 3.0.13 h7f8727e_0
pip 23.3.1 py311h06a4308_0
pycparser 2.21 pyhd3eb1b0_0
python 3.11.7 h955ad1f_0
pytorch 2.1.0 cpu_py311h6d93b4c_0
readline 8.2 h5eee18b_0
setuptools 68.2.2 py311h06a4308_0
sqlite 3.41.2 h5eee18b_0
sympy 1.12 py311h06a4308_0
tbb 2021.8.0 hdb19cb5_0
tk 8.6.12 h1ccaba5_0
typing-extensions 4.9.0 py311h06a4308_1
typing_extensions 4.9.0 py311h06a4308_1
tzdata 2023d h04d1e81_0
wheel 0.41.2 py311h06a4308_0
xz 5.4.5 h5eee18b_0
zlib 1.2.13 h5eee18b_0 发现确实没安装任何cuda库而pytorch的版本我们也可以看到确实cpu版本pytorch 2.1.0 cpu_py311h6d93b4c_0
conda安装GPU版本的pytorch
如何安装gpu版本的pytorch呢我们继续 我们查看一下pytorch可安装的版本
$ conda search pytorch
Loading channels: done
# Name Version Build Channel
...
pytorch 1.13.1 gpu_cuda113py39h0809116_0 pkgs/main
pytorch 1.13.1 gpu_cuda113py39h09dffc6_0 pkgs/main
pytorch 1.13.1 gpu_cuda113py39h926b89d_1 pkgs/main
pytorch 1.13.1 gpu_cuda113py39hde3f150_1 pkgs/main
pytorch 2.0.1 cpu_py310hab5cca8_0 pkgs/main
pytorch 2.0.1 cpu_py310hdc00b08_0 pkgs/main
pytorch 2.0.1 cpu_py311h53e38e9_0 pkgs/main
pytorch 2.0.1 cpu_py311h6d93b4c_0 pkgs/main
pytorch 2.0.1 cpu_py38hab5cca8_0 pkgs/main
pytorch 2.0.1 cpu_py38hdc00b08_0 pkgs/main
pytorch 2.0.1 cpu_py39hab5cca8_0 pkgs/main
pytorch 2.0.1 cpu_py39hdc00b08_0 pkgs/main
pytorch 2.0.1 gpu_cuda118py310h7799f5a_0 pkgs/main
pytorch 2.0.1 gpu_cuda118py310he342708_0 pkgs/main
pytorch 2.0.1 gpu_cuda118py311h7668aad_0 pkgs/main
pytorch 2.0.1 gpu_cuda118py311hce0f3bd_0 pkgs/main
pytorch 2.0.1 gpu_cuda118py38h7799f5a_0 pkgs/main
pytorch 2.0.1 gpu_cuda118py38he342708_0 pkgs/main
pytorch 2.0.1 gpu_cuda118py39h7799f5a_0 pkgs/main
pytorch 2.0.1 gpu_cuda118py39he342708_0 pkgs/main
pytorch 2.1.0 cpu_py310hab5cca8_0 pkgs/main
pytorch 2.1.0 cpu_py310hdc00b08_0 pkgs/main
pytorch 2.1.0 cpu_py311h53e38e9_0 pkgs/main
pytorch 2.1.0 cpu_py311h6d93b4c_0 pkgs/main
pytorch 2.1.0 cpu_py38hab5cca8_0 pkgs/main
pytorch 2.1.0 cpu_py38hdc00b08_0 pkgs/main
pytorch 2.1.0 cpu_py39hab5cca8_0 pkgs/main
pytorch 2.1.0 cpu_py39hdc00b08_0 pkgs/main 我们可以看到2.1.0版本的Build列中有点的cpu有的是gpu那么如何安装时指定安装带gpu表示的版本的呢
我们只需制定版本号的同时指定build即可
$ conda create -n pyt-gpu
$ conda activate pyt-gpu
$ conda install pytorch2.0.1gpu_cuda118py39he342708_0
...
The following NEW packages will be INSTALLED:_libgcc_mutex pkgs/main/linux-64::_libgcc_mutex-0.1-main _openmp_mutex pkgs/main/linux-64::_openmp_mutex-5.1-1_gnu blas pkgs/main/linux-64::blas-1.0-mkl ca-certificates pkgs/main/linux-64::ca-certificates-2023.12.12-h06a4308_0 cffi pkgs/main/linux-64::cffi-1.16.0-py39h5eee18b_0 cudatoolkit pkgs/main/linux-64::cudatoolkit-11.8.0-h6a678d5_0 cudnn pkgs/main/linux-64::cudnn-8.9.2.26-cuda11_0 cupti pkgs/main/linux-64::cupti-11.8.0-he078b1a_0 filelock pkgs/main/linux-64::filelock-3.13.1-py39h06a4308_0 gmp pkgs/main/linux-64::gmp-6.2.1-h295c915_3 gmpy2 pkgs/main/linux-64::gmpy2-2.1.2-py39heeb90bb_0 intel-openmp pkgs/main/linux-64::intel-openmp-2023.1.0-hdb19cb5_46306 jinja2 pkgs/main/linux-64::jinja2-3.1.3-py39h06a4308_0 ld_impl_linux-64 pkgs/main/linux-64::ld_impl_linux-64-2.38-h1181459_1 libffi pkgs/main/linux-64::libffi-3.4.4-h6a678d5_0 libgcc-ng pkgs/main/linux-64::libgcc-ng-11.2.0-h1234567_1 libgomp pkgs/main/linux-64::libgomp-11.2.0-h1234567_1 libprotobuf pkgs/main/linux-64::libprotobuf-3.20.3-he621ea3_0 libstdcxx-ng pkgs/main/linux-64::libstdcxx-ng-11.2.0-h1234567_1 magma pkgs/main/linux-64::magma-2.7.1-h2c23e93_0 markupsafe pkgs/main/linux-64::markupsafe-2.1.3-py39h5eee18b_0 mkl pkgs/main/linux-64::mkl-2023.1.0-h213fc3f_46344 mkl-service pkgs/main/linux-64::mkl-service-2.4.0-py39h5eee18b_1 mkl_fft pkgs/main/linux-64::mkl_fft-1.3.8-py39h5eee18b_0 mkl_random pkgs/main/linux-64::mkl_random-1.2.4-py39hdb19cb5_0 mpc pkgs/main/linux-64::mpc-1.1.0-h10f8cd9_1 mpfr pkgs/main/linux-64::mpfr-4.0.2-hb69a4c5_1 mpmath pkgs/main/linux-64::mpmath-1.3.0-py39h06a4308_0 ncurses pkgs/main/linux-64::ncurses-6.4-h6a678d5_0 networkx pkgs/main/linux-64::networkx-3.1-py39h06a4308_0 ninja pkgs/main/linux-64::ninja-1.10.2-h06a4308_5 ninja-base pkgs/main/linux-64::ninja-base-1.10.2-hd09550d_5 numpy pkgs/main/linux-64::numpy-1.26.3-py39h5f9d8c6_0 numpy-base pkgs/main/linux-64::numpy-base-1.26.3-py39hb5e798b_0 openssl pkgs/main/linux-64::openssl-3.0.13-h7f8727e_0 pip pkgs/main/linux-64::pip-23.3.1-py39h06a4308_0 pycparser pkgs/main/noarch::pycparser-2.21-pyhd3eb1b0_0 python pkgs/main/linux-64::python-3.9.18-h955ad1f_0 pytorch pkgs/main/linux-64::pytorch-2.0.1-gpu_cuda118py39he342708_0 readline pkgs/main/linux-64::readline-8.2-h5eee18b_0 setuptools pkgs/main/linux-64::setuptools-68.2.2-py39h06a4308_0 sqlite pkgs/main/linux-64::sqlite-3.41.2-h5eee18b_0 sympy pkgs/main/linux-64::sympy-1.12-py39h06a4308_0 tbb pkgs/main/linux-64::tbb-2021.8.0-hdb19cb5_0 tk pkgs/main/linux-64::tk-8.6.12-h1ccaba5_0 typing-extensions pkgs/main/linux-64::typing-extensions-4.9.0-py39h06a4308_1 typing_extensions pkgs/main/linux-64::typing_extensions-4.9.0-py39h06a4308_1 tzdata pkgs/main/noarch::tzdata-2023d-h04d1e81_0 wheel pkgs/main/linux-64::wheel-0.41.2-py39h06a4308_0 xz pkgs/main/linux-64::xz-5.4.5-h5eee18b_0 zlib pkgs/main/linux-64::zlib-1.2.13-h5eee18b_0
Proceed ([y]/n)? 安装时从提示安装的依赖我们可以看出这个版本确实带上了cuda相关包cudatoolkit、cudnn这次安装显然时间长了很多包的大小也近2G
安装完成后我们再次确认一下cuda加速是否可用
同样我们先安装一个ipython
$ conda install ipython进入ipython后依次执行如下代码import torch、torch.cuda.is_available()
$ ipython
Python 3.9.18 (main, Sep 11 2023, 13:41:44)
Type copyright, credits or license for more information
IPython 8.15.0 -- An enhanced Interactive Python. Type ? for help.In [1]: import torchIn [2]: torch.cuda.is_available()
Out[2]: True这次cuda可以正常工作到此完成gpu版本的pytorch安装
当然不是采用conda安装的话希望自己手鲁从nvidia驱动、cuda、cudnn这些库开始手动一个个安装好后最后再安装pytorch也是可以的关于pytorch手动安装方式这里给出官方地址点这里 最后再安装个jupyter工具conda install jupyter 启动命令jupyter-notebook
