【Trick】conda指令安装yml文件中的python依赖

2024-05-04 14:44

本文主要是介绍【Trick】conda指令安装yml文件中的python依赖,希望对大家解决编程问题提供一定的参考价值,需要的开发者们随着小编来一起学习吧!

安装python依赖通常使用【pip】或【conda】指令,pip主要用于txt文件,conda主要用于yml文件。以下将给出其使用方法

pip

首先,创建一个YAML文件,列出python依赖项。

dependencies:- python=3.8- numpy- pandas- matplotlib

然后,打开终端,运行pip指令(假设文件名为requirements,实际应用时下列指令应该根据文件名进行修改)。

对于yml:

pip install -r requirements.yml

对于txt:

pip install -r requirements.txt

后续只需等待即可。 

conda

 首先,创建一个YAML文件,列出python依赖项。

channels:- defaults
dependencies:- python=3.8- numpy- pandas- matplotlib

然后,打开终端,运行conda指令(假设文件名为requirements,实际应用时下列指令应该根据文件名进行修改)。

对于yml:

conda env create -f requirements.yml

后续只需等待即可。 

实际案例

yml文件内容如下:

name: con_110
channels:- pytorch- defaults
dependencies:- _libgcc_mutex=0.1=main- _openmp_mutex=4.5=1_gnu- backcall=0.2.0=pyhd3eb1b0_0- blas=1.0=mkl- bzip2=1.0.8=h7b6447c_0- ca-certificates=2022.07.19=h06a4308_0- certifi=2022.9.24=py37h06a4308_0- cudatoolkit=11.3.1=h2bc3f7f_2- debugpy=1.5.1=py37h295c915_0- decorator=5.1.1=pyhd3eb1b0_0- entrypoints=0.4=py37h06a4308_0- faiss-gpu=1.7.2=py3.7_h28a55e0_0_cuda11.3- ffmpeg=4.3=hf484d3e_0- freetype=2.11.0=h70c0345_0- giflib=5.2.1=h7b6447c_0- gmp=6.2.1=h2531618_2- gnutls=3.6.15=he1e5248_0- intel-openmp=2021.4.0=h06a4308_3561- ipykernel=6.15.2=py37h06a4308_0- ipython=7.31.1=py37h06a4308_1- jedi=0.18.1=py37h06a4308_1- joblib=1.1.0=pyhd3eb1b0_0- jpeg=9d=h7f8727e_0- jupyter_client=7.1.2=pyhd3eb1b0_0- jupyter_core=4.11.1=py37h06a4308_0- lame=3.100=h7b6447c_0- lcms2=2.12=h3be6417_0- ld_impl_linux-64=2.35.1=h7274673_9- libfaiss=1.7.2=hfc2d529_0_cuda11.3- libffi=3.3=he6710b0_2- libgcc-ng=9.3.0=h5101ec6_17- libgfortran-ng=7.5.0=ha8ba4b0_17- libgfortran4=7.5.0=ha8ba4b0_17- libgomp=9.3.0=h5101ec6_17- libiconv=1.15=h63c8f33_5- libidn2=2.3.2=h7f8727e_0- libpng=1.6.37=hbc83047_0- libsodium=1.0.18=h7b6447c_0- libstdcxx-ng=9.3.0=hd4cf53a_17- libtasn1=4.16.0=h27cfd23_0- libtiff=4.2.0=h85742a9_0- libunistring=0.9.10=h27cfd23_0- libuv=1.40.0=h7b6447c_0- libwebp=1.2.2=h55f646e_0- libwebp-base=1.2.2=h7f8727e_0- lz4-c=1.9.3=h295c915_1- matplotlib-inline=0.1.6=py37h06a4308_0- mkl=2021.4.0=h06a4308_640- mkl-service=2.4.0=py37h7f8727e_0- mkl_fft=1.3.1=py37hd3c417c_0- mkl_random=1.2.2=py37h51133e4_0- ncurses=6.3=h7f8727e_2- nest-asyncio=1.5.5=py37h06a4308_0- nettle=3.7.3=hbbd107a_1- numpy=1.21.2=py37h20f2e39_0- numpy-base=1.21.2=py37h79a1101_0- olefile=0.46=py37_0- openh264=2.1.1=h4ff587b_0- openssl=1.1.1q=h7f8727e_0- packaging=21.3=pyhd3eb1b0_0- parso=0.8.3=pyhd3eb1b0_0- pexpect=4.8.0=pyhd3eb1b0_3- pickleshare=0.7.5=pyhd3eb1b0_1003- pillow=8.4.0=py37h5aabda8_0- pip=21.2.2=py37h06a4308_0- prompt-toolkit=3.0.20=pyhd3eb1b0_0- ptyprocess=0.7.0=pyhd3eb1b0_2- py=1.11.0=pyhd3eb1b0_0- pygments=2.11.2=pyhd3eb1b0_0- pyparsing=3.0.9=py37h06a4308_0- python=3.7.11=h12debd9_0- python-dateutil=2.8.2=pyhd3eb1b0_0- pytorch=1.10.2=py3.7_cuda11.3_cudnn8.2.0_0- pytorch-mutex=1.0=cuda- pyzmq=22.3.0=py37h295c915_2- readline=8.1.2=h7f8727e_1- scikit-learn=1.0.2=py37h51133e4_1- scipy=1.7.3=py37hc147768_0- setuptools=58.0.4=py37h06a4308_0- six=1.16.0=pyhd3eb1b0_1- sqlite=3.37.2=hc218d9a_0- threadpoolctl=2.2.0=pyh0d69192_0- tk=8.6.11=h1ccaba5_0- torchvision=0.11.3=py37_cu113- tornado=6.1=py37h27cfd23_0- tqdm=4.62.3=pyhd3eb1b0_1- traitlets=5.1.1=pyhd3eb1b0_0- typing_extensions=3.10.0.2=pyh06a4308_0- wcwidth=0.2.5=pyhd3eb1b0_0- wheel=0.37.1=pyhd3eb1b0_0- xz=5.2.5=h7b6447c_0- zeromq=4.3.4=h2531618_0- zlib=1.2.11=h7f8727e_4- zstd=1.4.9=haebb681_0- pip:- charset-normalizer==2.0.12- click==8.0.4- docker-pycreds==0.4.0- gitdb==4.0.9- gitpython==3.1.27- idna==3.3- importlib-metadata==4.11.1- pathtools==0.1.2- promise==2.3- protobuf==3.19.4- psutil==5.9.0- pyyaml==6.0- requests==2.27.1- sentry-sdk==1.5.6- shortuuid==1.0.8- smmap==5.0.0- termcolor==1.1.0- urllib3==1.26.8- wandb==0.12.10- yaspin==2.1.0- zipp==3.7.0

conda指令如下:

conda env create -f environment.yml

运行过程如下:

(base) ubuntu@xwk2:~/GG$ conda env create -f environment.yml
Retrieving notices: ...working... done
Collecting package metadata (repodata.json): - WARNING conda.models.version:get_matcher(556): Using .* with relational operator is superfluous and deprecated and will be removed in a future version of conda. Your spec was 1.7.1.*, but conda is ignoring the .* and treating it as 1.7.1
WARNING conda.models.version:get_matcher(556): Using .* with relational operator is superfluous and deprecated and will be removed in a future version of conda. Your spec was 1.9.0.*, but conda is ignoring the .* and treating it as 1.9.0
WARNING conda.models.version:get_matcher(556): Using .* with relational operator is superfluous and deprecated and will be removed in a future version of conda. Your spec was 1.8.0.*, but conda is ignoring the .* and treating it as 1.8.0
WARNING conda.models.version:get_matcher(556): Using .* with relational operator is superfluous and deprecated and will be removed in a future version of conda. Your spec was 1.6.0.*, but conda is ignoring the .* and treating it as 1.6.0
done
Solving environment: done==> WARNING: A newer version of conda exists. <==current version: 23.7.3latest version: 24.4.0Please update conda by running$ conda update -n base -c https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main condaOr to minimize the number of packages updated during conda update useconda install conda=24.4.0Downloading and Extracting Packages
blas-1.0             | 6 KB      | ############################################################################## | 100% 
certifi-2022.9.24    | 154 KB    | ############################################################################## | 100% 
setuptools-58.0.4    | 775 KB    | ############################################################################## | 100% 
numpy-base-1.21.2    | 4.8 MB    | ############################################################################## | 100% 
ipykernel-6.15.2     | 189 KB    | ############################################################################## | 100% 
prompt-toolkit-3.0.2 | 259 KB    | ############################################################################## | 100% 
pygments-2.11.2      | 759 KB    | ############################################################################## | 100% 
python-3.7.11        | 45.3 MB   | ############################################################################## | 100% 
pyzmq-22.3.0         | 465 KB    | ############################################################################## | 100% 
libunistring-0.9.10  | 536 KB    | ############################################################################## | 100% 
entrypoints-0.4      | 16 KB     | ############################################################################## | 100% ... (more hidden) ...Preparing transaction: done                                                                                              
Verifying transaction: done                                                                                              
Executing transaction: \ By downloading and using the CUDA Toolkit conda packages, you accept the terms and conditions of the CUDA End User License Agreement (EULA): https://docs.nvidia.com/cuda/eula/index.html                                \                                                                                                                        Installed package of scikit-learn can be accelerated using scikit-learn-intelex.                                     More details are available here: https://intel.github.io/scikit-learn-intelex                                        For example:      $ conda install scikit-learn-intelex$ python -m sklearnex my_application.pydone
Installing pip dependencies: \ Ran pip subprocess with arguments:
['/home/ubuntu/miniconda3/envs/con_110/bin/python', '-m', 'pip', 'install', '-U', '-r', '/home/ubuntu/GLC/condaenv.h1f9e76a.requirements.txt', '--exists-action=b']
Pip subprocess output:
Collecting charset-normalizer==2.0.12Downloading charset_normalizer-2.0.12-py3-none-any.whl (39 kB)
Collecting click==8.0.4Downloading click-8.0.4-py3-none-any.whl (97 kB)
Collecting docker-pycreds==0.4.0Downloading docker_pycreds-0.4.0-py2.py3-none-any.whl (9.0 kB)
Collecting gitdb==4.0.9Downloading gitdb-4.0.9-py3-none-any.whl (63 kB)
Collecting gitpython==3.1.27Downloading GitPython-3.1.27-py3-none-any.whl (181 kB)
Collecting idna==3.3Downloading idna-3.3-py3-none-any.whl (61 kB)
Collecting importlib-metadata==4.11.1Downloading importlib_metadata-4.11.1-py3-none-any.whl (17 kB)
Collecting pathtools==0.1.2Downloading pathtools-0.1.2.tar.gz (11 kB)
Collecting promise==2.3Downloading promise-2.3.tar.gz (19 kB)
Collecting protobuf==3.19.4Downloading protobuf-3.19.4-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.1 MB)
Collecting psutil==5.9.0Downloading psutil-5.9.0-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (280 kB)
Collecting pyyaml==6.0Downloading PyYAML-6.0-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl (596 kB)
Collecting requests==2.27.1Downloading requests-2.27.1-py2.py3-none-any.whl (63 kB)
Collecting sentry-sdk==1.5.6Downloading sentry_sdk-1.5.6-py2.py3-none-any.whl (144 kB)
Collecting shortuuid==1.0.8Downloading shortuuid-1.0.8-py3-none-any.whl (9.5 kB)
Collecting smmap==5.0.0Downloading smmap-5.0.0-py3-none-any.whl (24 kB)
Collecting termcolor==1.1.0Downloading termcolor-1.1.0.tar.gz (3.9 kB)
Collecting urllib3==1.26.8Downloading urllib3-1.26.8-py2.py3-none-any.whl (138 kB)
Collecting wandb==0.12.10Downloading wandb-0.12.10-py2.py3-none-any.whl (1.7 MB)
Collecting yaspin==2.1.0Downloading yaspin-2.1.0-py3-none-any.whl (18 kB)
Collecting zipp==3.7.0Downloading zipp-3.7.0-py3-none-any.whl (5.3 kB)
Requirement already satisfied: six>=1.4.0 in /home/ubuntu/miniconda3/envs/con_110/lib/python3.7/site-packages (from docker-pycreds==0.4.0->-r /home/ubuntu/GLC/condaenv.h1f9e76a.requirements.txt (line 3)) (1.16.0)
Requirement already satisfied: typing-extensions>=3.7.4.3 in /home/ubuntu/miniconda3/envs/con_110/lib/python3.7/site-packages (from gitpython==3.1.27->-r /home/ubuntu/GLC/condaenv.h1f9e76a.requirements.txt (line 5)) (3.10.0.2)
Requirement already satisfied: certifi>=2017.4.17 in /home/ubuntu/miniconda3/envs/con_110/lib/python3.7/site-packages (from requests==2.27.1->-r /home/ubuntu/GLC/condaenv.h1f9e76a.requirements.txt (line 13)) (2022.9.24)
Requirement already satisfied: python-dateutil>=2.6.1 in /home/ubuntu/miniconda3/envs/con_110/lib/python3.7/site-packages (from wandb==0.12.10->-r /home/ubuntu/GLC/condaenv.h1f9e76a.requirements.txt (line 19)) (2.8.2)
Building wheels for collected packages: pathtools, promise, termcolorBuilding wheel for pathtools (setup.py): startedBuilding wheel for pathtools (setup.py): finished with status 'done'Created wheel for pathtools: filename=pathtools-0.1.2-py3-none-any.whl size=8806 sha256=a66ada93957cf7fc7b3ef5bffd7844b018a4a97eaca9289f2e292761489ee2d8Stored in directory: /home/ubuntu/.cache/pip/wheels/3e/31/09/fa59cef12cdcfecc627b3d24273699f390e71828921b2cbba2Building wheel for promise (setup.py): startedBuilding wheel for promise (setup.py): finished with status 'done'Created wheel for promise: filename=promise-2.3-py3-none-any.whl size=21503 sha256=884eb51f491d088c5fb1959e5be5f3ec41086ef58eac9c32cc737c5c83a45578Stored in directory: /home/ubuntu/.cache/pip/wheels/29/93/c6/762e359f8cb6a5b69c72235d798804cae523bbe41c2aa8333dBuilding wheel for termcolor (setup.py): startedBuilding wheel for termcolor (setup.py): finished with status 'done'Created wheel for termcolor: filename=termcolor-1.1.0-py3-none-any.whl size=4848 sha256=889e55b1d10f55c622c011af4442358d02ae5abd2629c38ed91bdaebd3dd542dStored in directory: /home/ubuntu/.cache/pip/wheels/3f/e3/ec/8a8336ff196023622fbcb36de0c5a5c218cbb24111d1d4c7f2
Successfully built pathtools promise termcolor
Installing collected packages: zipp, smmap, urllib3, termcolor, importlib-metadata, idna, gitdb, charset-normalizer, yaspin, shortuuid, sentry-sdk, requests, pyyaml, psutil, protobuf, promise, pathtools, gitpython, docker-pycreds, click, wandbAttempting uninstall: psutilFound existing installation: psutil 5.8.0Uninstalling psutil-5.8.0:Successfully uninstalled psutil-5.8.0
Successfully installed charset-normalizer-2.0.12 click-8.0.4 docker-pycreds-0.4.0 gitdb-4.0.9 gitpython-3.1.27 idna-3.3 importlib-metadata-4.11.1 pathtools-0.1.2 promise-2.3 protobuf-3.19.4 psutil-5.9.0 pyyaml-6.0 requests-2.27.1 sentry-sdk-1.5.6 shortuuid-1.0.8 smmap-5.0.0 termcolor-1.1.0 urllib3-1.26.8 wandb-0.12.10 yaspin-2.1.0 zipp-3.7.0done
#
# To activate this environment, use
#
#     $ conda activate con_110
#
# To deactivate an active environment, use
#
#     $ conda deactivate

这篇关于【Trick】conda指令安装yml文件中的python依赖的文章就介绍到这儿,希望我们推荐的文章对编程师们有所帮助!



http://www.chinasem.cn/article/959447

相关文章

python中列表list切分的实现

《python中列表list切分的实现》列表是Python中最常用的数据结构之一,经常需要对列表进行切分操作,本文主要介绍了python中列表list切分的实现,文中通过示例代码介绍的非常详细,对大家... 目录一、列表切片的基本用法1.1 基本切片操作1.2 切片的负索引1.3 切片的省略二、列表切分的高

基于Python实现一个PDF特殊字体提取工具

《基于Python实现一个PDF特殊字体提取工具》在PDF文档处理场景中,我们常常需要针对特定格式的文本内容进行提取分析,本文介绍的PDF特殊字体提取器是一款基于Python开发的桌面应用程序感兴趣的... 目录一、应用背景与功能概述二、技术架构与核心组件2.1 技术选型2.2 系统架构三、核心功能实现解析

通过Python脚本批量复制并规范命名视频文件

《通过Python脚本批量复制并规范命名视频文件》本文介绍了如何通过Python脚本批量复制并规范命名视频文件,实现自动补齐数字编号、保留原始文件、智能识别有效文件等功能,听过代码示例介绍的非常详细,... 目录一、问题场景:杂乱的视频文件名二、完整解决方案三、关键技术解析1. 智能路径处理2. 精准文件名

基于Python开发PDF转Doc格式小程序

《基于Python开发PDF转Doc格式小程序》这篇文章主要为大家详细介绍了如何基于Python开发PDF转Doc格式小程序,文中的示例代码讲解详细,感兴趣的小伙伴可以跟随小编一起学习一下... 用python实现PDF转Doc格式小程序以下是一个使用Python实现PDF转DOC格式的GUI程序,采用T

Python使用PIL库将PNG图片转换为ICO图标的示例代码

《Python使用PIL库将PNG图片转换为ICO图标的示例代码》在软件开发和网站设计中,ICO图标是一种常用的图像格式,特别适用于应用程序图标、网页收藏夹图标等场景,本文将介绍如何使用Python的... 目录引言准备工作代码解析实践操作结果展示结语引言在软件开发和网站设计中,ICO图标是一种常用的图像

IDEA与JDK、Maven安装配置完整步骤解析

《IDEA与JDK、Maven安装配置完整步骤解析》:本文主要介绍如何安装和配置IDE(IntelliJIDEA),包括IDE的安装步骤、JDK的下载与配置、Maven的安装与配置,以及如何在I... 目录1. IDE安装步骤2.配置操作步骤3. JDK配置下载JDK配置JDK环境变量4. Maven配置下

使用Python开发一个图像标注与OCR识别工具

《使用Python开发一个图像标注与OCR识别工具》:本文主要介绍一个使用Python开发的工具,允许用户在图像上进行矩形标注,使用OCR对标注区域进行文本识别,并将结果保存为Excel文件,感兴... 目录项目简介1. 图像加载与显示2. 矩形标注3. OCR识别4. 标注的保存与加载5. 裁剪与重置图像

使用Python实现表格字段智能去重

《使用Python实现表格字段智能去重》在数据分析和处理过程中,数据清洗是一个至关重要的步骤,其中字段去重是一个常见且关键的任务,下面我们看看如何使用Python进行表格字段智能去重吧... 目录一、引言二、数据重复问题的常见场景与影响三、python在数据清洗中的优势四、基于Python的表格字段智能去重

Python中如何控制小数点精度与对齐方式

《Python中如何控制小数点精度与对齐方式》在Python编程中,数据输出格式化是一个常见的需求,尤其是在涉及到小数点精度和对齐方式时,下面小编就来为大家介绍一下如何在Python中实现这些功能吧... 目录一、控制小数点精度1. 使用 round() 函数2. 使用字符串格式化二、控制对齐方式1. 使用

Python如何快速下载依赖

《Python如何快速下载依赖》本文介绍了四种在Python中快速下载依赖的方法,包括使用国内镜像源、开启pip并发下载功能、使用pipreqs批量下载项目依赖以及使用conda管理依赖,通过这些方法... 目录python快速下载依赖1. 使用国内镜像源临时使用镜像源永久配置镜像源2. 使用 pip 的并