Python中pip安装非PyPI官网第三方库的方法
在python中安装非自带python模块,有三种方式:
1.easy_install
2.pip
3.下载压缩包(.zip, .tar, .tar.gz)后解压, 进入解压缩的目录后执行python setup.py install命令
本文主要针对pip安装时可能会碰到的一种情况,及解决办法:
假如我要安装pylint模块,该模块非python自带模块,用import肯定不能导入,需要额外安装
代码如下:
>>> import pylint
Traceback (most recent call last):
File "
ImportError: No module named pylint
【现象】
执行pip install
代码如下:
D:\>pip install pylint --allow-external pylint
Downloading/unpacking pylint
Requirement already satisfied (use --upgrade to upgrade): six in c:\python27\lib\site-packages\six-1
.8.0-py2.7.egg (from pylint)
Downloading/unpacking astroid>=1.3.6 (from pylint)
Real name of requirement astroid is astroid
Could not find any downloads that satisfy the requirement astroid>=1.3.6 (from pylint)
Some insecure and unverifiable files were ignored (use --allow-unverified astroid to allow).
Cleaning up...
No distributions at all found for astroid>=1.3.6 (from pylint)
Storing debug log for failure in C:\Users\aaa\pip\pip.log
【分析】
在Perl中安装新模块,一般可以用PPM图形化工具,也可以用CPAN来安装,比如说: cpan>install Test::Class, 非常方便,不会碰到这种情况,这种情况主要是因为pip版本问题: pip最新的版本(1.5以上的版本), 出于安全的考
虑,pip不允许安装非PyPI的URL,因为该安装文件实际上来自pylint.org,因而导致上面的错误!
NOTE:
1. 可以在官方changelog里面查看更改的信息
2. 可以用pip --version来查看pip的版本信息
代码如下:
C:\>pip --version
pip 1.5.6 from C:\Python27\lib\site-packages (python 2.7)
【办法】
针对上面的情况,既然这个问题是因为pip版本的原因,可以改用pip低一点的版本
方法一: 用pip 1.4版本,再执行pip install pylint命令来安装
方法二: 执行命令时,加上--allow-all-external, --allow-unverified及依赖包版本(astroid==1.3.6)
代码如下:
pip install pylint --allow-all-external pylint astroid==1.3.6 --allow-unverified pylint
NOTE:
1. --allow-all-external # 允许所有外部地址的标签,只有打上该标签pip方可下载外部地址模块
2. --allow-unverified # pip没有办法校验外部模块的有效性,所以必须同时打上该标签
3. astroid==1.3.6 # 依赖包必须要添加上,并赋予其版本号,pip方能从列表下载
方法三: 在当前目录下,新增requirements.txt,内容如下:
代码如下:
# requirements.txt
--allow-all-external pylint
--allow-unverified pylint
pylint
--allow-all-external astroid==1.3.6
再执行: pip install -r requirements.txt
【结论】
1. pip这个设计不够友好,使用也很不方便,远不如Perl中的PPM,期待Python中也有这么个工具。
2. 如果碰到这种错,导致不能安装模块的话: 直接下载压缩包安装好了。 >>>下载包地址
3. 执行pip -h命令查看更新pip相关的帮助信息
代码如下:
Usage:
pip
Commands:
install Install packages.
uninstall Uninstall packages.
freeze Output installed packages in requirements format.
list List installed packages.
show Show information about installed packages.
search Search PyPI for packages.
wheel Build wheels from your requirements.
zip DEPRECATED. Zip individual packages.
unzip DEPRECATED. Unzip individual packages.
bundle DEPRECATED. Create pybundles.
help Show help for commands.
General Options:
-h, --help Show help.
-v, --verbose Give more output. Option is additive, and can be used up to 3 times.
-V, --version Show version and exit.
-q, --quiet Give less output.
--log-file
--log
--proxy
--timeout
--exists-action
--cert

Hot AI Tools

Undresser.AI Undress
AI-powered app for creating realistic nude photos

AI Clothes Remover
Online AI tool for removing clothes from photos.

Undress AI Tool
Undress images for free

Clothoff.io
AI clothes remover

AI Hentai Generator
Generate AI Hentai for free.

Hot Article

Hot Tools

Notepad++7.3.1
Easy-to-use and free code editor

SublimeText3 Chinese version
Chinese version, very easy to use

Zend Studio 13.0.1
Powerful PHP integrated development environment

Dreamweaver CS6
Visual web development tools

SublimeText3 Mac version
God-level code editing software (SublimeText3)

Hot Topics



PHP and Python have their own advantages and disadvantages, and the choice depends on project needs and personal preferences. 1.PHP is suitable for rapid development and maintenance of large-scale web applications. 2. Python dominates the field of data science and machine learning.

Python and JavaScript have their own advantages and disadvantages in terms of community, libraries and resources. 1) The Python community is friendly and suitable for beginners, but the front-end development resources are not as rich as JavaScript. 2) Python is powerful in data science and machine learning libraries, while JavaScript is better in front-end development libraries and frameworks. 3) Both have rich learning resources, but Python is suitable for starting with official documents, while JavaScript is better with MDNWebDocs. The choice should be based on project needs and personal interests.

Enable PyTorch GPU acceleration on CentOS system requires the installation of CUDA, cuDNN and GPU versions of PyTorch. The following steps will guide you through the process: CUDA and cuDNN installation determine CUDA version compatibility: Use the nvidia-smi command to view the CUDA version supported by your NVIDIA graphics card. For example, your MX450 graphics card may support CUDA11.1 or higher. Download and install CUDAToolkit: Visit the official website of NVIDIACUDAToolkit and download and install the corresponding version according to the highest CUDA version supported by your graphics card. Install cuDNN library:

Docker uses Linux kernel features to provide an efficient and isolated application running environment. Its working principle is as follows: 1. The mirror is used as a read-only template, which contains everything you need to run the application; 2. The Union File System (UnionFS) stacks multiple file systems, only storing the differences, saving space and speeding up; 3. The daemon manages the mirrors and containers, and the client uses them for interaction; 4. Namespaces and cgroups implement container isolation and resource limitations; 5. Multiple network modes support container interconnection. Only by understanding these core concepts can you better utilize Docker.

PyTorch distributed training on CentOS system requires the following steps: PyTorch installation: The premise is that Python and pip are installed in CentOS system. Depending on your CUDA version, get the appropriate installation command from the PyTorch official website. For CPU-only training, you can use the following command: pipinstalltorchtorchvisiontorchaudio If you need GPU support, make sure that the corresponding version of CUDA and cuDNN are installed and use the corresponding PyTorch version for installation. Distributed environment configuration: Distributed training usually requires multiple machines or single-machine multiple GPUs. Place

When installing PyTorch on CentOS system, you need to carefully select the appropriate version and consider the following key factors: 1. System environment compatibility: Operating system: It is recommended to use CentOS7 or higher. CUDA and cuDNN:PyTorch version and CUDA version are closely related. For example, PyTorch1.9.0 requires CUDA11.1, while PyTorch2.0.1 requires CUDA11.3. The cuDNN version must also match the CUDA version. Before selecting the PyTorch version, be sure to confirm that compatible CUDA and cuDNN versions have been installed. Python version: PyTorch official branch

In VS Code, you can run the program in the terminal through the following steps: Prepare the code and open the integrated terminal to ensure that the code directory is consistent with the terminal working directory. Select the run command according to the programming language (such as Python's python your_file_name.py) to check whether it runs successfully and resolve errors. Use the debugger to improve debugging efficiency.

Updating PyTorch to the latest version on CentOS can follow the following steps: Method 1: Updating pip with pip: First make sure your pip is the latest version, because older versions of pip may not be able to properly install the latest version of PyTorch. pipinstall--upgradepip uninstalls old version of PyTorch (if installed): pipuninstalltorchtorchvisiontorchaudio installation latest
