Pip mirror source analysis: Tips to speed up Python package installation

Understand the Pip mirror source in one article: The secret to improving the speed of Python package installation

Python is a programming language widely used in data analysis, artificial intelligence and other fields. Flexible and all-inclusive third-party libraries make Python the first choice for developers. However, when installing these third-party libraries, due to limitations of the network environment, slow installation speed is often encountered, which is a major obstacle to development efficiency. In order to solve this problem, we can use the Pip mirror source to improve the installation speed of Python packages.

1. What is Pip mirror source?

Pip mirror refers to a server mirror that hosts Python packages, which can provide a fast copy of the original package. When using Pip installation package, you can specify the mirror source and point the download source to these mirrors to speed up the download.

2. How to configure the Pip mirror source

Configuring the Pip mirror source is very simple, just follow the following steps:

Step 1: Open the command Run tools (such as CMD or PowerShell under Windows, Terminal under Mac).

Step 2: Enter the following command to configure Pip's mirror source as a domestic mirror, such as Tsinghua Mirror Station (Tsinghua Mirror as an example):

pip config set global.index-url https: //pypi.tuna.tsinghua.edu.cn/simple

Step 3: Wait for the command execution to complete, and then use Pip to install the package normally.

3. Benefits brought by configuring Pip mirror source

By configuring Pip mirror source, the following benefits can be brought:

• Improve Python package Download speed: Since mirror sources are usually deployed on high-speed domestic servers, the download speed is faster, which can significantly save the time spent downloading Python packages through the network.
• Solve the problem of restricted access to specific websites: Some developers may be restricted from accessing specific websites. Choosing to use domestic mirror sources can circumvent this problem and ensure normal downloading of required packages.
• Reduce package download errors caused by network fluctuations: When downloading Python packages, download errors often occur due to network fluctuations. Using mirror sources can reduce the occurrence of this situation and improve download stability.

4. Commonly used domestic Pip mirror sources

In addition to the Tsinghua Mirror Station, there are several commonly used Pip mirror sources in China for developers to choose and use. ：

• Alibaba Cloud Mirror: https://mirrors.aliyun.com/pypi/simple/
• University of Science and Technology of China Mirror: https://pypi.mirrors.ustc.edu .cn/simple/
• Douban Mirror: https://pypi.douban.com/simple/

The usage method is the same as the Tsinghua Mirror Station in the above steps. You only need to Just replace the URL of the mirror source.

5. Verify whether the Pip mirror source configuration is successful

In order to verify whether the Pip mirror source configuration is successful, enter the following command on the command line:

pip config get global.index-url

If the output URL is consistent with the mirror source URL you configured, then the configuration is successful.

6. How to restore the default Pip mirror source

If you want to restore the default Pip mirror source, you can use the following command:

pip config unset global .index-url

7. Configuring the Pip mirror source in Jupyter Notebook

If you use Jupyter Notebook for Python development, you can also configure the Pip mirror source through the following steps:

Step 1: Open Jupyter Notebook and execute the following code in a new Notebook:

!pip install pip -U
!pip config set global.index-url https:// pypi.tuna.tsinghua.edu.cn/simple

Step 2: Restart Jupyter Notebook to enjoy the acceleration effect brought by the Pip mirror source.

Summary:

By configuring the Pip mirror source, we can greatly improve the installation speed of Python packages, save development time, and improve development efficiency. I hope this article can help everyone speed up Python development.

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