January Python popular open source projects on Github

January on GitHub most popular open source Python project ranking has been released you, take a look at the list details:

1. fastapi


Star 9051

FastAPI Rails is a library for fast data query interdependent set of human-readable and return time, standard API output. By constructing complex SQL queries and efficient use of JOIN and sub-queries.



Star 3802

ALBERT model is an improved version of BERT, and other recent State of the art model different is that this is a small model pre-trained, better, fewer parameters. It has been three BERT renovation:

  • Word embedded vector parameter factorization
  • Cross-layer parameter sharing
  • Paragraph continuous task

3. spiderfoot


Star 3802

SpiderFoot is a free, open-source Web site information gathering tool, you can get information based on the specified domain name, such as website subdomains, email addresses, web server version, etc., can help penetration testers for automating web site information collection, asset discovery the process, improve work efficiency.

4. JobFunnel


Star 1244

Crawl work sites, filtering and inspection checklist tool for the job posting climb .csv file.

5. SlowFast


Star 2005

PySlowfast is based PyTorch code base, so that researchers can easily reproduced from basic to cutting-edge video recognition (Video Classification) and behavior detection (Action Detection) algorithm.

Not only that, PySlowFast open source code base at the same time a large number of pre-training model (pretrain models), allowing researchers to eliminating the trouble of repeated training model can be used directly FAIR pre-trained frontier (cutting edge performance) model. By a Facebook Open Source

6. system-design-primer


Star 82155

The project is on how to design large-scale systems, as well as how to deal with system design interview. The system design is a very broad topic. On the Internet, the resources on the system design principles is a dime a dozen. The warehouse is to organize the collection of these resources, it can help you learn how to build scalable systems.

7. ansible


Star 41269

Ansible is a configuration management and configuration tools, like Chef, Puppet or Salt. This is a very simple and very easy to deploy tools to get started, it uses the SSH connection to the server and runs the configured tasks, without having to install any extra software on the server only needs to open ssh, all the work to be responsible for client-side ansible .

8. pandas


Star 23377

Pandas is a data analysis package python, the first in April 2008, was developed by AQR Capital Management, and at the end of 2009 revenue came out, there continue to develop a focus on Python packets developed PyData development team and maintenance, are PyData part of the project . Pandas and was originally developed as a financial data analysis tools, therefore, pandas time series analysis provides a good support. Pandas name from python and data analysis (data analysis) data of the panel (panel data). panel data is a term regarding economics cube, also provided in the panel Pandas data types.

9. GitHub-Chinese-Top-Charts


Star 14343

GitHub Chinese rankings, high scores to help you find excellent Chinese projects more efficiently absorb the outstanding achievements of people of experience; list, updated weekly.

10. mlflow


Star 5832

MLflow is open source technology from the Apache Spark team a machine-learning platform, the main open:

  • Open interfaces: with any ML library for use with algorithms, deployment tools or programming languages.
  • Open Source: Developers can easily be extended, and cross-organizational workflow steps and sharing model.

The current version MLflow is alpha version. This means that the API and data formats may vary!

11. spinningup


Star 4105

Spinning Up is the famous Open AI launched a free reinforcement learning teaching project, aimed at reducing the threshold of reinforcement learning, so that beginners can learn step by step, step by step in the course of the project in practice. Project not only provides a detailed theoretical description, as well as the full exercise code really is not worthy of study for the conscience of the depth of reinforcement learning.

【Editor's Choice】

[Editor: Zhao Ningning TEL: (010) 68476606]

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