Use airflow to author workflows as directed acyclic graphs (DAGs) of tasks. The airflow scheduler executes your tasks on an array of workers while following the specified dependencies. Rich command line utilities make performing complex surgeries on DAGs a snap. The rich user interface makes it easy to visualize pipelines running in production, monitor progress, and troubleshoot issues when needed.

Black makes code review faster by producing the smallest diffs possible. Blackened code looks the same regardless of the project you’re reading. Formatting becomes transparent after a while and you can focus on the content instead.

CircleCI automatically runs your build in a clean container or virtual machine, allowing you to test every commit. CircleCI integrates with GitHub, GitHub Enterprise, and Bitbucket.

Colaboratory, or "Colab" for short, allows you to write and execute Python in your browser, with

Zero configuration required
Free access to GPUs
Easy sharing

Open-source Version Control System for Machine Learning Projects

Flake8 is a wrapper around these tools: PyFlakes; pycodestyle; Ned Batchelder’s McCabe script. Flake8 runs all the tools by launching the single flake8 command. It displays the warnings in a per-file, merged output.

Jupyter supports interactive data science and scientific computing across all programming languages. Jupyter Notebook and its flexible interface extends the notebook beyond code to visualization, multimedia, collaboration, and more.

Luigi is a Python module that helps you build complex pipelines of batch jobs. It handles dependency resolution, workflow management, visualization etc. It also comes with Hadoop support built in.

This document gives coding conventions for the Python code comprising the standard library in the main Python distribution. Please see the companion informational PEP describing style guidelines for the C code in the C implementation of Python [1]. This document and PEP 257 (Docstring Conventions) were adapted from Guido's original Python Style Guide essay, with some additions from Barry's style guide [2].

PyCharm knows everything about your code. Rely on it for intelligent code completion, on-the-fly error checking and quick-fixes, easy project navigation, and much more.

Pytest is a mature full-featured Python testing tool that helps you write better programs. The pytest framework makes it easy to write small tests, yet scales to support complex functional testing for applications and libraries.

To allow researchers to quickly disseminate the results of their research. This will considerably increase the potential of citations of scientific papers. To provide a large community of users with the ability to use the latest scientific methods. This will speed up the process of converting scientific results into productive forces. To allow members of the academic community (researchers, editors, referees, etc.) to replicate scientific results and to demonstrate their robustness. This will increase transparency and trust in science.

The simplest way to test and deploy your projects. Easily sync your GitHub projects with Travis CI and you'll be testing your code in minutes.

Visual Studio Code is a lightweight but powerful source code editor which runs on your desktop and is available for Windows, macOS and Linux. It comes with built-in support for JavaScript, TypeScript and Node.js and has a rich ecosystem of extensions for other languages (such as C++, C#, Java, Python, PHP, Go) and runtimes (such as .NET and Unity).