![]() Nbdev_proc_nbs Process notebooks in `path` for docs rendering Nbdev_prepare Export, test, and clean notebooks, and render README if needed Nbdev_migrate Convert all markdown and notebook files in `path` from v1 to v2 Nbdev_merge Git merge driver for notebooks Nbdev_install_quarto Install latest Quarto on macOS or Linux, prints instructions for Windows Nbdev_install_hooks Install Jupyter and git hooks to automatically clean, trust, and fix merge conflicts in notebooks Nbdev_install Install Quarto and the current library Nbdev_help Show help for all console scripts Nbdev_fix Create working notebook from conflicted notebook `nbname` Nbdev_filter A notebook filter for Quarto Nbdev_export Export notebooks in `path` to Python modules Nbdev_docs Create Quarto docs and README.md Nbdev_create_config Create a config file. Nbdev_conda Create a `meta.yaml` file ready to be built into a package, and optionally build and upload it ![]() Nbdev_clean Clean all notebooks in `fname` to avoid merge conflicts Nbdev_changelog Create a CHANGELOG.md file from closed and labeled GitHub issues Nbdev_bump_version Increment version in settings.ini by one That installed 29 command-line utilities, which you can list using nbdev_help: nbdev works on Windows under WSL (Windows Subsystem for Linux), but not under cmd or PowerShell. It requires a recent version of Python 3 I used Python 3.9.6 on macOS Ventura, running on an M1 MacBook Pro. Nbdev works on macOS, Linux, and most Unix-style operating systems. The nbdev software works with Jupyter Notebooks, GitHub, Quarto, Anaconda, and PyPi to produce a productive, interactive environment for Python development. Git-friendly notebooks with Jupyter/Git hooks that clean unwanted metadata and render merge conflicts in a human-readable format.There is continuous integration with GitHub Actions that run your tests and rebuild your docs.Tests written as ordinary notebook cells are run in parallel with a single command.There is two-way sync between notebooks and plaintext source code, allowing you to use your IDE for code navigation or quick edits.Python best practices are automatically followed, for example, only exported objects are included in _all_. You can publish packages to PyPI and Conda as well as tools to simplify package releases.The docs support LaTeX, are searchable, and are automatically hyperlinked. You can generate documentation using Quarto and host it on GitHub Pages.To summarize what each piece of this system does: While there’s a learning curve for these utilities, that investment pays dividends, as you can be done with your development project in about the time it would normally take to simply write the code.Īs you can see in the diagram below, nbdev works with Jupyter Notebooks, GitHub, Quarto, Anaconda, and PyPI. In addition to producing correct Python code quickly, you can produce documentation and tests at the same time, save it all to Git without fear of corruption from merge conflicts, and publish to PyPI and Conda with a few commands. Jeremy Howard and Hamel Husain of fast.ai, along with about two dozen minor contributors, have come up with a set of command-line utilities that not only allow Jupyter Notebooks to play well with Git, but also enable a highly productive interactive literate programming style. That creates a problem when there are merge conflicts, as notebooks are cell-oriented and source code repositories such as Git are line-oriented. One issue with notebooks is that they sometimes don’t play well with repositories because they save too much information, including metadata that doesn’t matter to anyone. Literate programming has been used widely for scientific programming and data science, often using notebook environments, such as Jupyter Notebooks, Jupyter Lab, Visual Studio Code, and P圜harm. The drawback of this style of programming is that once you’ve written the code you have to separately pull out the tests and write the documentation, save all that to a repository, do your packaging, and publish your package and documentation.ĭonald Knuth’s literate programming paradigm prescribes writing the documentation and code in the same document, with the documentation aimed at humans interspersed with the code intended for the computer. The REPL method, which originated in LISP development environments, is well-suited to Python programming, as Python has always had good interactive development tools. One of the most productive paradigms is interactive: You use a REPL (read-eval-print loop) to write and test your code as you code, and then copy the tested code into a file. There are many ways to go about programming. ![]()
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