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Jupyter Notebooks Are Dev Tools With nbdev

by Mike Levin

Friday, April 08, 2022

Well, I just copy/pasted that entry from yesterday about whatsametafor.io and nbdev out to that site. In fact, this very entry now will probably be the second journal-style entry over there. Get stuff kicked off. You can just start pushing stuff out as markdown transformed into HTML through the Jekyll system. That’s good for now. It’s a solution that abides by the 80/20-rule to large degree. It’s the github.io documentation publishing system that this trick uses, along with the associating of custom registered domains with github.io spaces. It’s a good trick.

As I get more advanced, I’ll use some sort of “make” process to build these journal entries from whatever, and perhaps to paginate and organize them into a more user-friendly surfable tree-structure or other navigation system. I’m keeping it open ended, because just like my YouTube videos, I’m doing this writing more for me than for you. If it ever helps you out in any way, I am of course glad I could do some good in this world. But it’s more about total immersion and practice in this Linux, Python, vim & git methodology I advocate.

Adeptness in LPvg doesn’t come easy and requires lots of practice. It’s with that practice, organizing content over a diverse set of sites will also come easy. It’s just copy/paste between text files, and eventually running some scripts to put things in place. The scripts can of course be triggered with a git push integration these days, but that’s getting ahead of myself. I’m focusing on nbdev and my advancements over the past few days right now. That’s where I push my own personal state-of-the-art forward.

Okay, just dump out your new realizations. This belongs on whatsametafor.io because it is all about tooling-up. Isn’t that my linuxpythonvimgit.com site? Well, I’ll sort that all out later. This is heady stuff and quite derivative from my LPvg approach, because it plays off of the honorary-mention of JupyterLab in my essential toolkit. JupyterLab is a game-changer for a number of reasons I’ll cover in a separate article. But when the time comes to do more advanced things than the Python execution context of Jupyter (clicking Run in browser) provides, there are basically 2 approaches: Papermill to parameterize Notebooks (still .ipynb) or nbdev to “extract” package.py files appropriate for parametrized use in Linux scheduler/pipeline systems or even releasing as pip installable PyPI packages for publishing and sharing.

Clearly I went the nbdev route since I’ve been talking about it so much. In fact, I’m quite enamored with the approach and am willing to forgive a “make” process for the .py-file package-extraction just as happily as I’m embracing the web browser as a portal to a standard CPython.exe. For you see, Jupyter really is based on a fairly recent but always quite standard official Python release, and so all the skills and techniques you learn, even while running Python in a web browser, are 100-percent applicable to a long-term career involving Python, Jupyter or not.

I wasn’t keen on Jupyter at first but it won me over. I like generic old-school tools. I dislike Excel and I’m just this side of Linux guru commands like awk and gcc. I mostly use minimal Linux, Python, vim and git. That means no desktop graphical user interface. Mikey likey generic command-line interface. Generic means obsolescence resistant, disruption proof and a good candidate for the sort of muscle memory you use for natural recording and playing back keyboard macros. This is vim. It’s a zen-zoning thing. So it was to my great surprise I’ve added Desktop Jupyter to my indispensable toolkit.

I learned Notebooks on Jupyter Classic as running in a browser tab and installed by Anaconda. I fell in love with its unique exploratory coding environment and how you could mix in documentation and really talk about process. It was perfect for my field of search engine optimization (SEO), a form of data jockeying. I jumped to JupyterLab when it came out for the better user interface and promise of data-dashboard-building possibilities (as made clear by the dask package).

Anaconda changed their terms of service and Jupyter put out a standalone Web app version, and the world has changed. Nobody knows it yet, but stand-alone Jupyter changes everything. There is now the one clear best stepping stone into programming for newcomers… period. Not newcomers to Python in particular or even just Data Science or Machine Learning. But everybody with a mainstream desktop Windows, Mac or Linux can write and share in language and in a way where the skills will directly last them the rest of their lives.

Microsoft got close with their Store install when you type python from a CMD window and how it subsequently guides you to VSCode as a way to write and test your code. You’ll get a very recent Python 3.10 that works as python.exe filename.py but you’ll never have to because all of your interaction will be through VSCode. And if you’re fine with being dependent on vendor tools until the next time fads change everything and you have to retrain your muscle memory all over again, then you’re good. It’s a very cool aid.

But now that you don’t need all of Anaconda on your machine to get to the good part, Jupyter, then if you can find it, the Microsoft Installer file (MSI) for Jupyter provides a much better on-ramp to the wonderful world of coding in general and Python in particular than anything else I’ve experienced. Of course Mac and Debian/RPM have their own installers. We all win when JupyterLab works well stand-alone in this world of Web-apps and wheels.

I’m over 10 years int Python now, mostly though the vim text editor and headless Linux servers. That 10 years of Python was also in “fool-me-twice” mode. In other words, I’m determined to not get fooled again. And so I’ve steered clear of particular vendors and “vibes” of products that have steered me wrong in the past. And when I say steered me wrong, I particularly mean that the hard-won muscle-memory-bound skills of text-editor adeptness, automatic macro-making ability and such. I’ll always build upon what I know. I was quite happy to add new API-extensions to my Python know-how, such as list comprehensions, lambdas, the context manager, the walrus operator and such. But these all get layered-in over the years without invalidating all your past work. Python’s only at version 3 after 30 years after all. That says a lot about API stability.

Notebooks with a .ipynb extension or all the same Jupyter features in VSCode with a .py extension? Going the VSCode route with .py extensions is indeed very tempting and may even be the better choice for “pure” developers. Certainly if you’re using NodeJS in a “full web stack” you’re going to need VSCode. You can’t really manage the complexity of the directory structures and build process without vendor power-tools. But if the current fads of WebDev aren’t an artificially limiting criteria for you today, using actual .ipynb (Notebook) files through JupyterLab has its advantages.

Top among them is that Notebooks are intended to read like, well, Notebooks. You can go back and forth between code and documentation. The documentation are cells which are switched from their default mode as Code (Python by default) to Markdown. When in Markdown mode, a cell lets you use the abbreviated easy notation for making headlines, sub-heads, paragraphs, lists, code excerpts and the like without the overhead of full-fledged HTML tags. In other words, all those pointy brackets go away and you can focus on just content.

Wait, what? Focus on content in programming code? Yes, where better! Python’s top criteria when Guido van Rossum wrote the language was readability. Readable code is long-lived useful code. People having to inherit and take over other peoples code don’t have to adapt to style that much. Sure there will be some style, but the more people program in Python over time, the more styles converge onto a Pythonic ideal that makes you just as self-satisfied with your idyllic solution as in a PERL code obfuscation contest. Different vibes. Python’s got a good readable vibe that frees your documentation efforts up for really high level stuff.

In other words, because the operation of Python code is in so many cases, self-evident, your documentation can talk more about overarching purpose than the mechanics. Or when you do talk about the mechanics, it’s really something worth talking about because implementation isn’t immediately obvious.

Okay, so Notebooks good. Notebooks in standalone desktop Jupyter even better. .ipynb files have a reason to exist that doesn’t go away because VSCode can apply the Juptyer REPL-engine (what makes exploratory programming possible) to .py files in VSCode. In case you didn’t know, you can execute code in VSCode block-by-block just like in Jupyter because VSCode detects a Jupyter installation and “lifts” those features. You’ve got to love a Free and Open Source Software world. All sorts of feature-embedding Kung Fu is possible. And some of this VSCode Kung Fu appears at first glance to invalidate, or at least obsolete .ipynb Notebooks. As evidence they don’t I present to you the continuing growing popularity of Notebooks in all places where their Notebook qualities are actually an asset. Data Science, Finance and my field of SEO are three that pop to mind.

I have to force my way through next steps. I’ve recently overcome a number of hurdles working a Notebook developer tool, appropriately named nbdev, into my work-flow. It wasn’t easy. It still isn’t easy. But I have faith that it will become easy because the benefits are just so big. All those upsides of Notebooks has the one whopping big downside that when it comes time to automate, you need to either take the code out of the .ipynb Notebook environment, or you need ways to run your Notebooks a lot more like they were standalone .py-files, right down to supporting parameterized input so schedulers/pipelines can apply the same processes against different input.

As I mentioned earlier in this article, when faced with the choice between the two leading packages for these purposes, you have to pick a “vibe” such as it were. The vibe of Papermill works the actual .ipynb Notebooks into a parameterized, schedule-able, and otherwise automateable component. This maintains a Notebook-centric vibe. While appealing at first, the alternative sprang out of fast.ai and a Literate Programming advocate, Jeremy Howard. You really have to listen to Jeremy Howard speak to appreciate how deep this goes. Google the still-living computer scientist Donald Knuth and Literate Programming to appreciate the vision Jeremy’s trying to achieve with nbdev.

Honestly, the way best-of-breed components build upon each other these days, it should be no surprise that someone connected the dots between components to release such potential. In particular, the iPython “REPL” engine existed before the browser-based version we know as Jupyter. iPython would become literally millions of times more accessible if only you could get to it through the familiar web browser user interface. So the dots were connected between iPython, Chrome browser components via the Electron project, NodeJS with all the components talking to each other through ZeroMQ. For those familiar with how the lightweight Flask weboframework sits on top of Werkzeug and the Jinja templates to release such vast potential by cleverly connecting the dots on the underlying components, it’s the same thing.

nbdev is a bit more subtle, but along the same lines. Arguably, Project Jupyter itself is serving the role of Flask, gluing together iPython, Electron and ZeroMQ, creating what we know today as the Notebook phenomenon. For those not familiar with the broader Notebook product category in academia that has been around for awhile that Jupyter has become the FOSS (free and open source) leader, it’s been dominated by such products as MATLAB (not to be confused with the popular Python package Matplotlib) and Wolfram Mathematica. These are products that math students in college use to solve all sorts of mathematical problems while embedding student notes. This field has been somewhat disrupted by Project Jupyter. While MATLAB and Mathematica are both commercial products with licensing fees, Jupyter is both free and open source and comes by default with Python, leading to a much greater ability to integrate whatever analytical processes you explored your way to in a Notebook to 24x7 automated systems.

Jupyter made a huge impact maybe a decade ago when the field of Science (in general) had a crisis of reproducibility. Studies showed that over 1/3 of all published scientific research was unreproducible by scientific colleagues given the same original data-set and the published findings. Given that a main tenant of science is that given the same initial conditions and processes, you can expect the same results, this was a problem. And nobody wanted to require the payment of commercial licenses to MATLAB or Mathematica to distribute reproducible science. Jupyter Notebooks were in ready-shape to step in and fill this vacuum. It was a perfect storm. So now a lot of people are in Notebooks working in “Lost” mode. That is, they have to sit there and press the button.

nbdev bridges that gap between Notebooks and 24x7 information technology systems. By that I mean that the very same code as is in the Notebook is the production-ready code being used in webservers or schedulers. You could imagine something that looks much like a data dashboard inside of Jupyter can be transplanted into a web environment so that a web address will reproduce that same dashboard in browser, or alternatively be emailed as a PDF attachment on a daily basis or updating a Google Sheet. Bridging this gap with nbdev is not merely a stupid Notebook-trick (as it would be with Papermill). Instead, it’s easing you into the world of standard (Notebook-less) Linux/Python code-running contexts.

Okay, so pip install nbdev. Easy enough, right? If you’re on Windows using the Windows Subsystem for Linux (WSL or WSL2), then you have to pip install nbdev a second time over on the Linux side. I highly recommend this because the tools are developed on a Unix/Linux like environment (Mac, I think) and using the command-line tools just works more smoothly. That’s not to say you can’t use the nbdev command-line tools from the Windows side, through say PowerShell. But I wholeheartedly believe that it’s easier in Linux, and this of all places should be where you begin your venture into the Unix/Linux command-line interface (CLI), also known as the Terminal or SSH Shell. It’s the “old way” that will never go obsolete.

I work in JupyterLab Desktop for Windows. This means that all the environment paths are of the oddball Windows sort. Consequently, it’s good to take up operating system independent habits such as using pathlib.Path from the Python standard library for all file interaction. Keeping your paths OS-independent will generally mean that you’re coding in a platform-independent fashion.

Now here’s the thing. That concept of you coding in a platform-independent fashion comes off as so many buzzwords. It is not. This is significant and new and different. Once acquiring some pretty good Python skills and a basic command of the “naked” or “headless” Linux code execution context (Raspberry Pi’s, cloud servers, etc.), you have always applicable, always relevant skills for a lifetime. The joke goes that when our machine children return to Earth in a million years to see what’s become of use, they’ll still have a Linux boot kernel. Learning this stuff has a long shelf-life and high degree of applicability.

So really it’s about “taking control” of a piece of hardware. Anyone can write a bit of code in Jupyter. But to really make it have an ongoing impact on the world, it needs to start running when some server somewhere starts running, and keep running until you want it to stop. So interestingly, the seldom-discussed big trick in tech is getting some code running as if it were an operating system service auto-started at boot. That’s the Frankenstein moment of breathing life into a previously powerless piece of equipment. That’s where real power comes from in tech. Anyone can install, run and use other peoples’ products. But I’m talking about having the generic ability to “make products”.

Recently thanks to some standardization in the Linux distros, there’s a “simple enough” boot sequence, service-launcher (daemons), scheduler called systemd. d is for daemon. Simple enough? It was copied from a pre-existing Mac OSX system manager called launchd. If you want to learn Linux a little beyond the ssh, cd and ls commands so you can take full control of a machine, you want to learn some systemd. We’re about 10 years into systemd and most major Linux distros are using it, so it looks like we’ve got a winner. Learn how to start services at boot-time essentially turning it into an appliance (setting up Docker images, for example) and you will appear to be a tech deity.

Okay, so once you’ve pip installed nbdev, what’s different? What’s changed in JupyterLab? Well, almost nothing you’ll notice straight away. Instead, there’s a prescribed workflow that kicks in. In this way, it’s reminiscent of data pipelining software or certain frameworks in which you simply adhere to a certain convention, and you’ll get some magical benefits.

First, you must know that there are command-line commands that have been installed onto your system. They go by such names as nbdev_new, nbdev_build_lib, nbdev_clean_nbs and such. They should be executed from the same command-line environment that runs your Python. This has presented a unique challenge for me with Desktop Jupyter for Windows.

Flash-update! I just tested the default Terminal configuration in Jupyter 3.3.2-2 and a Microsoft PowerShell is properly configured! Pip is working from there. The same Python 3.8.13 can run from there. I even tested a few of the nbdev commands, including command-line completion in PowerShell to cycle through all the different nbdev commands. nbdev_clean_nbs and nbdev_build_lib both worked. nbdev_build_docs had problems, but I only really got that working on the Linux side for the first time yesterday controlling the issues like hiding the async function. So now the PowerShell that comes up as a default part of JupyterLab Desktop has just now become very equivalent to the Anaconda Prompts did in the past. This is good news for people who haven’t taken the WSL Linux plunge on Windows yet. Don’t use this as an excuse not to.

On the WSL Linux side, I’m running Ubuntu 20.04. I boot directly into my daily working virtualenv where I’m running Python 3.10, which is slightly ahead of my 3.8 on Jupyter. I steer clear of the new pattern matching filter functions for now. I’m looking forward to Jupyter being on 3.10.

The main gist of nbdev, you can get at their tutorials. Here’s their minimal example and here’s their general tutorial

I’ll give you my main take-aways.

You can’t just make a new folder and nbdev_new in there. You’ve got to have a git remote endpoint and the easiest way to do that is to host it on Github. Also it should be public because of something about installing the git hooks. You can switch the repo back to private after running nbdev_new. Also if you don’t use nbdev_new, you can clone this template.

Once nbdev_new runs successfully, the next important thing to know is that there are fields that really have to be filled-in in settings.ini, including keywords and description. Some of them are edited out on the default and HAVE TO be edited back in.

There’s a bunch of commands in nbdev, but the main commands that I’ll use in my release process are:

I also like to make sure all my pip installable libraries are up-to-date. This can occasionally break nbdev. The package in particular that has to be locked at a certain version right now for the nbdev_build_docs procedure is:

!pip install -U mistune==0.8.4

Okay, so what I want to figure out most is how the html files in /docs get published. Clearly the index.html becomes README.md for Github’s main project page.