KC Sivaramakrishnan Assistant Professor @ IIT Madras

Teaching OCaml and Prolog through Jupyter Notebooks

Last semester at IIT Madras, I taught a revamped core course CS3100 Paradigms of Programming, which introduces 3rd-year students to functional and logic programming paradigms. While the course had been traditionally offered in Lisp and Prolog, I introduced OCaml instead of Lisp. All of the lectures were delivered through interactive Jupyter notebooks. The assignments were also distributed as Jupyter notebooks and evaluated through autograder facility in Jupyter. There has since been several requests to replicate this setup elsewhere. Hence, I thought I should write about the set up and experience of teaching through Jupyter notebooks.

Course Content

Having never taken a functional programming course, there was the question of what I wanted the students to take away from the course. I wanted the course to be a mixture of functional programming concepts (types and lambda calculus) as well as advanced yet pragmatic concepts that one would find in modern functional programming languages (such as GADTs and Monads). The OCaml part of the course is based on the excellent CS3110 from Cornell and AFP from Cambridge Computer Laboratory. In particular, I would highly recommend the CS3110 book for anyone taking first steps into functional programming. Lambda calculus lectures were based on Peter Selinger’s lecture notes on lambda calculus.

The Prolog part of the course were modelled on Prolog lectures from Cambridge Computer Laboratory and the wonderful The Art of Prolog book.

Teaching functional and logic programming in the same course allowed me to develop interesting content that intersected both of the paradigms. In the functional programming part of the lecture, I had introduced simply typed lambda calculus. In the logic part of the course, we developed a type checker for simply typed lambda calculus in Prolog. Merely encoding type checking rules for simply typed lambda calculus in Prolog, type inference with polymorphic types falls out. With a tiny bit of coaxing, Prolog synthesizes programs for the given type. In the last assignment, the students were asked to implement a Prolog interpreter in OCaml. There was indeed some value in teaching multiple paradigms in the same course, not just for a comparative study of strengths and weaknesses, but to be able to teach the students to pick the right tool for the job.

Course Delivery

I had a clear idea that the course will have to be interactive where programs are developed during the lectures. There was the option of using pdf slides and switching to utop for interactive development. But this solution lacked the uniformity that the students would like when reviewing the course materials. Moreover, switching between two mediums made it difficult for me to plan the lectures and was a distraction for the students.

Jupyter Notebooks

Hence, I decided to use Jupyter Notebooks for the course. Jupyter is a collection of open source standards and software for interactive development. Jupyter supports a variety of languages. For OCaml, I used akabe/ocaml-jupyter, an OCaml kernel for Jupyter notebooks. This uses utop, an advanced OCaml top-level in the backend and hence provides excellent interactive top-level support. The situation for Prolog was not so great. Eventually, I zeroed in on targodan/jupyter-swi-prolog but ended up improving the solution a bit kayceesrk/jupyter-swi-prolog (TODO KC: upstream fixes). Jupyter supports mathjax, which allows typesetting LaTeX in the notebooks. This was great for writing the lectures on lambda calculus.

RISE for slideshow

Jupyter notebooks are webpages that mixes text and code. For lectures, I much prefer slides since they let you focus on a particular images, statement or an inference rule. While Jupyter allows the conversion of notebooks to slides out-of-band, RISE is an Jupyter notebook extension that lets turn your Jupyter notebook into a slideshow. Adding RISE to the setup makes the Jupyter experience compatible with traditional slides based lectures.

Course Distribution

Apart from delivering the lectures through the notebooks, I also wanted the students to be able to go through the notebooks and be able to run the snippets. Installing all the required software (OPAM, OCaml, Prolog, Jupyter and its extensions, Jupyter Kernels for OCaml and Prolog) and correctly was not something I wanted the students to go through. I wasn’t even sure if this software combination works on various Mac, Windows and Linux distributions. Hence, everything was packaged as a Docker file, and the latest version of the image uploaded to docker hub. In order to review the course, the students only had to install Docker and Git and run exactly 4 commands.

Docker is generally supported on all major OSes. Packaging up the course content as a docker image and pushing it to dockerhub is insurance against the software combination not working in the next offering of the course; if for some reason one of the dependency does not work next year, I can always fallback to the docker image while I find a fix. One of my TAs ran a tutorial on basic Docker and Git in the first week of the course to ensure that everyone was setup. I would consider Docker and Git as essential tools for modern software development as well as research. After that, the students did not ever have to do anything on the command line.

Assignments

nbgrader is a tool that facilitates creating and grading assignments in Jupyter notebook. It uses language-agnostic logic to identify failing cells, which meant that it was easy to set up nbgrader for OCaml and Prolog. The assignments were released as Jupyter notebooks, which the students filled in and submitted. nbgrader has support for unit tests which allowed the students to get instant feedback as they were developing the solutions.

Wish List

Overall, the students felt that the Jupyter notebooks were better than slidedecks. However, not everything was perfect with the Jupyter notebook based lecturing. Here are some of the things that could be improved.

  • There is no good diagramming + animation support for Jupyter notebooks. The best I could find was egal whose user interface I did not find intuitive. Even for simple diagrams, it was much more effort making diagrams there compared to Keynote, PowerPoint or OmniGraffle. Eventually, I used draw.io to make the diagrams and include the images in the slides for a few of the cases where I actually needed to make diagrams.
  • Docker for Windows does not work on Windows Home or Student. Support for OPAM on Windows is slowly improving, but it is not yet for novices. Hence, I had to recommend the students to run an Ubuntu VM on their Windows machines in which they ran the course’s docker container.
  • nbgrader had several bugs which caused the autograder to award marks even for failing cells. The TAs had to go through a few of the assignments manually to ensure that students were awarded grades correctly. This is something that should be fixable easily.
  • RISE doesn’t easily let you change the size of the font. One has to edit the CSS to change the font size. And the default style wastes too much space. This meant that not much content can be fit into a single slide. Hence, I’ve had to artificially split content into multiple slides or zoom out several steps to show content that was cut off on the bottom.
  • The support for Prolog is not so great. There are a few advanced features in Prolog for which the Prolog setup fails. I had to switch to SWI-Prolog top-level for a few lectures. That said, the Prolog support is mostly there and the issues can be fixed with some effort.

Conclusion

I have started working on fixing some of these issues and upstreaming the solutions. Hopefully the fixes should be ready for the next iteration of the course. If you would like to replicate this setup for your course, do feel free to utilise the course materials.


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