Racket with Jupyter

RackeTutes is a tutorial series for Racket and Rosette solver-aided programming language to promote “Secure and validated” hardware designs.

Installing dependencies, fixing issues and learning new things add too much of overhead to the learning curve. That is why this tutorial series is created.

RackeTutes series is configured to run with Jupyter notebook with racket kernel. You can run all your racket experiments within the docker container without installing anything except docker. Credits to rmculpepper for creating the kernel.

Installing Container

First, we need to install docker on your system. For that, you can follow the instructions available here.

Then we need to clone the docker image with all the configurations from the docker-hub. For that, you can use the below command while the Docker daemon is running.

docker pull archfx/rosette

This is configured with the following packages

• Racket
• Rosette
• rtlv
• jupyter
• z3

Running the Container

Now the container is cloned to our local computer. Before that let’s clone the tutorial series from the GitHub. Let’s use the following command for that after navigating to your favorite location in the terminal.

git clone https://github.com/Archfx/RACKETutes

Then, we need to run the container with the following command and mount the folder which contains our tutorials to the docker container. Here with the -v parameter, we mount the tutorial folder.

 docker run -t -p 8888:8888 -v "${PWD}/:/RACKETutes" -w /RACKETutes --name rosette archfx/rosette

This will log you into the container. If you wish to open the Jupyter notebook on a different terminal window, you can access the container with

docker exec -it rosette /bin/bash

Inside the container, you can initialize the Jupyter server by the following command.

jupyter notebook --ip 0.0.0.0 --no-browser --allow-root

Click on the link that appears on the container terminal.

If you prefer using VSCode to access your racket kernel, you can simply do it by “🖳 select kernel -> select another kernel -> Existing Jupyter Server” and copying the link from the container which looks like

http://127.0.0.1:8888/?token=91c5042b649115d20684412aeb83588ad8ce3c6ee0f96546

Enjoy coding!