This file describes the installation and usage of common-lisp-jupyter on a local machine, but you can try out common-lisp-jupyter without installing anything by clicking on the Binder badge above.
In developing Maxima-Jupyter there were a number of enhancements and features added that cl-jupyter does not support. Because the structure of Maxima-Jupyter is significantly different from cl-jupyter back-porting these changes would probably be difficult. Therefore common-lisp-jupyter was created as library to support both Maxima-Jupyter and the included Common Lisp kernel. The library component handles all Jupyter messaging and most of the common kernel management tasks. This leaves only code evaluation and completion testing left to the derived kernel.
- about.ipynb ? Simple examples including displaying images.
- widgets.ipynb ? Basic widget examples.
- julia.ipynb ? A Julia set explorer.
Comparison to cl-jupyter
In comparison to cl-jupyter the included kernel
common-lisp has the following features.
Markdown and PDF rendering
Handling and rendering of multiple value return
Correct setting of the REPL variables
Automatic detection of MIME types for files
Handles code inspection, code completeness checking, code completion, shutdown requests and history requests.
Can send clear output requests.
Improved JSON serialization via jsown
Improved message handling
Automatic detection of prompts on
input_requestmessage to facilitate responses.
COMM message handling and registration.
Lisp interface to core IPython widgets is included in the
common-lisp-jupyter may be installed on a machine using a local installation, a repo2docker installation, or via a Docker image.
Roswell or a system-wide installed Common Lisp implementation. Currently Clozure Common Lisp, Embeddable Common Lisp and Steel Bank Common Lisp are known to work. Other implementations which support the Bordeaux Threads package might work. For current implementation status please the Wiki.
ZeroMQ library including development headers. On debian-based systems, you can satisfy this requirement by installing the package
libczmq-dev. On Arch-based systems the package is named
zeromq. In homebrew the package is named
czmq. There are several ways to satisfy the requirement on Windows. For more details see the Windows Installation instruction in the wiki.
Installing via Roswell
Install Roswell using the Roswell Installation Guide. If you already have Roswell installed you may need to update your Quicklisp distribution with
(ql:update-dist "quicklisp")inside a
ros runshell to resolve package conflicts.
Add the PATH in the initialization file (such as
Install common-lisp-jupyter by roswell
ros install common-lisp-jupyter
Installing via Quicklisp
Install Quicklisp and use
(ql:add-to-init-file). If you already have Quicklisp installed you may need to update your distribution with
(ql:update-dist "quicklisp")to resolve package conflicts.
Start your Lisp implementation and evaluate the following. The
installcommand will try to deduce the correct command line arguments for your implementation. The keyword parameters
:ev-flagcan be used to customize these arguments. For example, for SBCL
--eval. To install a kernel image using uiop:dump-image use
install-imagetakes no arguments.
(ql:quickload :common-lisp-jupyter) (cl-jupyter:install)
common-lisp-jupyter may be run from a local installation in console mode by the following.
jupyter console --kernel=common-lisp
Notebook mode is initiated by the following.
common-lisp-jupyter may be run as a Docker image managed by repo2docker which will fetch the current code from GitHub and handle all the details of running the Jupyter Notebook server.
First you need to install repo2docker (
sudo may be required)
pip install jupyter-repo2docker
Once repo2docker is installed then the following will build and start the server. Directions on accessing the server will be displayed once the image is built.
jupyter-repo2docker --user-id=1000 --user-name=jupyter https://github.com/yitzchak/common-lisp-jupyter
A prebuilt docker image is available via Docker Hub. This image maybe run run the following command.
docker run --network=host -it yitzchak/common-lisp-jupyter jupyter notebook --ip=127.0.0.1
A local Docker image of common-lisp-jupyter may be built after this repo has been cloned using the following command (
sudo may be required). This image is based on the docker image
docker build --tag=common-lisp-jupyter .
After the image is built the console may be run with
docker run -it common-lisp-jupyter jupyter console --kernel=common-lisp
Writing Jupyter Kernels
New Jupyter kernels can be created by defining a new sub-class of
jupyter:kernel and by defining methods for the generic functions
jupyter:code-is-complete. For reference, please see cl-jupyter.lisp for the Common Lisp kernel that is included in the package.
The derived class of
jupyter:kernel should initialize the following slots. Most of these slots are used to reply to
kernel_info messages. Documentation for each can be found in the declaration of
jupyter:evaluate-code should evaluate all code included in the
input argument and return a list of evaluation results. Each result should be wrapped in an appropriate sub-class of
jupyter:result. For instance, to return a S-Expr result one would call
jupyter:evaluate-code will be called with the package declared in the kernel class as the current default. For example, the Common Lisp kernel evaluates code in the
The Jupyter message
is_complete_request is also supported via the
code-is-complete method. The return result should be one of allowed status messages, i.e.
User level installation of kernels can be accomplished by a call to
jupyter:install-kernel. cl-jupyter.lisp has an example of this call made during the installation phase of Roswell.