Main features#

  • There is one main command, kosu, which execute one of several sub-commands:

    • build — Build a course or courses. Reads the course’s YAML file, finds and processes the notebooks in prod, compiles the course directory, and compresses everything into a ZIP file for course participants. This command has several options, see kosu build --help.

    • clean — Remove the build files associated with a course or courses.

    • init — Initialize a directory to start using kosu.

    • publish — Publish a course or courses to AWS.

    • test — Test that a course builds.

  • You may only ever need to use init once, when you first start using kosu.

  • There is a global control file, .kosu.yaml, which contains some parameters you will want to set and maintain.

  • All of the other commands take either a single course name, or the --all flag, which applies the command to all the courses listed in .kosu.yaml under the all key.

  • There is one control file per course, e.g. geocomp.yaml. This file contains the metadata for the course, including the curriculum and a list of its notebooks.

  • There is one main, common environment file, environment.yaml. This contains packages to be installed for (i.e. common to) all courses. A course’s YAML control file lists any other packages to install for that class.

Course project structure#

The tool is designed to be used in a directory with the following structure:

├── environment.yaml
├── example_course.yaml
├── images
│   └── agile_logo.png
├── .kosu.yaml
├── prod
│   ├── Interesting_notebook.ipynb
│   ├── Intro_to_Matplotlib.ipynb
│   ├── Intro_to_NumPy.ipynb
│   └── Intro_to_Python.ipynb
├── references
│   └── useful.pdf
├── scripts
│   └── example.py
└── templates
    └── README.md

What happens during course compilation?#

The course folder is built by kosu build example_course in the following way:

  • A course folder is created in a (new if necessary) folder called build.

  • A course README is built by placing the curriculum in the README template.

  • The notebooks in the curriculum are processed as described below.

  • Images required by the notebooks (detected automatically) are copied into an images folder.

  • Scripts are copied into master and notebooks so they can be imported into any notebook.

  • References are copied over into a references folder.

  • We almost always import data from Amazon S3, making data files easy to detect. Accordingly, we check that data files mentioned in the notebooks are present in the S3 bucket.

  • If some data files are to be included in the course repo, they are downloaded from the bucket and unzipped if necessary.

  • A conda environment file is constructed from the various requirements in the global env file and the course control YAML file. (Dependencies are not yet detected from the notebooks.)

  • The course folder is optionally zipped and optionally uploaded to S3.

  • Build files are optionally cleaned up.

Jupyter Notebooks in the prod directory are processed in various ways:

  • Two copies of the Notebooks are included in the course folder: one goes into the master folder, the other into notebooks. Students use the latter in the class.

  • Cells with exercise tags are highlighted in green in the student notebooks.

  • Cells with info tags are highlighted in blue in the student notebooks.

  • Cells with hide tags are hidden in the student notebooks.

  • Cell outputs are deleted in the student notebooks.