nbmake

What? Pytest plugin for testing and releasing notebook documentation

Why? To raise the quality of scientific material through better automation

Who is this for? Research/Machine Learning Software Engineers who maintain packages/teaching materials with documentation written in notebooks.

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Functionality

Executes notebooks using pytest and nbclient, allowing parallel notebook testing
Optionally writes back to the repo, allowing faster building of nbsphinx or jupyter book docs

Quick Start

If you have a notebook that runs interactively using an ipython kernel, you can try testing it automatically as follows:

pip install pytest nbmake
pytest --nbmake **/*ipynb

Configure Cell Timeouts

You can configure the cell timeout with the following pytest flag:

pytest --nbmake --nbmake-timeout=3000 # allows each cell 3000 seconds to finish

Allow Errors For a Whole Notebook

This configuration must be placed in the notebook's top-level metadata (not cell-level metadata).

Your notebook should look like this:

{
  "cells": [ ... ],
  "metadata": {
    "kernelspec": { ... },
    "execution": {
      "allow_errors": true,
      "timeout": 300
    }
  }
}

Allow a Cell to Throw an Exception

A cell with the following metadata can throw an exception without failing the test:

  "metadata": {
    "tags": [
      "raises-exception"
    ]
  }

Ignore a Code Cell

A cell with the following metadata will not be executed by nbmake

{
  "language": "python",
  "custom": {
    "metadata": {
      "tags": [
        "skip-execution"
      ]
    }
  }
}

Override Notebook Kernels when Testing

Regardless of the kernel configured in the notebook JSON, you can force nbmake to use a specific kernel when testing:

pytest --nbmake --nbmake-kernel=mycustomkernel

Add Missing Jupyter Kernel to Your CI Environment

If you are not using the flag above and are using a kernel name other than the default ‘python3’, you will see an error message when executing your notebooks in a fresh CI environment: Error - No such kernel: 'mycustomkernel'

Use ipykernel to install the custom kernel:

python -m ipykernel install --user --name mycustomkernel

If you are using another language such as c++ in your notebooks, you may have a different process for installing your kernel.

Parallelisation

For repos containing a large number of notebooks that run slowly, you can run each notebook in parallel using pytest-xdist.

pip install pytest-xdist

pytest --nbmake -n=auto

It is also possible to parallelise at a CI-level using strategies, see example

Build Jupyter Books Faster

Using xdist and the --overwrite flag let you build a large jupyter book repo faster:

pytest --nbmake --overwrite -n=auto examples
jb build examples

Find missing imports in a directory of failing notebooks

It's not always feasible to get notebooks running from top to bottom from the start.

You can however, use nbmake to check that there are no ModuleNotFoundErrors:

pytest \
  --nbmake \
  --nbmake-find-import-errors \ # Ignore all errors except ModuleNotFoundError
  --nbmake-timeout=20 # Skip past cells longer than 20s

Mock out variables to simplify testing

If your notebook runs a training process that takes a long time to run, you can use nbmake's mocking feature to overwrite variables after a cell runs:

{
  "cells": [
    ...,
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "nbmake": {
          "mock": {
            // these keys will override global variables after this cell runs
            "epochs": 2,
            "config": "/test/config.json",
            "args": {
              "env": "test"
            }
          }
        }
      },
      "outputs": [],
      "source": [
        "epochs = 10\n",
        "..."
      ]
    },
    ...
  ],
  ...
}

Run test logic after a cell executes

You can fetch CI secrets and run assertions after any cell by putting scripts in the cell metadata under nbmake.post_cell_execute:

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "nbmake": {
     "post_cell_execute": [
       "y = 3",
       "z = x+y"
     ]
    }
   },
   "outputs": [],
   "source": [
    "x = 1\n",
    "y = 2\n",
    "z = 0\n",
    "# this cell has a post_cell_execute that assigns y and z"
   ]
  },

Advice on Usage

nbmake is best used in a scenario where you use the ipynb files only for development. Consumption of notebooks is primarily done via a docs site, built through jupyter book, nbsphinx, or some other means. If using one of these tools, you are able to write assertion code in cells which will be hidden from readers.

Pre-commit

Treating notebooks like source files lets you keep your repo minimal. Some tools, such as plotly may drop several megabytes of javascript in your output cells, as a result, stripping out notebooks on pre-commit is advisable:

# .pre-commit-config.yaml
repos:
  - repo: https://github.com/kynan/nbstripout
    rev: master
    hooks:
      - id: nbstripout

See https://pre-commit.com/ for more...

Disable Nbmake

Implicitly:

pytest

Explicitly:

pytest -p no:nbmake

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