nbmake
Pytest plugin for testing notebooks
Decision gist · record as of 2026-08-14
Yes, if you maintain Jupyter notebooks as part of documentation, teaching materials, or package examples and want to automate their execution in CI. The low install friction and tight pytest integration make it straightforward to add. The aging maintenance status (599 days since last release) is a minor concern but not a blocker—the project is active and has no known vulnerabilities. Not necessary if notebooks are only consumed as rendered HTML and never need to be re-executed.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python >=3.8.0 and an active Jupyter kernel (default 'python3' or custom kernel via --nbmake-kernel flag).
- Low friction install with a small, focused dependency tree (ipykernel, nbclient, nbformat, pygments, pytest).
- Last release was 599 days ago; the project is in aging maintenance but remains active with no archived status.
License · maintenance · safety
Apache-2.0 (permissive) — Apache-2.0 permissive license allows use in commercial and private projects with minimal restrictions.
last release 2024-12-23 (599 days) · last repo commit 2025-03-18 · 210 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 618,179 downloads/mo, #5,734 on PyPI
Alternatives
Verify before relying
pip install pytest nbmake
pytest --nbmake **/*.ipynb- Whether parallel execution with pytest-xdist is stable for large notebook suites in production CI environments.
- Performance characteristics when mocking or skipping cells in notebooks with hundreds of cells.
- Compatibility with recent versions of nbsphinx and jupyter-book beyond the documentation examples.
What it is and what it does
nbmake is a pytest plugin that treats Jupyter notebooks as executable test suites. It runs notebook cells sequentially using nbclient and the ipykernel, capturing output and failures just like pytest captures test results. You can configure cell timeouts, allow specific cells to raise exceptions, skip cells, mock variables mid-execution, and run post-cell assertions—all via notebook metadata. The plugin integrates with pytest's standard discovery and reporting, and supports parallel execution via pytest-xdist for faster CI runs.
The primary use case is maintaining documentation notebooks that must stay runnable and up-to-date. Research and ML engineers use it to ensure teaching materials and package examples don't break as dependencies evolve. It can optionally write executed notebooks back to disk, speeding up builds for documentation tools like jupyter-book and nbsphinx that consume the output. The tool assumes notebooks are primarily developed interactively but consumed as rendered docs, not as live notebooks.
Use it for
- Validate that all code examples in a package's documentation notebooks execute without errors in CI.
- Test ML training notebooks by mocking long-running cells and asserting outputs at checkpoints.
- Ensure teaching material notebooks run end-to-end with current package versions before release.
- Parallelize execution of a large repository of slow notebooks to reduce CI time.
- Find missing imports across a directory of partially-working notebooks without running them to completion.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you maintain Jupyter notebooks as part of documentation, teaching materials, or package examples and want to automate their execution in CI.
The low install friction and tight pytest integration make it straightforward to add. The aging maintenance status (599 days since last release) is a minor concern but not a blocker—the project is active and has no known vulnerabilities. Not necessary if notebooks are only consumed as rendered HTML and never need to be re-executed.
Install
nbmake on PyPI
Before you install
Low friction install with a small, focused dependency tree (ipykernel, nbclient, nbformat, pygments, pytest). Last release was 599 days ago; the project is in aging maintenance but remains active with no archived status.
Requires Python >=3.8.0 and an active Jupyter kernel (default 'python3' or custom kernel via --nbmake-kernel flag).
License in practice
Apache-2.0 permissive license allows use in commercial and private projects with minimal restrictions.
Quickstart
pip install pytest nbmake
pytest --nbmake **/*.ipynbVerify before relying
- Whether parallel execution with pytest-xdist is stable for large notebook suites in production CI environments.
- Performance characteristics when mocking or skipping cells in notebooks with hundreds of cells.
- Compatibility with recent versions of nbsphinx and jupyter-book beyond the documentation examples.
Package facts
| License | Apache-2.0 permissive |
| Python support | Supports the current Python release >=3.8.0 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 5 packagesipykernelnbclientnbformatpygmentspytest |
| Maintenance | Aging 599 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 618,179 / month, #5,734 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
Evidence: nbmake-1.5.5-py3-none-any.whl
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See also nbval · testbook · execnb · nbdev · jupyter-cache · nbclient · nbstripout · nb-clean · ipytest · jupyter-nbmodel-client