jupyter-cache
A defined interface for working with a cache of jupyter notebooks.
Decision gist · record as of 2026-08-14
Yes, if you maintain Jupyter notebooks or documentation that re-builds frequently and contains long-running code. The package is actively maintained, has no known vulnerabilities, and low install friction. Not necessary for one-off notebooks or those with non-deterministic outputs.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.9 or later; notebooks must have deterministic outputs (same environment, no random code, no time-dependent external resources).
- Low friction install with 8 straightforward runtime dependencies.
- Actively maintained with recent commits; last release was 637 days ago but the repository shows ongoing activity as of 2026-08-03.
License · maintenance · safety
permissive license (permissive) — Licensed under MIT (permissive), so you can use, modify, and distribute the package freely with minimal restrictions.
last release 2024-11-15 (637 days) · last repo commit 2026-08-03 · 60 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 1,002,718 downloads/mo, #4,533 on PyPI
Alternatives
Verify before relying
pip install jupyter-cache
from jupyter_cache import CacheManager
cm = CacheManager()
cm.cache_notebook_file('notebook.ipynb')- Whether the cache correctly handles notebook cell dependencies and invalidation across Python package updates or git branch changes.
- Performance characteristics and cache size limits for large numbers of cached notebooks.
- Compatibility with recent versions of jupyter-book and other downstream tools that depend on this package.
What it is and what it does
jupyter-cache provides a persistent cache layer for Jupyter notebook execution outputs. It stores the outputs of notebooks so that when you rebuild documentation or re-run notebooks, the cached results can be reused instead of re-executing the entire notebook—useful when notebooks contain long-running computations or generate outputs in formats that don't naturally store results.
The package is designed for notebooks with deterministic execution: same environment, no random code, no time-dependent external resources. It separates content edits from code cell changes, so only actual code modifications trigger re-execution. The package is actively used by jupyter-book to enable fast document rebuilds and is maintained by the Executable Books project.
Use it for
- Speed up documentation builds in jupyter-book by caching notebook execution outputs across rebuilds.
- Avoid re-running expensive computations (long simulations, data processing) when only notebook text or formatting changes.
- Maintain consistent outputs across multiple notebook runs in CI/CD pipelines without re-executing deterministic code.
- Cache outputs from notebooks that depend on external data or scripts that rarely change.
- Enable parallel notebook execution with shared cache for teams working on the same documentation.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you maintain Jupyter notebooks or documentation that re-builds frequently and contains long-running code.
The package is actively maintained, has no known vulnerabilities, and low install friction. Not necessary for one-off notebooks or those with non-deterministic outputs.
Install
jupyter-cache on PyPI
Before you install
Low friction install with 8 straightforward runtime dependencies. Actively maintained with recent commits; last release was 637 days ago but the repository shows ongoing activity as of 2026-08-03.
Requires Python 3.9 or later; notebooks must have deterministic outputs (same environment, no random code, no time-dependent external resources).
License in practice
Licensed under MIT (permissive), so you can use, modify, and distribute the package freely with minimal restrictions.
Quickstart
pip install jupyter-cache
from jupyter_cache import CacheManager
cm = CacheManager()
cm.cache_notebook_file('notebook.ipynb')Verify before relying
- Whether the cache correctly handles notebook cell dependencies and invalidation across Python package updates or git branch changes.
- Performance characteristics and cache size limits for large numbers of cached notebooks.
- Compatibility with recent versions of jupyter-book and other downstream tools that depend on this package.
Package facts
| License | permissive license permissive |
| Python support | Supports the current Python release >=3.9 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 8 packagesattrsclickimportlib-metadatanbclientnbformatpyyamlsqlalchemytabulate |
| Maintenance | Actively maintained 637 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 1,002,718 / month, #4,533 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 4 - BetaFramework :: Sphinx :: ExtensionIntended Audience :: DevelopersLicense :: OSI Approved :: MIT LicenseProgramming Language :: Python :: 3Programming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.9Programming Language :: Python :: Implementation :: CPythonTopic :: Software Development :: Libraries :: Python Modules |
Evidence: jupyter_cache-1.0.1-py3-none-any.whl
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See also jupyter-book · nbmake · nbclient · nb-clean · execnb · scrapbook · session-info · nbstripout · jupytext · mkdocs-jupyter