Cash — stop re-running notebooks that haven't changed

Two lines. Zero config. Restart-and-run-all in seconds instead of minutes.

import cash
%cash_on

That's it. The next time you re-run the notebook, every statement that hasn't changed is restored from cache instead of recomputed.

📺 Watch the 90-second demo — Cash caching a real notebook, end to end:

https://github.com/user-attachments/assets/3f376660-aeb5-4794-89cc-532a04f82f32

Or try it live in your browser — no install:

Read the docs →

Why Cash is different

Most caching tools cache cells or function calls. Cash caches statements — the individual lines inside a cell — and tracks the dependency graph between them.

• Statement-level, not cell-level. Change one line in a 20-line cell → that line and its dependents recompute. The other 19 stay cached.
• Dependency-aware. Cash builds a lineage graph. Touch config → only cells that read config (transitively) re-run.
• File-aware. Cash intercepts pd.read_csv, np.load, open, etc. Replace data.csv → dependent cells recompute automatically.
• Mutation-aware. df.append(...) and += are detected, so you don't get stale reads.
• Survives kernel restarts. The cache lives on disk by default. Restart, run the cell, get the value back instantly.
• Zero-config. %cash_on and you're done. No decorators, no config file.

Cash saves time on re-runs — restoring an unchanged result instead of recomputing it, not speeding the first execution up. The more a statement costs to compute relative to the size of its result, the more a restore saves; %cash_stats reports your actual numbers, and says so plainly when caching cost you time. See the benchmarks for how that plays out on real workloads.

Install

pip install cash-lib

Optional extras: pip install "cash-lib[all]" (pandas, polars, redis, s3, …). See installation.

Quick start

Cell 1 — turn it on:

import cash
%cash_on

Cell 2 — your normal code:

import pandas as pd
df = pd.read_csv("large_dataset.csv")   # tracked: file change → recompute
summary = df.describe()

Re-run the notebook:

• ✅ Nothing changed → restored from cache instantly.
• 🔄 large_dataset.csv changed → the cells that read it recompute.
• ⚡ Only the analysis changed → the load stays cached, only the analysis recomputes.

Cash shows a badge above each cell summarizing what it did — see Reading the Cash badge.

Beyond notebooks

@cash.cache caches any Python function across processes, with the same dependency- and file-awareness. Impure functions (LLM calls, HTTP, file writes) are flagged by default, since their side effects only run on the first call.

import cash

@cash.cache
def expensive(x):
    return x ** 2 + sum(range(x))

Full walkthrough in the decorator guide.

Learn more

• Documentation home · Why Cash? · Quick start
• Reading the badge · Magic commands · Annotations
• The decorator · Backends · Command-line interface
• Known limitations · Benchmarks · Versioning & compatibility

Status

Beta. 0.2.0 is the current release. The public API is stabilizing, but this is a 0.x release — the cache format may still change between minor versions, so run %cash_repair --full after upgrading (see versioning & compatibility). The known limitations are documented honestly.

Bug reports welcome: the badge has a "Report a bug" button, %cash_feedback prints how, or open an issue.

Contributing

See CONTRIBUTING.md. Good first issues are tagged on the tracker.

License

MIT — see LICENSE.