stet 0.0.1

Persistent memoization by parameter identity for experiment scripts

stet

A Python library for making parameter sweeps safely resumable.

When a long-running experiment script is re-run — whether after a crash, a time limit, or deliberately to extend a sweep — stet automatically skips any parameter combinations that have already been completed.

import stet

@stet.once(store='markov_runs.csv', key=['alpha', 'n_states', 'seed'])
def solve_markov(alpha, n_states, seed, n_iter=10_000):
    # expensive computation
    ...

for alpha in alphas:
    for n_states in [10, 50, 100]:
        for seed in range(20):
            solve_markov(alpha=alpha, n_states=n_states, seed=seed)

On restart, any already-completed (alpha, n_states, seed) combinations are skipped:

[once] Skipping solve_markov(alpha=0.01, n_states=10, seed=0)
[once] Skipping solve_markov(alpha=0.01, n_states=10, seed=1)
...

Installation

uv add stet
# or: python -m pip install stet

With Parquet support:

uv add stet[parquet]
# or: python -m pip install stet[parquet]

Usage

Zero config — uses _stet_store.csv in the current directory, all parameters as the key:

@stet.once
def run_experiment(alpha, seed):
    ...

Named store — backend selected from file extension (.csv, .json, .sqlite, .parquet):

@stet.once(store='runs.sqlite')
def run_experiment(alpha, seed):
    ...

Key subset — only alpha and seed determine whether a run is skipped; n_iter is ignored:

@stet.once(store='runs.csv', key=['alpha', 'seed'])
def run_experiment(alpha, seed, n_iter=1000):
    ...

Utilities

stet.status()            # print a summary of completed runs
stet.reset()             # clear the store
stet.reset(key_dict={'alpha': '0.1', 'seed': '42'})  # remove one entry

Storage backends

Extension	Backend	Notes
.csv	pandas CSV	Default. Human-readable.
.json	stdlib json	No extra dependencies.
.sqlite	stdlib sqlite3	Best for large stores and parallel workers.
.parquet	pandas + pyarrow	Requires stet[parquet].

Documentation

Full documentation including how-to guides, API reference, and explanation of design decisions:

uv run mkdocs serve