scitex-parallel 0.1.6

Thread/process pool parallel execution utilities

scitex-parallel

Thread/process pool parallel execution utilities — `map` with tqdm, auto CPU count.

Full Documentation · pip install scitex-parallel

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Problem and Solution

#	Problem	Solution
1	concurrent.futures is low-level — users rewrite "map-with-progress-bar" every project	stx.parallel.run(func, args) — drop-in map with tqdm, auto CPU-count; 1-liner for I/O-bound workloads (HTTP, file reads, API calls)
2	joblib.Parallel is heavyweight + process-based by default — overkill for threaded I/O	Thread-based, no dep beyond stdlib + tqdm — right tool for the 80% case

Installation

pip install scitex-parallel

Quick Start

from scitex_parallel import run

results = run(my_func, items, n_jobs=4)

1 Interfaces

Python API

from scitex_parallel import run

# I/O-bound parallel map with tqdm progress bar.
results = run(fetch_url, urls, n_jobs=8, desc="Fetching")

# Tuple-arg form for multi-arg functions.
results = run(my_func, [(a, b) for a, b in zip(xs, ys)], n_jobs=4)

Part of SciTeX

scitex-parallel is part of SciTeX.

Four Freedoms for Research

0. The freedom to run your research anywhere — your machine, your terms.
1. The freedom to study how every step works — from raw data to final manuscript.
2. The freedom to redistribute your workflows, not just your papers.
3. The freedom to modify any module and share improvements with the community.

AGPL-3.0 — because we believe research infrastructure deserves the same freedoms as the software it runs on.

License

AGPL-3.0

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