{"categories":[{"label":"Distributed Computing","url":"https://skillfed.io/packages/category/system-distributed-computing/3"}],"enrichment":{"capability":"Parallelbar wraps Python's multiprocessing Pool methods (map, starmap, imap, imap_unordered) to display live progress bars and handle exceptions and timeouts across worker processes.","skillfed_tags":["multiprocessing","progress-bar","parallel-execution"],"use_cases":["Parallelize CPU-intensive batch processing (e.g., image resizing, data transformation) and watch progress in real time.","Run parameter sweeps or hyperparameter searches across multiple cores with live feedback on completion.","Process large datasets with starmap to unpack argument tuples while monitoring progress and catching per-task errors.","Add timeout protection to worker tasks to prevent hung processes from blocking the entire pool.","Debug parallel code by capturing exception tracebacks from worker processes instead of losing them to the pool."],"what_it_does":"Parallelbar is a thin wrapper around Python's standard multiprocessing.Pool that adds live progress bars to parallel map operations. It sits on top of tqdm and colorama to display task completion and handles exceptions and timeouts that occur in worker processes, capturing their tracebacks and returning them in the result list alongside successful outputs.\n\nYou use it by replacing calls to pool.map() or pool.imap() with progress_map() or progress_imap(), specifying the number of CPU cores and optionally a timeout per task. The package supports exception handling (exceptions are caught and returned in-place), task timeouts (with automatic process restart), and decorators for adding progress to nested function calls within workers.","worth_installing":"Yes. Low install friction, active maintenance, permissive license, and no known vulnerabilities. Use it if you need live progress feedback on multiprocessing.Pool operations or want built-in exception and timeout handling for parallel tasks. Skip it only if you prefer raw Pool performance without monitoring overhead or use async/concurrent.futures instead of multiprocessing."},"id":"parallelbar","links":{"html":"https://skillfed.io/packages/parallelbar","md":"https://skillfed.io/packages/parallelbar.md","pypi":"https://pypi.org/project/parallelbar/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2025-01-17","license_spdx":null,"license_treatment":"permissive","name":"parallelbar","python_support":"unspecified","summary":"Parallel processing with progress bars"},"popularity":{"monthly_downloads":99992,"position":13007,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"2.5"}