tqdm-multiprocess
Easy multiprocessing with tqdm and logging redirected to main process.
Decision gist · record as of 2026-08-14
No—the package is abandoned (last commit 2021-01-06, no releases since 2020-10-27) and will not receive maintenance or compatibility updates. While it has low install friction and permissive licensing, the lack of active maintenance poses a risk for projects targeting modern Python versions or expecting bug fixes. Consider alternatives that are actively maintained if you need multiprocessing progress tracking.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Worker functions must accept tqdm_func and global_tqdm as final arguments; tqdm(iterator) is not supported—you must initialize tqdm with total= and call update() manually.
- Low friction installation with only two runtime dependencies (tqdm and colorama).
- However, the package is abandoned—last commit was 2021-01-06 and no releases since 2020-10-27—so expect no maintenance, bug fixes, or compatibility updates for modern Python versions.
License · maintenance · safety
permissive license (permissive) — Licensed under MIT (permissive), so you can use, modify, and distribute freely with minimal restrictions.
last release 2020-10-27 (2117 days) · last repo commit 2021-01-06 · 43 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 694,822 downloads/mo, #5,312 on PyPI
Alternatives
Verify before relying
from tqdm_multiprocess import TqdmMultiProcessPool
import tqdm
pool = TqdmMultiProcessPool()
tasks = [(your_function, (arg1, arg2)) for arg1, arg2 in work_items]
with tqdm.tqdm(total=total_work) as global_progress:
results = pool.map(4, global_progress, tasks, error_callback, done_callback)- Whether the package works reliably with Python versions released after 2021 (last commit date).
- Performance characteristics when handling hundreds or thousands of tasks.
- Compatibility with recent versions of tqdm and colorama.
What it is and what it does
tqdm-multiprocess wraps Python's multiprocessing pool to coordinate progress bars across worker processes. It uses queues to collect tqdm updates and log messages from subprocesses and displays them cleanly in the main process, along with a global aggregate progress bar. Each worker can display multiple nested progress bars, and all logging from workers is redirected to the main process's root logger.
The package requires you to pass worker functions that accept tqdm_func and global_tqdm as extra parameters, and you must manually initialize tqdm objects with a total count and call update() rather than using tqdm's iterator mode. The design trades some convenience for the ability to show real-time progress from parallel work without overwhelming the main process.
Use it for
- Monitor progress of a batch of independent long-running tasks across multiple CPU cores with per-task and aggregate progress bars.
- Redirect logging from worker processes to a single log file or console while maintaining tqdm progress display.
- Track nested loops in parallel workers (e.g., outer loop per task, inner loops within each task) with separate progress bars for each level.
- Aggregate progress reporting for data processing pipelines that spawn multiple worker processes.
- Debug multiprocessing jobs by collecting and displaying worker logs alongside progress updates in the main process.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
No—the package is abandoned (last commit 2021-01-06, no releases since 2020-10-27) and will not receive maintenance or compatibility updates.
While it has low install friction and permissive licensing, the lack of active maintenance poses a risk for projects targeting modern Python versions or expecting bug fixes. Consider alternatives that are actively maintained if you need multiprocessing progress tracking.
Install
tqdm-multiprocess on PyPI
Before you install
Low friction installation with only two runtime dependencies (tqdm and colorama). However, the package is abandoned—last commit was 2021-01-06 and no releases since 2020-10-27—so expect no maintenance, bug fixes, or compatibility updates for modern Python versions.
Worker functions must accept tqdm_func and global_tqdm as final arguments; tqdm(iterator) is not supported—you must initialize tqdm with total= and call update() manually.
License in practice
Licensed under MIT (permissive), so you can use, modify, and distribute freely with minimal restrictions.
Quickstart
from tqdm_multiprocess import TqdmMultiProcessPool
import tqdm
pool = TqdmMultiProcessPool()
tasks = [(your_function, (arg1, arg2)) for arg1, arg2 in work_items]
with tqdm.tqdm(total=total_work) as global_progress:
results = pool.map(4, global_progress, tasks, error_callback, done_callback)Verify before relying
- Whether the package works reliably with Python versions released after 2021 (last commit date).
- Performance characteristics when handling hundreds or thousands of tasks.
- Compatibility with recent versions of tqdm and colorama.
Package facts
| License | permissive license permissive |
| Python support | Supports the current Python release >=3.6 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 2 packagestqdmcolorama |
| Maintenance | Abandoned 2,117 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 694,822 / month, #5,312 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | License :: OSI Approved :: MIT LicenseOperating System :: OS IndependentProgramming Language :: Python :: 3 |
Evidence: tqdm_multiprocess-0.0.11-py3-none-any.whl
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See also multiprocessing-logging · p-tqdm · parallelbar · tqdm-joblib · tqdm · pqdm · tdqm · tqdm-loggable · pyfunceble-process-manager · stqdm