dask-glm
Generalized Linear Models with Dask
Decision gist · record as of 2026-08-14
No. The package explicitly states it is not ready for use, has not been updated recently, and carries Beta status with an aging maintenance signal. While the install friction is low and the license is permissive, the lack of readiness and dormant development make it unsuitable for production or even reliable experimental work. Consider alternatives with active maintenance.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Package explicitly states 'This library is not ready for use' in its own description; production use is not recommended.
- Low friction install with a pure-Python wheel, but the package is explicitly marked as not ready for use and has not been updated recently despite being in Beta status.
- The maintenance signal is aging.
License · maintenance · safety
BSD-3-Clause (permissive) — BSD-3-Clause permissive license allows commercial and private use with minimal restrictions, requiring only license and copyright notice inclusion.
last release 2026-01-23 (203 days) · last repo commit 2026-01-23 · 78 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 110,399 downloads/mo, #12,464 on PyPI
Alternatives
Verify before relying
pip install dask-glm
import dask_glm
# See documentation at https://dask-glm.readthedocs.io for usage examples- Current API stability and whether the 'not ready for use' warning has been lifted since the last release.
- Whether distributed and numba dependencies are optional or required for all use cases.
- Specific GLM models and solvers currently implemented and their performance characteristics.
What it is and what it does
Dask-glm is a library for fitting generalized linear models in a distributed setting using Dask, the parallel computing framework. It integrates with Dask's task scheduler to enable model training across multiple machines or cores, leveraging dependencies like scikit-learn for model definitions, numpy and scipy for numerical computation, and numba for JIT-compiled performance. The package targets scientific and machine-learning workflows where dataset size or computational cost makes single-machine fitting impractical.
However, the package's own documentation explicitly warns that it is not ready for use, and it has not received recent updates. The Beta development status and aging maintenance signal suggest the project is dormant or abandoned. Installation is straightforward (low friction, pure-Python wheel), but adopting it for production work carries substantial risk given the explicit non-readiness statement and lack of recent activity.
Use it for
- Training logistic regression or other GLMs on datasets too large to fit in a single machine's memory using Dask clusters.
- Prototyping distributed statistical models in research or exploratory analysis where the library's experimental status is acceptable.
- Learning how to structure distributed machine-learning code using Dask's primitives and task graphs.
- Benchmarking or comparing distributed GLM implementations across different parallelization frameworks.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
No.
The package explicitly states it is not ready for use, has not been updated recently, and carries Beta status with an aging maintenance signal. While the install friction is low and the license is permissive, the lack of readiness and dormant development make it unsuitable for production or even reliable experimental work. Consider alternatives with active maintenance.
Install
dask-glm on PyPI
Before you install
Low friction install with a pure-Python wheel, but the package is explicitly marked as not ready for use and has not been updated recently despite being in Beta status. The maintenance signal is aging.
Package explicitly states 'This library is not ready for use' in its own description; production use is not recommended.
License in practice
BSD-3-Clause permissive license allows commercial and private use with minimal restrictions, requiring only license and copyright notice inclusion.
Quickstart
pip install dask-glm
import dask_glm
# See documentation at https://dask-glm.readthedocs.io for usage examplesVerify before relying
- Current API stability and whether the 'not ready for use' warning has been lifted since the last release.
- Whether distributed and numba dependencies are optional or required for all use cases.
- Specific GLM models and solvers currently implemented and their performance characteristics.
Package facts
| License | BSD-3-Clause permissive |
| Python support | Supports the current Python release >=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 9 packagescloudpickledaskdistributedmultipledispatchnumbanumpyscipyscikit-learnsparse |
| Maintenance | Aging 203 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 110,399 / month, #12,464 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 4 - BetaIntended Audience :: Science/ResearchProgramming Language :: Python :: 3Programming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.14Topic :: Scientific/Engineering |
Evidence: dask_glm-0.4.0-py3-none-any.whl
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