dask-ml
A library for distributed and parallel machine learning
Decision gist · record as of 2026-08-14
Yes, if you need to scale machine learning workflows to multi-node clusters and are comfortable with Dask's distributed computing model. Low install friction and permissive license make it accessible. However, note the aging maintenance status—last release was February 2025—so verify that the version meets your stability and security requirements before adopting in new production systems.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.10 or later.
- Distributed computing setup may require additional configuration for multi-machine clusters.
- Low install friction with a pure-wheel distribution.
License · maintenance · safety
permissive license (permissive) — New BSD license (permissive). You may use, modify, and distribute the package freely in commercial and private projects, provided you retain the copyright notice and disclaimer.
last release 2025-02-08 (552 days) · last repo commit 2025-09-27 · 951 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 119,586 downloads/mo, #12,062 on PyPI
Alternatives
Verify before relying
pip install dask-ml
import dask_ml.model_selection
from dask_ml.model_selection import GridSearchCV
from dask import distributed
# Use GridSearchCV with Dask for distributed hyperparameter tuning
gs = GridSearchCV(estimator, param_grid={...})- Whether the aging maintenance status (552 days since last release) affects stability or security for current production use.
- Specific performance gains or cluster size thresholds at which Dask-ML becomes cost-effective versus single-machine training.
What it is and what it does
Dask-ML bridges single-machine machine learning libraries and distributed computing by wrapping popular ML tools to run on Dask clusters. It allows you to train models on datasets larger than a single machine's memory and parallelize hyperparameter search across many nodes.
The package integrates with dask, distributed, and dask-glm for cluster coordination and specialized algorithms. It exposes familiar APIs so you can often swap in Dask-ML versions with minimal code changes. It depends on numpy, pandas, scipy, numba, multipledispatch, and packaging to handle numerical operations and dependency management.
Use it for
- Hyperparameter tuning on large datasets using distributed search across a Dask cluster.
- Training models on data that exceeds available RAM by distributing work across cluster nodes.
- Scaling machine learning pipelines to production workloads without rewriting code for a different framework.
- Integrating distributed gradient boosting into a Dask workflow for multi-node model training.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you need to scale machine learning workflows to multi-node clusters and are comfortable with Dask's distributed computing model.
Low install friction and permissive license make it accessible. However, note the aging maintenance status—last release was February 2025—so verify that the version meets your stability and security requirements before adopting in new production systems.
Install
dask-ml on PyPI
Before you install
Low install friction with a pure-wheel distribution. Maintenance status is aging—last release was in February 2025 with last commit in September 2025, though the repository remains active and marked Production/Stable.
Requires Python 3.10 or later. Distributed computing setup may require additional configuration for multi-machine clusters.
License in practice
New BSD license (permissive). You may use, modify, and distribute the package freely in commercial and private projects, provided you retain the copyright notice and disclaimer.
Quickstart
pip install dask-ml
import dask_ml.model_selection
from dask_ml.model_selection import GridSearchCV
from dask import distributed
# Use GridSearchCV with Dask for distributed hyperparameter tuning
gs = GridSearchCV(estimator, param_grid={...})Verify before relying
- Whether the aging maintenance status (552 days since last release) affects stability or security for current production use.
- Specific performance gains or cluster size thresholds at which Dask-ML becomes cost-effective versus single-machine training.
Package facts
| License | permissive license permissive |
| Python support | Supports the current Python release >=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 10 packagesdask-glmdaskdistributedmultipledispatchnumbanumpypackagingpandasscikit-learnscipy |
| Maintenance | Aging 552 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 119,586 / month, #12,062 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 5 - Production/StableIntended Audience :: DevelopersLicense :: OSI Approved :: BSD LicenseProgramming Language :: Python :: 3Programming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Topic :: DatabaseTopic :: Scientific/Engineering |
Evidence: dask_ml-2025.1.0-py3-none-any.whl
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