---
id: dask-ml
version: "2025.1.0"
license: Copyright (c) 2017, Anaconda, Inc. and contributors All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following… (full text in the JSON record)
license_treatment: permissive
maintenance: aging
---

# dask-ml — A library for distributed and parallel machine learning

License: permissive · Maintenance: aging · Downloads: 119.6K/mo

## 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 above — verify before relying.

Dask-ML provides distributed and parallel machine learning by integrating Dask with scikit-learn, XGBoost, and other ML libraries to scale training and inference across clusters.

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

pip install dask-ml
uv add dask-ml
poetry add dask-ml

## Installing dask-ml

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.

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={...})

Requires Python 3.10 or later. Distributed computing setup may require additional configuration for multi-machine clusters.

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: Copyright (c) 2017, Anaconda, Inc. and contributors All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following… (full text in the JSON record) (permissive)
- Python support: supports_current
- Install friction: low
- Maintenance: aging
- Downloads: 119.6K/month (top 15,000 on PyPI)
- Known vulnerabilities: none known

## Tags

distributed machine learning, parallel machine learning, dask machine learning, scalable ML training, cluster-based ML, distributed hyperparameter tuning, large-scale model training, distributed-computing, hyperparameter-tuning

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