pybear-dask
Python packages that augment your data analytics experience.
pybear-dask is a scikit-style Python computing library that supplements the pybear library with analogous modules that have dask capability.
Python versions 3.10, 3.11, 3.12, 3.13, and 3.14 are supported.
Website: https://github.com/PylarBear/pybear-dask
License
BSD 3-Clause License. See License File.
Installation
Dependencies
pybear-dask operating on Python 3.14 requires:
Python (==3.14)
dask (>=2025.1.0)
dask-ml (>=2025.1.0)
distributed (>=2025.1.0)
pybear (>=0.2.4)
scikit-learn (>=1.7.2)
pybear-dask operating on Python 3.13 requires:
Python (==3.13)
dask (>=2025.1.0)
dask-ml (>=2025.1.0)
distributed (>=2025.1.0)
pybear (>=0.2.0)
scikit-learn (>=1.6.1)
pybear-dask operating on Python 3.12 requires:
Python (==3.12)
dask (>=2024.4.1)
dask-ml (>=2024.3.20)
distributed (>=2024.4.1)
pybear (>=0.2.0)
scikit-learn (>=1.4.2)
pybear-dask operating on Python 3.11 requires:
Python (==3.11)
dask (>=2024.4.1)
dask-ml (>=2024.3.20)
distributed (>=2024.4.1)
pybear (>=0.2.0)
scikit-learn (>=1.4.2)
pybear-dask operating on Python 3.10 requires:
Python (==3.10)
dask (>=2024.3.0)
dask-expr (>=1.0,<2.0.0)
dask-ml (>=2024.3.20)
distributed (>=2024.3.0)
pybear (>=0.2.0)
scikit-learn (>=1.3.0,<1.8)
User installation
Install pybear-dask from the online PyPI package repository using pip:
(your-env) $ pip install pybear-dask
A Conda distribution is not expected to be made available anytime soon.
Usage
The folder structure of pybear-dask is nearly identical to scikit-learn. This is so those that are familiar with the scikit layout and have experience with writing the associated import statements have an easy transition to pybear-dask. The pybear-dask subfolders are base and model_selection.
You can import pybear-dask’s packages in the same way you would with scikit. Here are a few examples of how you could import and use pybear-dask modules:
from pybear-dask.model_selection import GSTCVDask
search = GSTCVDask()
search.fit(X, y)
from pybear-dask import model_selection as ms
search = ms.AutoGridSearchCVDask()
search.fit(X, y)Major Modules
AutoGridSearchCVDask
Perform multiple uninterrupted passes of grid search with dask_ml GridSearchCV and dask objects utilizing progressively narrower search grids.
Access via pybear-dask.model_selection.AutoGridSearchCVDask.
GSTCVDask (GridSearchThresholdCV for Dask)
Perform conventional grid search on a classifier with concurrent threshold search using dask objects in parallel and distributed environments. Finds the global optima for the passed parameters and thresholds. Fully compliant with the dask_ml/scikit-learn GridSearchCV API.
Access via pybear-dask.model_selection.GSTCVDask.
AutoGSTCVDask (AutoGridSearchThresholdCV for Dask)
Perform multiple uninterrupted passes of grid search with pybear-dask GSTCVDask utilizing progressively narrower search grids.
Access via pybear-dask.model_selection.AutoGSTCVDask.
Changelog
See the changelog for a history of notable changes to pybear-dask.
Development
Important links
Official source code repo: https://github.com/PylarBear/pybear-dask
Download releases: https://pypi.org/project/pybear-dask/
Issue tracker: https://github.com/PylarBear/pybear-dask/issues
Source code
You can clone the latest source code with the command:
git clone https://github.com/PylarBear/pybear-dask.git
Contributing
pybear-dask is not ready for contributions at this time! If you have a good idea that uses dask, it may be better to try to contribute directly to dask or dask-ml.
Testing
pybear-dask 0.3 is tested via GitHub Actions to run on Linux, Windows, and MacOS, with Python versions 3.10, 3.11, 3.12, 3.13, and 3.14. pybear-dask is not supported nor tested on earlier versions.
If you want to test pybear-dask yourself, you will need:
pytest (>=8.0.0) for Python version 3.14
pytest (>=7.0.0) for Python versions 3.13, 3.12, 3.11, and 3.10
The tests are not available in the PyPI pip installation. You can get the tests by downloading the tarball from the pybear-dask project page on pypi.org or cloning the pybear-dask repo from GitHub. Once you have the source files in a local project folder, create a poetry environment for the project and install the test dependencies. After installation, open the poetry environment shell and you can launch the test suite from the root of your pybear-dask project folder with:
(your-pybear-dask-env) you@your_computer:/path/to/pybear-dask/project$ pytest tests/
Project History
This project was spun off the main pybear project just prior to the first public release of both. pybear-dask was spun off to ensure maximum stability for the main pybear project, while keeping these modules available.
Help and Support
Documentation
Documentation is not expected to be made available via a website for this package. Use the documentation for similar packages in the main pybear package. See the repo for pybear: https://github.com/PylarBear/pybear/ See the online docs for pybear: https://pybear.readthedocs.io/en/stable/index.html
Communication
GitHub Discussions: https://github.com/PylarBear/pybear-dask/discussions
Metadata
Release files for pybear-dask 0.3.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| pybear_dask-0.3.0.tar.gz | 74.7 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| pybear_dask-0.3.0-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 115.5 kB
Release files / pybear_dask-0.3.0.tar.gz
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| Uploaded via |
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PyPI Publish Attestation
PyPI verified that this artifact, at this checksum, originated from the publisher listed below.
Signed by GitHub Actions, verified by PyPI on Jul 11, 2026.
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