Package Health Score

51 / 100

security
Security review needed
popularity
Recognized
maintenance
Inactive
community
Sustainable

Popularity

Recognized

GitHub Stars: 162
Forks: 43
Contributors: 20

Direct Usage Popularity

Based on project statistics from the GitHub repository for the PyPI package dask-xgboost, we found that it has been starred 162 times.

Security

Security review needed

Security and license risk for latest version

Release Date: Jul 14, 2021
Direct Vulnerabilities: 0
C
0
H
0
M
0
L
Indirect Vulnerabilities: 0
C
0
H
0
M
0
L
License Risk: 0
H
0
M
0
L

All security vulnerabilities belong to production dependencies of direct and indirect packages.

License

BSD-3-Clause

Security Policy

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Maintenance

Inactive

archived

Commit Frequency

No Recent Commits

Open Issues: 28
Open PR: 5
Last Release: 3 years ago
Last Commit: 3 years ago

Further analysis of the maintenance status of dask-xgboost based on released PyPI versions cadence, the repository activity, and other data points determined that its maintenance is Inactive.

An important project maintenance signal to consider for dask-xgboost is that it hasn't seen any new versions released to PyPI in the past 12 months, and could be considered as a discontinued project, or that which receives low attention from its maintainers.

In the past month we didn't find any pull request activity or change in issues status has been detected for the GitHub repository.

Community

Sustainable

Readme: Yes
Contributing.md: Yes
Code of Conduct: No
Contributors: 20
Funding: Yes

With more than 10 contributors for the dask-xgboost repository, this is possibly a sign for a growing and inviting community.

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Package

Python Versions Compatibility: Unspecified

Age: 8 years
Latest Release: 3 years ago
Dependencies: 3 Direct / 20 Total
Versions: 9
Maintainers: 3
Wheels: OS Independent

Readme

Dask-XGBoost

Warning

Dask-XGBoost has been deprecated and is no longer maintained. The functionality of this project has been included directly in XGBoost. To use Dask and XGBoost together, please use xgboost.dask instead .

Distributed training with XGBoost and Dask.distributed

This repository offers a legacy option to perform distributed training with XGBoost on Dask.array and Dask.dataframe collections.

pip install dask-xgboost

Please note that XGBoost now includes a Dask API as part of its official Python package. That API is independent of dask-xgboost and is now the recommended way to use Dask adn XGBoost together. See the xgb.dask documentation here https://xgboost.readthedocs.io/en/latest/tutorials/dask.html for more details on the new API.

Example

from dask.distributed import Client
client = Client('scheduler-address:8786')  # connect to cluster

import dask.dataframe as dd
df = dd.read_csv('...')  # use dask.dataframe to load and
df_train = ...           # preprocess data
labels_train = ...

import dask_xgboost as dxgb
params = {'objective': 'binary:logistic', ...}  # use normal xgboost params
bst = dxgb.train(client, params, df_train, labels_train)

&gt;&gt;&gt; bst  # Get back normal XGBoost result


predictions = dxgb.predict(client, bst, data_test)

How this works

For more information on using Dask.dataframe for preprocessing see the Dask.dataframe documentation.

Once you have created suitable data and labels we are ready for distributed training with XGBoost. Every Dask worker sets up an XGBoost slave and gives them enough information to find each other. Then Dask workers hand their in-memory Pandas dataframes to XGBoost (one Dask dataframe is just many Pandas dataframes spread around the memory of many machines). XGBoost handles distributed training on its own without Dask interference. XGBoost then hands back a single xgboost.Booster result object.

Larger Example

For a more serious example see

History

Conversation during development happened at dmlc/xgboost #2032

dask-xgboost dependencies

dask distributed xgboost

FAQs

What is dask-xgboost?

Interactions between Dask and XGBoost. Visit Snyk Advisor to see a full health score report for dask-xgboost, including popularity, security, maintenance & community analysis.

Is dask-xgboost popular?

The python package dask-xgboost receives a total of 26,556 weekly downloads. As such, dask-xgboost popularity was classified as a recognized. Visit the popularity section on Snyk Advisor to see the full health analysis.

Is dask-xgboost well maintained?

We found indications that dask-xgboost is an Inactive project. See the full package health analysis to learn more about the package maintenance status.

Is dask-xgboost safe to use?

While scanning the latest version of dask-xgboost, we found that a security review is needed. A total of 0 vulnerabilities or license issues were detected. See the full security scan results.

Last updated on 31 October-2024, at 16:00 (UTC).

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dask-xgboost

Interactions between Dask and XGBoost For more information about how to use this package see README

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Dask-XGBoost

Example

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dask-xgboost dependencies

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Readme

Dask-XGBoost

Example

How this works

Larger Example

History

dask-xgboost dependencies

FAQs

What is dask-xgboost?

Is dask-xgboost popular?

Is dask-xgboost well maintained?

Is dask-xgboost safe to use?

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