View autogluon.tabular on Snyk Open Source Advisor

Package Health Score

87 / 100

security
No known security issues
popularity
Popular
maintenance
Sustainable
community
Active

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Popularity

Popular

GitHub Stars: 8.05K
Forks: 928
Contributors: 120

Direct Usage Popularity

Based on project statistics from the GitHub repository for the PyPI package autogluon.tabular, we found that it has been starred 8,051 times.

Security

No known security issues

Security and license risk for latest version

Release Date: Jun 14, 2024
Direct Vulnerabilities: 0
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0
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Indirect Vulnerabilities: 0
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0
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License Risk: 0
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All security vulnerabilities belong to production dependencies of direct and indirect packages.

License

Apache-2.0

Security Policy

Yes

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Maintenance

Sustainable

Commit Frequency

Open Issues: 339
Open PR: 18
Last Release: 5 months ago
Last Commit: 4 days ago

Further analysis of the maintenance status of autogluon.tabular based on released PyPI versions cadence, the repository activity, and other data points determined that its maintenance is Sustainable.

We found that autogluon.tabular demonstrates a positive version release cadence with at least one new version released in the past 12 months.

As a healthy sign for on-going project maintenance, we found that the GitHub repository had at least 1 pull request or issue interacted with by the community.

Community

Active

Readme: Yes
Contributing.md: Yes
Code of Conduct: Yes
Contributors: 120
Funding: No

A good and healthy external contribution signal for autogluon.tabular project, which invites more than one hundred open source maintainers to collaborate on the repository.

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Package

Python Versions Compatibility: <3.12,>=3.8

Age: 4 years
Latest Release: 1 day ago
Dependencies: 0 Direct
Versions: 1.47K
Maintainers: 1
Wheels: OS Independent

Readme

Fast and Accurate ML in 3 Lines of Code

Installation | Documentation | Release Notes

AutoGluon automates machine learning tasks enabling you to easily achieve strong predictive performance in your applications. With just a few lines of code, you can train and deploy high-accuracy machine learning and deep learning models on image, text, time series, and tabular data.

💾 Installation

AutoGluon is supported on Python 3.8 - 3.11 and is available on Linux, MacOS, and Windows.

You can install AutoGluon with:

pip install autogluon

Visit our Installation Guide for detailed instructions, including GPU support, Conda installs, and optional dependencies.

⚡ Quickstart

Build accurate end-to-end ML models in just 3 lines of code!

from autogluon.tabular import TabularPredictor
predictor = TabularPredictor(label="class").fit("train.csv")
predictions = predictor.predict("test.csv")

AutoGluon Task	Quickstart	API
TabularPredictor
MultiModalPredictor
TimeSeriesPredictor

🔍 Resources

Hands-on Tutorials / Talks

Below is a curated list of recent tutorials and talks on AutoGluon. A comprehensive list is available here.

Title	Format	Location	Date
📺 AutoGluon 1.0: Shattering the AutoML Ceiling with Zero Lines of Code	Tutorial	AutoML Conf 2023	2023/09/12
🔉 AutoGluon: The Story	Podcast	The AutoML Podcast	2023/09/05
📺 AutoGluon: AutoML for Tabular, Multimodal, and Time Series Data	Tutorial	PyData Berlin	2023/06/20
📺 Solving Complex ML Problems in a few Lines of Code with AutoGluon	Tutorial	PyData Seattle	2023/06/20
📺 The AutoML Revolution	Tutorial	Fall AutoML School 2022	2022/10/18

📝 Citing AutoGluon

If you use AutoGluon in a scientific publication, please refer to our citation guide.

👋 How to get involved

We are actively accepting code contributions to the AutoGluon project. If you are interested in contributing to AutoGluon, please read the Contributing Guide to get started.

🏛️ License

This library is licensed under the Apache 2.0 License.

FAQs

What is autogluon.tabular?

Fast and Accurate ML in 3 Lines of Code. Visit Snyk Advisor to see a full health score report for autogluon.tabular, including popularity, security, maintenance & community analysis.

Is autogluon.tabular popular?

The python package autogluon.tabular receives a total of 59,243 weekly downloads. As such, autogluon.tabular popularity was classified as a popular. Visit the popularity section on Snyk Advisor to see the full health analysis.

Is autogluon.tabular well maintained?

We found indications that autogluon.tabular maintenance is sustainable demonstrating some project activity. We saw a total of 120 open source contributors collaborating on the project. See the full package health analysis to learn more about the package maintenance status.

Is autogluon.tabular safe to use?

The python package autogluon.tabular was scanned for known vulnerabilities and missing license, and no issues were found. Thus the package was deemed as safe to use. See the full health analysis review.

Last updated on 22 November-2024, at 05:16 (UTC).

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Fast and Accurate ML in 3 Lines of Code For more information about how to use this package see README

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Package

Readme

Fast and Accurate ML in 3 Lines of Code

💾 Installation

⚡ Quickstart

🔍 Resources

Hands-on Tutorials / Talks

Scientific Publications

Articles

Train/Deploy AutoGluon in the Cloud

📝 Citing AutoGluon

👋 How to get involved

🏛️ License

FAQs

What is autogluon.tabular?

Is autogluon.tabular popular?

Is autogluon.tabular well maintained?

Is autogluon.tabular safe to use?

Build a secure application checklist

Select a recommended open source package

Scan your app for vulnerabilities

Fix identified vulnerabilities

Monitor for new issues

Package Health Score

Explore Similar Packages

Direct Usage Popularity

Security and license risk for latest version

Commit Frequency

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Keep your project healthy

Check your requirements.txt

Snyk Vulnerability Scanner

Readme

Fast and Accurate ML in 3 Lines of Code

💾 Installation

⚡ Quickstart

🔍 Resources

Hands-on Tutorials / Talks

Scientific Publications

Articles

Train/Deploy AutoGluon in the Cloud

📝 Citing AutoGluon

👋 How to get involved

🏛️ License

FAQs

What is autogluon.tabular?

Is autogluon.tabular popular?

Is autogluon.tabular well maintained?

Is autogluon.tabular safe to use?

Build a secure application checklist

Select a recommended open source package

Scan your app for vulnerabilities

Source Code

Open Source

Container

Config

Fix identified vulnerabilities

Monitor for new issues