View autogluon.timeseries on Snyk Open Source Advisor

Package Health Score

90 / 100

security
No known security issues
popularity
Popular
maintenance
Healthy
community
Active

Popularity

Popular

GitHub Stars: 8.13K
Forks: 934
Contributors: 120

Direct Usage Popularity

Based on project statistics from the GitHub repository for the PyPI package autogluon.timeseries, we found that it has been starred 8,133 times.

Security

No known security issues

Security and license risk for latest version

Release Date: Nov 27, 2024
Direct Vulnerabilities: 0
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0
H
0
M
0
L
Indirect Vulnerabilities: 0
C
0
H
0
M
0
L
License Risk: 0
H
0
M
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

Healthy

Commit Frequency

Open Issues: 335
Open PR: 19
Last Release: 23 days ago
Last Commit: 15 days ago

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

We found that autogluon.timeseries demonstrates a positive version release cadence with at least one new version released in the past 3 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.timeseries project, which invites more than one hundred open source maintainers to collaborate on the repository.

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Package

Python Versions Compatibility: <3.13,>=3.9

Age: 2 years
Latest Release: 12 days ago
Dependencies: 1 Direct
Versions: 867
Maintainers: 0
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.

autogluon.timeseries dependencies

pytorch-lightning

FAQs

What is autogluon.timeseries?

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

Is autogluon.timeseries popular?

The python package autogluon.timeseries receives a total of 40,021 weekly downloads. As such, autogluon.timeseries popularity was classified as a popular. Visit the popularity section on Snyk Advisor to see the full health analysis.

Is autogluon.timeseries well maintained?

We found that autogluon.timeseries demonstrated a healthy version release cadence and project activity. It has a community 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.timeseries safe to use?

The python package autogluon.timeseries 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 8 December-2024, at 23:41 (UTC).

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autogluon.timeseries

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

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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

autogluon.timeseries dependencies

FAQs

What is autogluon.timeseries?

Is autogluon.timeseries popular?

Is autogluon.timeseries well maintained?

Is autogluon.timeseries safe to use?

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Package Health Score

Direct Usage Popularity

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Commit Frequency

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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

autogluon.timeseries dependencies

FAQs

What is autogluon.timeseries?

Is autogluon.timeseries popular?

Is autogluon.timeseries well maintained?

Is autogluon.timeseries 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

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