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https://github.com/awslabs/autogluon
Fast and Accurate ML in 3 Lines of Code
https://github.com/awslabs/autogluon
autogluon automated-machine-learning automl computer-vision data-science deep-learning ensemble-learning forecasting gluon hyperparameter-optimization machine-learning natural-language-processing object-detection python pytorch scikit-learn structured-data tabular-data time-series transfer-learning
Last synced: 15 days ago
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Fast and Accurate ML in 3 Lines of Code
- Host: GitHub
- URL: https://github.com/awslabs/autogluon
- Owner: autogluon
- License: apache-2.0
- Created: 2019-07-29T18:51:24.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2024-11-15T08:48:05.000Z (about 2 months ago)
- Last Synced: 2024-11-15T09:11:44.086Z (about 2 months ago)
- Topics: autogluon, automated-machine-learning, automl, computer-vision, data-science, deep-learning, ensemble-learning, forecasting, gluon, hyperparameter-optimization, machine-learning, natural-language-processing, object-detection, python, pytorch, scikit-learn, structured-data, tabular-data, time-series, transfer-learning
- Language: Python
- Homepage: https://auto.gluon.ai/
- Size: 19.9 MB
- Stars: 8,039
- Watchers: 97
- Forks: 927
- Open Issues: 356
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
- Security: SECURITY.md
- Roadmap: ROADMAP.md
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- awesome-machine-learning-resources - **[Library
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README
## Fast and Accurate ML in 3 Lines of Code
[](https://github.com/autogluon/autogluon/releases)
[](https://anaconda.org/conda-forge/autogluon)
[](https://pypi.org/project/autogluon/)
[](https://pepy.tech/project/autogluon)
[](./LICENSE)
[](https://discord.gg/wjUmjqAc2N)
[](https://twitter.com/autogluon)
[](https://github.com/autogluon/autogluon/actions/workflows/continuous_integration.yml)
[](https://github.com/autogluon/autogluon/actions/workflows/platform_tests-command.yml)
[Installation](https://auto.gluon.ai/stable/install.html) | [Documentation](https://auto.gluon.ai/stable/index.html) | [Release Notes](https://auto.gluon.ai/stable/whats_new/index.html)
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 3.9 - 3.12 and is available on Linux, MacOS, and Windows.
You can install AutoGluon with:
```python
pip install autogluon
```
Visit our [Installation Guide](https://auto.gluon.ai/stable/install.html) for detailed instructions, including GPU support, Conda installs, and optional dependencies.
## :zap: Quickstart
Build accurate end-to-end ML models in just 3 lines of code!
```python
from autogluon.tabular import TabularPredictor
predictor = TabularPredictor(label="class").fit("train.csv")
predictions = predictor.predict("test.csv")
```
| AutoGluon Task | Quickstart | API |
|:--------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------:|
| TabularPredictor | [](https://auto.gluon.ai/stable/tutorials/tabular/tabular-quick-start.html) | [](https://auto.gluon.ai/stable/api/autogluon.tabular.TabularPredictor.html) |
| MultiModalPredictor | [](https://auto.gluon.ai/stable/tutorials/multimodal/multimodal_prediction/multimodal-quick-start.html) | [](https://auto.gluon.ai/stable/api/autogluon.multimodal.MultiModalPredictor.html) |
| TimeSeriesPredictor | [](https://auto.gluon.ai/stable/tutorials/timeseries/forecasting-quick-start.html) | [](https://auto.gluon.ai/stable/api/autogluon.timeseries.TimeSeriesPredictor.html) |
## :mag: Resources
### Hands-on Tutorials / Talks
Below is a curated list of recent tutorials and talks on AutoGluon. A comprehensive list is available [here](AWESOME.md#videos--tutorials).
| Title | Format | Location | Date |
|--------------------------------------------------------------------------------------------------------------------------|----------|----------------------------------------------------------------------------------|------------|
| :tv: [AutoGluon: Towards No-Code Automated Machine Learning](https://www.youtube.com/watch?v=SwPq9qjaN2Q) | Tutorial | [AutoML 2024](https://2024.automl.cc/) | 2024/09/09 |
| :tv: [AutoGluon 1.0: Shattering the AutoML Ceiling with Zero Lines of Code](https://www.youtube.com/watch?v=5tvp_Ihgnuk) | Tutorial | [AutoML 2023](https://2023.automl.cc/) | 2023/09/12 |
| :sound: [AutoGluon: The Story](https://automlpodcast.com/episode/autogluon-the-story) | Podcast | [The AutoML Podcast](https://automlpodcast.com/) | 2023/09/05 |
| :tv: [AutoGluon: AutoML for Tabular, Multimodal, and Time Series Data](https://youtu.be/Lwu15m5mmbs?si=jSaFJDqkTU27C0fa) | Tutorial | PyData Berlin | 2023/06/20 |
| :tv: [Solving Complex ML Problems in a few Lines of Code with AutoGluon](https://www.youtube.com/watch?v=J1UQUCPB88I) | Tutorial | PyData Seattle | 2023/06/20 |
| :tv: [The AutoML Revolution](https://www.youtube.com/watch?v=VAAITEds-28) | Tutorial | [Fall AutoML School 2022](https://sites.google.com/view/automl-fall-school-2022) | 2022/10/18 |
### Scientific Publications
- [AutoGluon-Tabular: Robust and Accurate AutoML for Structured Data](https://arxiv.org/pdf/2003.06505.pdf) (*Arxiv*, 2020) ([BibTeX](CITING.md#general-usage--autogluontabular))
- [Fast, Accurate, and Simple Models for Tabular Data via Augmented Distillation](https://proceedings.neurips.cc/paper/2020/hash/62d75fb2e3075506e8837d8f55021ab1-Abstract.html) (*NeurIPS*, 2020) ([BibTeX](CITING.md#tabular-distillation))
- [Benchmarking Multimodal AutoML for Tabular Data with Text Fields](https://datasets-benchmarks-proceedings.neurips.cc/paper/2021/file/9bf31c7ff062936a96d3c8bd1f8f2ff3-Paper-round2.pdf) (*NeurIPS*, 2021) ([BibTeX](CITING.md#autogluonmultimodal))
- [XTab: Cross-table Pretraining for Tabular Transformers](https://proceedings.mlr.press/v202/zhu23k/zhu23k.pdf) (*ICML*, 2023)
- [AutoGluon-TimeSeries: AutoML for Probabilistic Time Series Forecasting](https://arxiv.org/abs/2308.05566) (*AutoML Conf*, 2023) ([BibTeX](CITING.md#autogluontimeseries))
- [TabRepo: A Large Scale Repository of Tabular Model Evaluations and its AutoML Applications](https://arxiv.org/pdf/2311.02971.pdf) (*Under Review*, 2024)
### Articles
- [AutoGluon-TimeSeries: Every Time Series Forecasting Model In One Library](https://towardsdatascience.com/autogluon-timeseries-every-time-series-forecasting-model-in-one-library-29a3bf6879db) (*Towards Data Science*, Jan 2024)
- [AutoGluon for tabular data: 3 lines of code to achieve top 1% in Kaggle competitions](https://aws.amazon.com/blogs/opensource/machine-learning-with-autogluon-an-open-source-automl-library/) (*AWS Open Source Blog*, Mar 2020)
- [AutoGluon overview & example applications](https://towardsdatascience.com/autogluon-deep-learning-automl-5cdb4e2388ec?source=friends_link&sk=e3d17d06880ac714e47f07f39178fdf2) (*Towards Data Science*, Dec 2019)
### Train/Deploy AutoGluon in the Cloud
- [AutoGluon Cloud](https://auto.gluon.ai/cloud/stable/index.html) (Recommended)
- [AutoGluon on SageMaker AutoPilot](https://auto.gluon.ai/stable/tutorials/cloud_fit_deploy/autopilot-autogluon.html)
- [AutoGluon on Amazon SageMaker](https://auto.gluon.ai/stable/tutorials/cloud_fit_deploy/cloud-aws-sagemaker-train-deploy.html)
- [AutoGluon Deep Learning Containers](https://github.com/aws/deep-learning-containers/blob/master/available_images.md#autogluon-training-containers) (Security certified & maintained by the AutoGluon developers)
- [AutoGluon Official Docker Container](https://hub.docker.com/r/autogluon/autogluon)
- [AutoGluon-Tabular on AWS Marketplace](https://aws.amazon.com/marketplace/pp/prodview-n4zf5pmjt7ism) (Not maintained by us)
## :pencil: Citing AutoGluon
If you use AutoGluon in a scientific publication, please refer to our [citation guide](CITING.md).
## :wave: 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](https://github.com/autogluon/autogluon/blob/master/CONTRIBUTING.md) to get started.
## :classical_building: License
This library is licensed under the Apache 2.0 License.