<div align="center">
<img src="https://user-images.githubusercontent.com/16392542/77208906-224aa500-6aba-11ea-96bd-e81806074030.png" width="350">
## AutoML for Image, Text, Time Series, and Tabular Data
[](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)
[Install Instructions](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.
</div>
## 💾 Installation
AutoGluon is supported on Python 3.8 - 3.11 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 1.0: Shattering the AutoML Ceiling with Zero Lines of Code](https://www.youtube.com/watch?v=5tvp_Ihgnuk) | Tutorial | [AutoML Conf 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.
Raw data
{
"_id": null,
"home_page": "https://github.com/tonyhoo/autogluon",
"name": "autogluon-tonyhu-test.multimodal",
"maintainer": "",
"docs_url": null,
"requires_python": ">=3.8, <3.12",
"maintainer_email": "",
"keywords": "",
"author": "AutoGluon Community",
"author_email": "",
"download_url": "https://files.pythonhosted.org/packages/8d/0c/79121aeda9319bf6cf61992ab7050741236c0095208f0452bf9c3e74ec56/autogluon-tonyhu-test.multimodal-1.0.5b20240302.tar.gz",
"platform": null,
"description": "\n\n<div align=\"center\">\n<img src=\"https://user-images.githubusercontent.com/16392542/77208906-224aa500-6aba-11ea-96bd-e81806074030.png\" width=\"350\">\n\n## AutoML for Image, Text, Time Series, and Tabular Data\n\n[](https://github.com/autogluon/autogluon/releases)\n[](https://anaconda.org/conda-forge/autogluon)\n[](https://pypi.org/project/autogluon/)\n[](https://pepy.tech/project/autogluon)\n[](./LICENSE)\n[](https://discord.gg/wjUmjqAc2N)\n[](https://twitter.com/autogluon)\n[](https://github.com/autogluon/autogluon/actions/workflows/continuous_integration.yml)\n[](https://github.com/autogluon/autogluon/actions/workflows/platform_tests-command.yml)\n\n[Install Instructions](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)\n\nAutoGluon 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.\n</div>\n\n## \ud83d\udcbe Installation\n\nAutoGluon is supported on Python 3.8 - 3.11 and is available on Linux, MacOS, and Windows.\n\nYou can install AutoGluon with:\n\n```python\npip install autogluon\n```\n\nVisit our [Installation Guide](https://auto.gluon.ai/stable/install.html) for detailed instructions, including GPU support, Conda installs, and optional dependencies.\n\n## :zap: Quickstart\n\nBuild accurate end-to-end ML models in just 3 lines of code!\n\n```python\nfrom autogluon.tabular import TabularPredictor\npredictor = TabularPredictor(label=\"class\").fit(\"train.csv\")\npredictions = predictor.predict(\"test.csv\")\n```\n\n| AutoGluon Task | Quickstart | API |\n|:--------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------:|\n| TabularPredictor | [](https://auto.gluon.ai/stable/tutorials/tabular/tabular-quick-start.html) | [](https://auto.gluon.ai/stable/api/autogluon.tabular.TabularPredictor.html) |\n| MultiModalPredictor | [](https://auto.gluon.ai/stable/tutorials/multimodal/multimodal_prediction/multimodal-quick-start.html) | [](https://auto.gluon.ai/stable/api/autogluon.multimodal.MultiModalPredictor.html) |\n| TimeSeriesPredictor | [](https://auto.gluon.ai/stable/tutorials/timeseries/forecasting-quick-start.html) | [](https://auto.gluon.ai/stable/api/autogluon.timeseries.TimeSeriesPredictor.html) |\n\n## :mag: Resources\n\n### Hands-on Tutorials / Talks\n\nBelow is a curated list of recent tutorials and talks on AutoGluon. A comprehensive list is available [here](AWESOME.md#videos--tutorials).\n\n| Title | Format | Location | Date |\n|--------------------------------------------------------------------------------------------------------------------------|----------|----------------------------------------------------------------------------------|------------|\n| :tv: [AutoGluon 1.0: Shattering the AutoML Ceiling with Zero Lines of Code](https://www.youtube.com/watch?v=5tvp_Ihgnuk) | Tutorial | [AutoML Conf 2023](https://2023.automl.cc/) | 2023/09/12 |\n| :sound: [AutoGluon: The Story](https://automlpodcast.com/episode/autogluon-the-story) | Podcast | [The AutoML Podcast](https://automlpodcast.com/) | 2023/09/05 |\n| :tv: [AutoGluon: AutoML for Tabular, Multimodal, and Time Series Data](https://youtu.be/Lwu15m5mmbs?si=jSaFJDqkTU27C0fa) | Tutorial | PyData Berlin | 2023/06/20 | \n| :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 | \n| :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 |\n\n### Scientific Publications\n- [AutoGluon-Tabular: Robust and Accurate AutoML for Structured Data](https://arxiv.org/pdf/2003.06505.pdf) (*Arxiv*, 2020) ([BibTeX](CITING.md#general-usage--autogluontabular))\n- [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))\n- [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))\n- [XTab: Cross-table Pretraining for Tabular Transformers](https://proceedings.mlr.press/v202/zhu23k/zhu23k.pdf) (*ICML*, 2023)\n- [AutoGluon-TimeSeries: AutoML for Probabilistic Time Series Forecasting](https://arxiv.org/abs/2308.05566) (*AutoML Conf*, 2023) ([BibTeX](CITING.md#autogluontimeseries))\n- [TabRepo: A Large Scale Repository of Tabular Model Evaluations and its AutoML Applications](https://arxiv.org/pdf/2311.02971.pdf) (*Under Review*, 2024)\n\n### Articles\n- [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)\n- [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)\n- [AutoGluon overview & example applications](https://towardsdatascience.com/autogluon-deep-learning-automl-5cdb4e2388ec?source=friends_link&sk=e3d17d06880ac714e47f07f39178fdf2) (*Towards Data Science*, Dec 2019)\n\n### Train/Deploy AutoGluon in the Cloud\n- [AutoGluon Cloud](https://auto.gluon.ai/cloud/stable/index.html) (Recommended)\n- [AutoGluon on SageMaker AutoPilot](https://auto.gluon.ai/stable/tutorials/cloud_fit_deploy/autopilot-autogluon.html)\n- [AutoGluon on Amazon SageMaker](https://auto.gluon.ai/stable/tutorials/cloud_fit_deploy/cloud-aws-sagemaker-train-deploy.html)\n- [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)\n- [AutoGluon Official Docker Container](https://hub.docker.com/r/autogluon/autogluon)\n- [AutoGluon-Tabular on AWS Marketplace](https://aws.amazon.com/marketplace/pp/prodview-n4zf5pmjt7ism) (Not maintained by us)\n\n## :pencil: Citing AutoGluon\n\nIf you use AutoGluon in a scientific publication, please refer to our [citation guide](CITING.md).\n\n## :wave: How to get involved\n\nWe 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.\n\n## :classical_building: License\n\nThis library is licensed under the Apache 2.0 License.\n",
"bugtrack_url": null,
"license": "Apache-2.0",
"summary": "AutoML for Image, Text, and Tabular Data",
"version": "1.0.5b20240302",
"project_urls": {
"Bug Reports": "https://github.com/autogluon/autogluon/issues",
"Contribute!": "https://github.com/autogluon/autogluon/blob/master/CONTRIBUTING.md",
"Documentation": "https://auto.gluon.ai",
"Homepage": "https://github.com/tonyhoo/autogluon",
"Source": "https://github.com/autogluon/autogluon/"
},
"split_keywords": [],
"urls": [
{
"comment_text": "",
"digests": {
"blake2b_256": "c24bedec6d8c8f29769364fcd77b0503ef7ca61f2a3b73180cbbe4264ac24318",
"md5": "388f1bbf5a134f7d7f802a481166da09",
"sha256": "26463a29cf804b212bc85419d0b3e7f7ac39f8abed326afa7de38b0e4cd6fbdc"
},
"downloads": -1,
"filename": "autogluon_tonyhu_test.multimodal-1.0.5b20240302-py3-none-any.whl",
"has_sig": false,
"md5_digest": "388f1bbf5a134f7d7f802a481166da09",
"packagetype": "bdist_wheel",
"python_version": "py3",
"requires_python": ">=3.8, <3.12",
"size": 414389,
"upload_time": "2024-03-02T05:01:04",
"upload_time_iso_8601": "2024-03-02T05:01:04.199303Z",
"url": "https://files.pythonhosted.org/packages/c2/4b/edec6d8c8f29769364fcd77b0503ef7ca61f2a3b73180cbbe4264ac24318/autogluon_tonyhu_test.multimodal-1.0.5b20240302-py3-none-any.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "8d0c79121aeda9319bf6cf61992ab7050741236c0095208f0452bf9c3e74ec56",
"md5": "b5d34bc0614d4dc270e5dd3cf14453ce",
"sha256": "f637cfc163c0b146b026531178feb3d8d58119ab90b060cd9e34e4cf59550edd"
},
"downloads": -1,
"filename": "autogluon-tonyhu-test.multimodal-1.0.5b20240302.tar.gz",
"has_sig": false,
"md5_digest": "b5d34bc0614d4dc270e5dd3cf14453ce",
"packagetype": "sdist",
"python_version": "source",
"requires_python": ">=3.8, <3.12",
"size": 323205,
"upload_time": "2024-03-02T05:01:06",
"upload_time_iso_8601": "2024-03-02T05:01:06.711193Z",
"url": "https://files.pythonhosted.org/packages/8d/0c/79121aeda9319bf6cf61992ab7050741236c0095208f0452bf9c3e74ec56/autogluon-tonyhu-test.multimodal-1.0.5b20240302.tar.gz",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2024-03-02 05:01:06",
"github": true,
"gitlab": false,
"bitbucket": false,
"codeberg": false,
"github_user": "tonyhoo",
"github_project": "autogluon",
"travis_ci": false,
"coveralls": false,
"github_actions": true,
"lcname": "autogluon-tonyhu-test.multimodal"
}
JSON
