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@Innixma Innixma released this 17 Apr 16:46
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v1.1.0
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Version 1.1.0

We're happy to announce the AutoGluon 1.1 release.

AutoGluon 1.1 contains major improvements to the TimeSeries module, achieving a 60% win-rate vs AutoGluon 1.0 through the addition of Chronos, a pretrained model for time series forecasting, along with numerous other enhancements. The other modules have also been enhanced through new features such as Conv-LORA support and improved performance for large tabular datasets between 5 - 30 GB in size. For a full breakdown of AutoGluon 1.1 features, please refer to the feature spotlights and the itemized enhancements below.

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This release supports Python versions 3.8, 3.9, 3.10, and 3.11. Loading models trained on older versions of AutoGluon is not supported. Please re-train models using AutoGluon 1.1.

This release contains 125 commits from 20 contributors!

Full Contributor List (ordered by # of commits):

@shchur @prateekdesai04 @Innixma @canerturkmen @zhiqiangdon @tonyhoo @AnirudhDagar @Harry-zzh @suzhoum @FANGAreNotGnu @nimasteryang @lostella @dassaswat @afmkt @npepin-hub @mglowacki100 @ddelange @LennartPurucker @taoyang1122 @gradientsky

Special thanks to @ddelange for their continued assistance with Python 3.11 support and Ray version upgrades!

Spotlight

AutoGluon Achieves Top Placements in ML Competitions!

AutoGluon has experienced wide-spread adoption on Kaggle since the AutoGluon 1.0 release.
AutoGluon has been used in over 130 Kaggle notebooks and mentioned in over 100 discussion threads in the past 90 days!
Most excitingly, AutoGluon has already been used to achieve top ranking placements in multiple competitions with thousands of competitors since the start of 2024:

Placement Competition Author Date AutoGluon Details Notes
🥉 Rank 3/2303 (Top 0.1%) Steel Plate Defect Prediction Samvel Kocharyan 2024/03/31 v1.0, Tabular Kaggle Playground Series S4E3
🥈 Rank 2/93 (Top 2%) Prediction Interval Competition I: Birth Weight Oleksandr Shchur 2024/03/21 v1.0, Tabular
🥈 Rank 2/1542 (Top 0.1%) WiDS Datathon 2024 Challenge #1 lazy_panda 2024/03/01 v1.0, Tabular
🥈 Rank 2/3746 (Top 0.1%) Multi-Class Prediction of Obesity Risk Kirderf 2024/02/29 v1.0, Tabular Kaggle Playground Series S4E2
🥈 Rank 2/3777 (Top 0.1%) Binary Classification with a Bank Churn Dataset lukaszl 2024/01/31 v1.0, Tabular Kaggle Playground Series S4E1
Rank 4/1718 (Top 0.2%) Multi-Class Prediction of Cirrhosis Outcomes Kirderf 2024/01/01 v1.0, Tabular Kaggle Playground Series S3E26

We are thrilled that the data science community is leveraging AutoGluon as their go-to method to quickly and effectively achieve top-ranking ML solutions! For an up-to-date list of competition solutions using AutoGluon refer to our AWESOME.md, and don't hesitate to let us know if you use AutoGluon in a competition!

Chronos, a pretrained model for time series forecasting

AutoGluon-TimeSeries now features Chronos, a family of forecasting models pretrained on large collections of open-source time series datasets that can generate accurate zero-shot predictions for new unseen data. Check out the new tutorial to learn how to use Chronos through the familiar TimeSeriesPredictor API.

General

TimeSeries

Highlights

AutoGluon 1.1 comes with numerous new features and improvements to the time series module. These include highly requested functionality such as feature importance, support for categorical covariates, ability to visualize forecasts, and enhancements to logging. The new release also comes with considerable improvements to forecast accuracy, achieving 60% win rate and 3% average error reduction compared to the previous AutoGluon version. These improvements are mostly attributed to the addition of Chronos, improved preprocessing logic, and native handling of missing values.

New Features

Fixes and Improvements

AutoMM

Highlights

AutoMM 1.1 introduces the innovative Conv-LoRA, a parameter-efficient fine-tuning (PEFT) method stemming from our latest paper presented at ICLR 2024, titled "Convolution Meets LoRA: Parameter Efficient Finetuning for Segment Anything Model". Conv-LoRA is designed for fine-tuning the Segment Anything Model, exhibiting superior performance compared to previous PEFT approaches, such as LoRA and visual prompt tuning, across various semantic segmentation tasks in diverse domains including natural images, agriculture, remote sensing, and healthcare. Check out our Conv-LoRA example.

New Features

Fixes and Improvements

Tabular

Highlights

AutoGluon-Tabular 1.1 primarily focuses on bug fixes and stability improvements. In particular, we have greatly improved the runtime performance for large datasets between 5 - 30 GB in size through the usage of subsampling for decision threshold calibration and the weighted ensemble fitting to 1 million rows, maintaining the same quality while being far faster to execute. We also adjusted the default weighted ensemble iterations from 100 to 25, which will speedup all weighted ensemble fit times by 4x. We heavily refactored the fit_pseudolabel logic, and it should now achieve noticeably stronger results.

Fixes and Improvements

Docs and CI

New Contributors

Contributors

lostella, canerturkmen, and 19 other contributors
Assets 2