What’s New

Here you can find the release notes for current and past releases of AutoGluon.

v1.3.1

Version 1.3.1

We are happy to announce the AutoGluon 1.3.1 release!

AutoGluon 1.3.1 contains several bug fixes and logging improvements for Tabular, TimeSeries, and Multimodal modules.

This release contains 9 commits from 5 contributors! See the full commit change-log here: https://github.com/autogluon/autogluon/compare/1.3.0…1.3.1

Join the community: Get the latest updates: Twitter

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 1.3.1.

General

Tabular

Fixes and Improvements

TimeSeries

Fixes and Improvements

  • Fix ensemble weights format for printing. @shchur #5132

  • Avoid masking the scaler param with the default target_scaler value for DirectTabular and RecursiveTabular models. @shchur #5131

  • Fix FutureWarning in leaderboard and evaluate methods. @shchur #5126

Multimodal

Fixes and Improvements

Documentation and CI

  • Add release instructions for pasting whats_new release notes. @Innixma #5111

  • Update docker image to use 1.3 release base. @tonyhoo #5130

Contributors

Full Contributor List (ordered by # of commits):

@shchur @tonyhoo @celestinoxp

New Contributors

v1.3.0

Version 1.3.0

We are happy to announce the AutoGluon 1.3.0 release!

AutoGluon 1.3 focuses on stability & usability improvements, bug fixes, and dependency upgrades.

This release contains 144 commits from 20 contributors! See the full commit change-log here: https://github.com/autogluon/autogluon/compare/v1.2.0…v1.3.0

Join the community: Get the latest updates: Twitter

Loading models trained on older versions of AutoGluon is not supported. Please re-train models using AutoGluon 1.3.

Highlights

AutoGluon-Tabular is the state of the art in the AutoML Benchmark 2025!

The AutoML Benchmark 2025, an independent large-scale evaluation of tabular AutoML frameworks, showcases AutoGluon 1.2 as the state of the art AutoML framework! Highlights include:

  • AutoGluon’s rank statistically significantly outperforms all AutoML systems via the Nemenyi post-hoc test across all time constraints.

  • AutoGluon with a 5 minute training budget outperforms all other AutoML systems with a 1 hour training budget.

  • AutoGluon is pareto efficient in quality and speed across all evaluated presets and time constraints.

  • AutoGluon with presets="high", infer_limit=0.0001 (HQIL in the figures) achieves >10,000 samples/second inference throughput while outperforming all methods.

  • AutoGluon is the most stable AutoML system. For “best” and “high” presets, AutoGluon has 0 failures on all time budgets >5 minutes.

https://raw.githubusercontent.com/Innixma/autogluon-doc-utils/refs/heads/main/docs/whats_new/v1.3.0/amlb2025_fig1.png

AutoGluon Multimodal’s “Bag of Tricks” Update

We are pleased to announce the integration of a comprehensive “Bag of Tricks” update for AutoGluon’s MultiModal (AutoMM). This significant enhancement substantially improves multimodal AutoML performance when working with combinations of image, text, and tabular data. The update implements various strategies including multimodal model fusion techniques, multimodal data augmentation, cross-modal alignment, tabular data serialization, better handling of missing modalities, and an ensemble learner that integrates these techniques for optimal performance.

Users can now access these capabilities through a simple parameter when initializing the MultiModalPredictor after following the instruction here to download the checkpoints:

from autogluon.multimodal import MultiModalPredictor
predictor = MultiModalPredictor(label="label", use_ensemble=True)
predictor.fit(train_data=train_data)

We express our gratitude to @zhiqiangdon, for this substantial contribution that enhances AutoGluon’s capabilities for handling complex multimodal datasets. Here is the corresponding research paper describing the technical details: Bag of Tricks for Multimodal AutoML with Image, Text, and Tabular Data.

Deprecations and Breaking Changes

The following deprecated TabularPredictor methods have been removed in the 1.3.0 release (deprecated in 1.0.0, raise in 1.2.0, removed in 1.3.0). Please use the new names:

  • persist_models -> persist, unpersist_models -> unpersist, get_model_names -> model_names, get_model_best -> model_best, get_pred_from_proba -> predict_from_proba, get_model_full_dict -> model_refit_map, get_oof_pred_proba -> predict_proba_oof, get_oof_pred -> predict_oof, get_size_disk_per_file -> disk_usage_per_file, get_size_disk -> disk_usage, get_model_names_persisted -> model_names(persisted=True)

The following logic has been deprecated starting in 1.3.0 and will log a FutureWarning. Functionality will be changed in a future release:

  • (FutureWarning) TabularPredictor.delete_models() will default to dry_run=False in a future release (currently dry_run=True). Please ensure you explicitly specify dry_run=True for the existing logic to remain in future releases. @Innixma (#4905)

General

Improvements

  • (Major) Internal refactor of AbstractTrainer class to improve extensibility and reduce code duplication. @canerturkmen (#4804, #4820, #4851)

Dependencies

Documentation

Fixes and Improvements

Tabular

Removed Models

  • Removed vowpalwabbit model (key: VW) and optional dependency (autogluon.tabular[vowpalwabbit]), as the model implemented in AutoGluon was not widely used and was largely unmaintained. @Innixma (#4975)

  • Removed TabTransformer model (key: TRANSF), as the model implemented in AutoGluon was heavily outdated, unmaintained since 2020, and generally outperformed by FT-Transformer (key: FT_TRANSFORMER). @Innixma (#4976)

  • Removed tabpfn from autogluon.tabular[tests] install in preparation for future tabpfn>=2.x support. @Innixma (#4974)

New Features

  • Add support for regression stratified splits via binning. @Innixma (#4586)

  • Add TabularPredictor.model_hyperparameters(model) that returns the hyperparameters of a model. @Innixma (#4901)

  • Add TabularPredictor.model_info(model) that returns the metadata of a model. @Innixma (#4901)

  • (Experimental) Add plot_leaderboard.py to visualize performance over training time of the predictor. @Innixma (#4907)

  • (Major) Add internal ag_model_registry to improve the tracking of supported model families and their capabilities. @Innixma (#4913, #5057, #5107)

  • Add raise_on_model_failure TabularPredictor.fit argument, default to False. If True, will immediately raise the original exception if a model raises an exception during fit instead of continuing to the next model. Setting to True is very helpful when using a debugger to try to figure out why a model is failing, as otherwise exceptions are handled by AutoGluon which isn’t desired while debugging. @Innixma (#4937, #5055)

Documentation

Fixes and Improvements

  • (Major) Ensure bagged refits in refit_full works properly (crashed in v1.2.0 due to a bug). @Innixma (#4870)

  • Improve XGBoost and CatBoost memory estimates. @Innixma (#5090)

  • Improve LightGBM memory estimates. @Innixma (#5101)

  • Fixed plot_tabular_models save path. @everdark (#4711)

  • Fixed balanced_accuracy metric edge-case exception + added unit tests to ensure future bugs don’t occur. @Innixma (#4775)

  • Fix HPO logging verbosity. @Innixma (#4781)

  • Improve logging for use_child_oof=True. @Innixma (#4780)

  • Fix crash when NN_TORCH trains with fewer than 8 samples. @Innixma (#4790)

  • Improve logging and documentation in CatBoost memory_check callback. @celestinoxp (#4802)

  • Improve code formatting to satisfy PEP585. @celestinoxp (#4823)

  • Remove deprecated TabularPredictor methods: @Innixma (#4906)

  • (FutureWarning) TabularPredictor.delete_models() will default to dry_run=False in a future release (currently dry_run=True). Please ensure you explicitly specify dry_run=True for the existing logic to remain in future releases. @Innixma (#4905)

  • Sped up tabular unit tests by 4x through various optimizations (3060s -> 743s). @Innixma (#4944)

  • Major tabular unit test refactor to avoid using fixtures. @Innixma (#4949)

  • Fix XGBoost GPU warnings. @Innixma (#4866)

  • Fix TabularPredictor.refit_full(train_data_extra) failing when categorical features exist. @Innixma (#4948)

  • Reduced memory usage of artifact created by convert_simulation_artifacts_to_tabular_predictions_dict by 4x. @Innixma (#5024)

  • Minor fixes. @shchur (#5030)

  • Ensure that max model resources is respected during holdout model fit. @Innixma (#5067)

  • Remove unintended setting of global random seed during LightGBM model fit. @Innixma (#5095)

TimeSeries

The new v1.3 release brings numerous usability improvements and bug fixes to the TimeSeries module. Internally, we completed a major refactor of the core classes and introduced static type checking to simplify future contributions, accelerate development, and catch potential bugs earlier.

API Changes and Deprecations

  • As part of the refactor, we made several changes to the internal AbstractTimeSeriesModel class. If you maintain a custom model implementation, you will likely need to update it. Please refer to the custom forecasting model tutorial for details.

    No action is needed from the users that rely solely on the public API of the timeseries module (TimeSeriesPredictor and TimeSeriesDataFrame).

New Features

  • New tutorial on adding custom forecasting models by @shchur in #4749

  • Add cutoff support in evaluate and leaderboard by @abdulfatir in #5078

  • Add horizon_weight support for TimeSeriesPredictor by @shchur in #5084

  • Add make_future_data_frame method to TimeSeriesPredictor by @shchur in #5051

  • Refactor ensemble base class and add new ensembles by @canerturkmen in #5062

Code Quality

Fixes and Improvements

Multimodal

New Features

AutoGluon’s MultiModal module has been enhanced with a comprehensive “Bag of Tricks” update that significantly improves performance when working with combined image, text, and tabular data through advanced fusion techniques, data augmentation, and an integrated ensemble learner now accessible via a simple use_ensemble=True parameter after following the instruction here to download the checkpoints.

Documentation

Fixes and Improvements

  • Update s3 path to public URL for AutoMM unit tests by @suzhoum in #4809

  • Fix object detection tutorial and default behavior of predict by @FANGAreNotGnu in #4865

  • Fix NLTK tagger path in download function by @k-ken-t4g in #4982

  • Fix AutoMM model saving logic by capping transformer range by @tonyhoo in #5007

  • fix: account for distributed training in learning rate schedule by @tonyhoo in #5003

Special Thanks

  • Zhiqiang Tang for implementing “Bag of Tricks” for AutoGluon’s MultiModal, which significantly enhances the multimodal performance.

  • Caner Turkmen for leading the efforts on refactoring and improving the internal logic in the timeseries module.

  • Celestino for providing numerous bug reports, suggestions, and code cleanup as a new contributor.

Contributors

Full Contributor List (ordered by # of commits):

@Innixma @shchur @canerturkmen @tonyhoo @abdulfatir @celestinoxp @suzhoum @FANGAreNotGnu @prateekdesai04 @zhiqiangdon @cheungdaven @LennartPurucker @abhishek-iitmadras @zkalson @nathanaelbosch @Killer3048 @FireballDWF @timostrunk @everdark @kbulygin @PGijsbers @k-ken-t4g

New Contributors

v1.2.0

Version 1.2.0

We’re happy to announce the AutoGluon 1.2.0 release.

AutoGluon 1.2 contains massive improvements to both Tabular and TimeSeries modules, each achieving a 70% win-rate vs AutoGluon 1.1. This release additionally adds support for Python 3.12 and drops support for Python 3.8.

This release contains 186 commits from 19 contributors! See the full commit change-log here: https://github.com/autogluon/autogluon/compare/v1.1.1…v1.2.0

Join the community:
Get the latest updates: Twitter

Loading models trained on older versions of AutoGluon is not supported. Please re-train models using AutoGluon 1.2.

For Tabular, we encompass the primary enhancements of the new TabPFNMix tabular foundation model and parallel fit strategy into the new "experimental_quality" preset to ensure a smooth transition period for those who wish to try the new cutting edge features. We will be using this release to gather feedback prior to incorporating these features into the other presets. We also introduce a new stack layer model pruning technique that results in a 3x inference speedup on small datasets with zero performance loss and greatly improved post-hoc calibration across the board, particularly on small datasets.

For TimeSeries, we introduce Chronos-Bolt, our latest foundation model integrated into AutoGluon, with massive improvements to both accuracy and inference speed compared to Chronos, along with fine-tuning capabilities. We also added covariate regressor support!

We are also excited to announce AutoGluon-Assistant (AG-A), our first venture into the realm of Automated Data Science.

See more details in the Spotlights below!

Spotlight

AutoGluon Becomes the Golden Standard for Competition ML in 2024

Before diving into the new features of 1.2, we would like to start by highlighting the wide-spread adoption AutoGluon has received on competition ML sites like Kaggle in 2024. Across all of 2024, AutoGluon was used to achieve a top 3 finish in 15 out of 18 tabular Kaggle competitions, including 7 first place finishes, and was never outside the top 1% of private leaderboard placements, with an average of over 1000 competing human teams in each competition. In the $75,000 prize money 2024 Kaggle AutoML Grand Prix, AutoGluon was used by the 1st, 2nd, and 3rd place teams, with the 2nd place team led by two AutoGluon developers: Lennart Purucker and Nick Erickson! For comparison, in 2023 AutoGluon achieved only 1 first place and 1 second place solution. We attribute the bulk of this increase to the improvements seen in AutoGluon 1.0 and beyond.

We’d like to emphasize that these results are achieved via human expert interaction with AutoGluon and other tools, and often includes manual feature engineering and hyperparameter tuning to get the most out of AutoGluon. To see a live tracking of all AutoGluon solution placements on Kaggle, refer to our AWESOME.md ML competition section where we provide links to all solution write-ups.

AutoGluon-Assistant: Automating Data Science with AutoGluon and LLMs

We are excited to share the release of a new AutoGluon-Assistant module (AG-A), powered by LLMs from AWS Bedrock or OpenAI. AutoGluon-Assistant empowers users to solve tabular machine learning problems using only natural language descriptions, in zero lines of code with our simple user interface. Fully autonomous AG-A outperforms 74% of human ML practitioners in Kaggle competitions and secured a live top 10 finish in the $75,000 prize money 2024 Kaggle AutoML Grand Prix competition as Team AGA 🤖!

TabularPredictor presets=”experimental_quality”

TabularPredictor has a new "experimental_quality" preset that offers even better predictive quality than "best_quality". On the AutoMLBenchmark, we observe a 70% winrate vs best_quality when running for 4 hours on a 64 CPU machine. This preset is a testing ground for cutting edge features and models which we hope to incorporate into best_quality for future releases. We recommend to use a machine with at least 16 CPU cores, 64 GB of memory, and a 4 hour+ time_limit to get the most benefit out of experimental_quality. Please let us know via a GitHub issue if you run into any problems running the experimental_quality preset.

TabPFNMix: A Foundation Model for Tabular Data

TabPFNMix is the first tabular foundation model created by the AutoGluon team, and was pre-trained exclusively on synthetic data. The model builds upon the prior work of TabPFN and TabForestPFN. TabPFNMix to the best of our knowledge achieves a new state-of-the-art for individual open source model performance on datasets between 1000 and 10000 samples, and also supports regression tasks! Across the 109 classification datasets with less than or equal to 10000 training samples in TabRepo, fine-tuned TabPFNMix outperforms all prior models, with a 64% win-rate vs the strongest tree model, CatBoost, and a 61% win-rate vs fine-tuned TabForestPFN.

The model is available via the TABPFNMIX hyperparameters key, and is used in the new experimental_quality preset. We recommend using this model for datasets smaller than 50,000 training samples, ideally with a large time limit and 64+ GB of memory. This work is still in the early stages, and we appreciate any feedback from the community to help us iterate and improve for future releases. You can learn more by going to our HuggingFace model page for the model (tabpfn-mix-1.0-classifier, tabpfn-mix-1.0-regressor). Give us a like on HuggingFace if you want to see more! A paper is planned in future to provide more details about the model.

fit_strategy=”parallel”

AutoGluon’s TabularPredictor now supports the new fit argument fit_strategy and the new "parallel" option, enabled by default in the new experimental_quality preset. For machines with 16 or more CPU cores, the parallel fit strategy offers a major speedup over the previous "sequential" strategy. We estimate with 64 CPU cores that most datasets will experience a 2-4x speedup, with the speedup getting larger as CPU cores increase.

Chronos-Bolt⚡: a 250x faster, more accurate Chronos model

Chronos-Bolt is our latest foundation model for forecasting that has been integrated into AutoGluon. It is based on the T5 encoder-decoder architecture and has been trained on nearly 100 billion time series observations. It chunks the historical time series context into patches of multiple observations, which are then input into the encoder. The decoder then uses these representations to directly generate quantile forecasts across multiple future steps—a method known as direct multi-step forecasting. Chronos-Bolt models are up to 250 times faster and 20 times more memory-efficient than the original Chronos models of the same size.

The following plot compares the inference time of Chronos-Bolt against the original Chronos models for forecasting 1024 time series with a context length of 512 observations and a prediction horizon of 64 steps.

Chronos-Bolt models are not only significantly faster but also more accurate than the original Chronos models. The following plot reports the probabilistic and point forecasting performance of Chronos-Bolt in terms of the Weighted Quantile Loss (WQL) and the Mean Absolute Scaled Error (MASE), respectively, aggregated over 27 datasets (see the Chronos paper for details on this benchmark). Remarkably, despite having no prior exposure to these datasets during training, the zero-shot Chronos-Bolt models outperform commonly used statistical models and deep learning models that have been trained on these datasets (highlighted by *). Furthermore, they also perform better than other FMs, denoted by a +, which indicates that these models were pretrained on certain datasets in our benchmark and are not entirely zero-shot. Notably, Chronos-Bolt (Base) also surpasses the original Chronos (Large) model in terms of the forecasting accuracy while being over 600 times faster.

Chronos-Bolt models are now available through AutoGluon in four sizes—Tiny (9M), Mini (21M), Small (48M), and Base (205M)—and can also be used on the CPU. With the addition of Chronos-Bolt models and other enhancements, AutoGluon v1.2 achieves a 70%+ win rate against the previous release!

In addition to the new Chronos-Bolt models, we have also added support for effortless fine-tuning of Chronos and Chronos-Bolt models. Check out the updated Chronos tutorial to learn how to use and fine-tune Chronos-Bolt models.

Time Series Covariate Regressors

We have added support for covariate regressors for all forecasting models. Covariate regressors are tabular regression models that can be combined with univariate forecasting models to incorporate exogenous information. These are particularly useful for foundation models like Chronos-Bolt, which rely solely on the target time series’ historical data and cannot directly use exogenous information (such as holidays or promotions). To improve the predictions of univariate models when covariates are available, a covariate regressor is first fit on the known covariates and static features to predict the target column at each time step. The predictions of the covariate regressor are then subtracted from the target column, and the univariate model then forecasts the residuals. The Chronos tutorial showcases how covariate regressors can be used with Chronos-Bolt.

General

Improvements

  • Update full_install.sh to install AutoGluon in parallel and to use uv, resulting in much faster source installation times. @Innixma (#4582, #4587, #4592)

Dependencies

Documentation

  • Update install instructions to use proper torch and ray versions. @Innixma (#4581)

  • Add +cpu tag for cpu installation guide. @tonyhoo (#4554)

  • Add SECURITY.md for vulnerability reporting. @tonyhoo (#4298)

Fixes and Improvements

  • Speed up DropDuplicatesFeatureGenerator fit time by 2x+. @shchur (#4543)

  • Add compute_metric as a replacement for compute_weighted_metric with improved compatibility across the project. @Innixma (#4631)

  • Enhanced generate_train_test_split. @Innixma (#4478)

Tabular

New Features

  • Add TabPFNMix model. Try it out with presets="experimental". @xiyuanzh @Innixma (#4671, #4694)

  • Parallel model fit support. Try it out with fit_strategy="parallel". @LennartPurucker @Innixma (#4606)

  • Predictor callbacks support. @Innixma (#4327, #4473)

  • Learning curve generation feature. @adibiasio @Innixma (#4411, #4635)

  • Set calibrate_decision_threshold="auto" by default, and improve decision threshold calibration. This dramatically improves results when the eval_metric is f1 and balanced_accuracy for binary classification. @Innixma (#4632)

  • Add roc_auc_ovo and roc_auc_ovr metrics. @Innixma (#4248)

  • Add support for custom memory (soft) limits. @LennartPurucker (#4333)

  • Add ag.compile hyperparameter to models to enable compiling at fit time rather than with predictor.compile. @Innixma (#4354)

  • Add AdaptiveES support to NN_TORCH and increase max_epochs from 500 to 1000, enabled by default. @Innixma (#4436)

  • Add support for controlling repeated cross-validation behavior via delay_bag_sets fit argument. Set default to False (previously True). @LennartPurucker (#4552)

  • Make positive_class an init argument of TabularPredictor. @Innixma (#4445)

  • Add AdamW support to NN_TORCH model. @Innixma (#4610)

Documentation

Fixes and Improvements

TimeSeries

New Features

Fixes and Improvements

Multimodal

Fixes and Improvements

  • Fix Missing Validation Metric While Resuming A Model Failed At Checkpoint Fusing Stage by @FANGAreNotGnu in https://github.com/autogluon/autogluon/pull/4449

  • Add coco_root for better support for custom dataset in COCO format. by @FANGAreNotGnu in https://github.com/autogluon/autogluon/pull/3809

  • Add COCO Format Saving Support and Update Object Detection I/O Handling by @FANGAreNotGnu in https://github.com/autogluon/autogluon/pull/3811

  • Skip MMDet Config Files While Checking with bandit by @FANGAreNotGnu in https://github.com/autogluon/autogluon/pull/4630

  • Fix Logloss Bug and Refine Compute Score Logics by @FANGAreNotGnu in https://github.com/autogluon/autogluon/pull/4629

  • Fix Index Typo in Tutorial by @FANGAreNotGnu in https://github.com/autogluon/autogluon/pull/4642

  • Fix Proba Metrics for Multiclass by @FANGAreNotGnu in https://github.com/autogluon/autogluon/pull/4643

  • Support torch 2.4 by @tonyhoo in https://github.com/autogluon/autogluon/pull/4360

  • Add Installation Guide for Object Detection in Tutorial by @FANGAreNotGnu in https://github.com/autogluon/autogluon/pull/4430

  • Add Bandit Warning Mitigation for Internal torch.save and torch.load Usage by @tonyhoo in https://github.com/autogluon/autogluon/pull/4502

  • update accelerate version range by @cheungdaven in https://github.com/autogluon/autogluon/pull/4596

  • Bound nltk version to avoid verbose logging issue by @tonyhoo in https://github.com/autogluon/autogluon/pull/4604

  • Upgrade TIMM by @prateekdesai04 in https://github.com/autogluon/autogluon/pull/4580

  • Key dependency updates in _setup_utils.py for v1.2 release by @tonyhoo in https://github.com/autogluon/autogluon/pull/4612

  • Configurable Number of Checkpoints to Keep per HPO Trial by @FANGAreNotGnu in https://github.com/autogluon/autogluon/pull/4615

  • Refactor Metrics for Each Problem Type by @FANGAreNotGnu in https://github.com/autogluon/autogluon/pull/4616

  • Fix Torch Version and Colab Installation for Object Detection by @FANGAreNotGnu in https://github.com/autogluon/autogluon/pull/4447

Special Thanks

Contributors

Full Contributor List (ordered by # of commits):

@Innixma @shchur @prateekdesai04 @tonyhoo @FangAreNotGnu @suzhoum @abdulfatir @canerturkmen @LennartPurucker @abhishek-iitmadras @adibiasio @rsj123 @nathanaelbosch @cheungdaven @lostella @zkalson @rey-allan @echowve @xiyuanzh

New Contributors

  • @nathanaelbosch made their first contribution in https://github.com/autogluon/autogluon/pull/4366

  • @adibiasio made their first contribution in https://github.com/autogluon/autogluon/pull/4391

  • @abdulfatir made their first contribution in https://github.com/autogluon/autogluon/pull/4608

  • @echowve made their first contribution in https://github.com/autogluon/autogluon/pull/4667

  • @abhishek-iitmadras made their first contribution in https://github.com/autogluon/autogluon/pull/4685

  • @xiyuanzh made their first contribution in https://github.com/autogluon/autogluon/pull/4694

v1.1.1

Version 1.1.1

We’re happy to announce the AutoGluon 1.1.1 release.

AutoGluon 1.1.1 contains bug fixes and logging improvements for Tabular, TimeSeries, and Multimodal modules, as well as support for PyTorch 2.2 and 2.3.

Join the community:
Get the latest updates: Twitter

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 1.1.1.

This release contains 52 commits from 10 contributors!

General

Tabular

Note: Trying to load a TabularPredictor with a FastAI model trained on a previous AutoGluon release will raise an exception when calling predict due to a fix in the model-interals.pkl path. Please ensure matching versions.

  • Fix deadlock when num_gpus>0 and dynamic_stacking is enabled. @Innixma (#4208)

  • Improve decision threshold calibration. @Innixma (#4136, #4137)

  • Improve dynamic stacking logging. @Innixma (#4208, #4262)

  • Fix regression metrics (other than RMSE and MSE) being calculated incorrectly for LightGBM early stopping. @Innixma (#4174)

  • Fix custom multiclass metrics being calculated incorrectly for LightGBM early stopping. @Innixma (#4250)

  • Fix HPO crashing with NN_TORCH and FASTAI models. @Innixma (#4232)

  • Improve NN_TORCH runtime estimate. @Innixma (#4247)

  • Add infer throughput logging. @Innixma (#4200)

  • Disable sklearnex for linear models due to observed performance degradation. @Innixma (#4223)

  • Improve sklearnex logging verbosity in Kaggle. @Innixma (#4216)

  • Rename cached version file to version.txt. @Innixma (#4203)

  • Add refit_full support for Linear models. @Innixma (#4222)

  • Add AsTypeFeatureGenerator detailed exception logging. @Innixma (#4251, #4252)

TimeSeries

  • Ensure prediction_length is stored as an integer. @shchur (#4160)

  • Fix tabular model preprocessing failure edge-case. @shchur (#4175)

  • Fix loading of Tabular models failure if predictor moved to a different directory. @shchur (#4171)

  • Fix cached predictions error when predictor saved on-top of an existing predictor. @shchur (#4202)

  • Use AutoGluon forks of Chronos models. @shchur (#4198)

  • Fix off-by-one bug in Chronos inference. @canerturkmen (#4205)

  • Rename cached version file to version.txt. @Innixma (#4203)

  • Use correct target and quantile_levels in fallback model for MLForecast. @shchur (#4230)

Multimodal

Docs and CI

Contributors

Full Contributor List (ordered by # of commits):

@Innixma @shchur @Harry-zzh @suzhoum @zhiqiangdon @lovvge @rey-allan @prateekdesai04 @canerturkmen @FANGAreNotGnu

New Contributors

  • @lovvge made their first contribution in https://github.com/autogluon/autogluon/commit/57a15fcfbbbc94514ff20ed2774cd447d9f4115f

  • @rey-allan made their first contribution in #4145

v1.1.0

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 121 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

:3rd_place_medal: Rank 3/2303 (Top 0.1%)

Steel Plate Defect Prediction

Samvel Kocharyan

2024/03/31

v1.0, Tabular

Kaggle Playground Series S4E3

:2nd_place_medal: Rank 2/93 (Top 2%)

Prediction Interval Competition I: Birth Weight

Oleksandr Shchur

2024/03/21

v1.0, Tabular

:2nd_place_medal: Rank 2/1542 (Top 0.1%)

WiDS Datathon 2024 Challenge #1

lazy_panda

2024/03/01

v1.0, Tabular

:2nd_place_medal: Rank 2/3746 (Top 0.1%)

Multi-Class Prediction of Obesity Risk

Kirderf

2024/02/29

v1.0, Tabular

Kaggle Playground Series S4E2

:2nd_place_medal: 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 used 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

v1.0.0

Version 1.0.0

Today is finally the day… AutoGluon 1.0 has arrived!! After over four years of development and 2061 commits from 111 contributors, we are excited to share with you the culmination of our efforts to create and democratize the most powerful, easy to use, and feature rich automated machine learning system in the world. AutoGluon 1.0 comes with transformative enhancements to predictive quality resulting from the combination of multiple novel ensembling innovations, spotlighted below. Besides performance enhancements, many other improvements have been made that are detailed in the individual module sections.

Note: Loading models trained on older versions of AutoGluon is not supported. Please re-train models using AutoGluon 1.0.

This release supports Python versions 3.8, 3.9, 3.10, and 3.11.

This release contains 223 commits from 17 contributors!

Full Contributor List (ordered by # of commits):

@shchur, @zhiqiangdon, @Innixma, @prateekdesai04, @FANGAreNotGnu, @yinweisu, @taoyang1122, @LennartPurucker, @Harry-zzh, @AnirudhDagar, @jaheba, @gradientsky, @melopeo, @ddelange, @tonyhoo, @canerturkmen, @suzhoum

Join the community:
Get the latest updates: Twitter

Spotlight

Tabular Performance Enhancements

AutoGluon 1.0 features major enhancements to predictive quality, establishing a new state-of-the-art in Tabular modeling. To the best of our knowledge, AutoGluon 1.0 marks the largest leap forward in the state-of-the-art for tabular data since the original AutoGluon paper from March 2020. The enhancements come primarily from two features: Dynamic stacking to mitigate stacked overfitting, and a new learned model hyperparameters portfolio via Zeroshot-HPO, obtained from the newly released TabRepo ensemble simulation library. Together, they lead to a 75% win-rate compared to AutoGluon 0.8 with faster inference speed, lower disk usage, and higher stability.

AutoML Benchmark Results

OpenML released the official 2023 AutoML Benchmark results on November 16th, 2023. Their results show AutoGluon 0.8 as the state-of-the-art in AutoML systems across a wide variety of tasks: “Overall, in terms of model performance, AutoGluon consistently has the highest average rank in our benchmark.” We now showcase that AutoGluon 1.0 achieves far superior results even to AutoGluon 0.8!

Below is a comparison on the OpenML AutoML Benchmark across 1040 tasks. LightGBM, XGBoost, and CatBoost results were obtained via AutoGluon, and other methods are from the official AutoML Benchmark 2023 results. AutoGluon 1.0 has a 95%+ win-rate against traditional tabular models, including a 99% win-rate vs LightGBM and a 100% win-rate vs XGBoost. AutoGluon 1.0 has between an 82% and 94% win-rate against other AutoML systems. For all methods, AutoGluon is able to achieve >10% average loss improvement (Ex: Going from 90% accuracy to 91% accuracy is a 10% loss improvement). AutoGluon 1.0 achieves first place in 63% of tasks, with lightautoml having the second most at 12% (AutoGluon 0.8 previously took first place 48% of the time). AutoGluon 1.0 even achieves a 7.4% average loss improvement over AutoGluon 0.8!

Method

AG Winrate

AG Loss Improvement

Rescaled Loss

Rank

Champion

AutoGluon 1.0 (Best, 4h8c)

-

-

0.04

1.95

63%

lightautoml (2023, 4h8c)

84%

12.0%

0.2

4.78

12%

H2OAutoML (2023, 4h8c)

94%

10.8%

0.17

4.98

1%

FLAML (2023, 4h8c)

86%

16.7%

0.23

5.29

5%

MLJAR (2023, 4h8c)

82%

23.0%

0.33

5.53

6%

autosklearn (2023, 4h8c)

91%

12.5%

0.22

6.07

4%

GAMA (2023, 4h8c)

86%

15.4%

0.28

6.13

5%

CatBoost (2023, 4h8c)

95%

18.2%

0.28

6.89

3%

TPOT (2023, 4h8c)

91%

23.1%

0.4

8.15

1%

LightGBM (2023, 4h8c)

99%

23.6%

0.4

8.95

0%

XGBoost (2023, 4h8c)

100%

24.1%

0.43

9.5

0%

RandomForest (2023, 4h8c)

97%

25.1%

0.53

9.78

1%

Not only is AutoGluon more accurate in 1.0, it is also more stable thanks to our new usage of Ray subprocesses during low-memory training, resulting in 0 task failures on the AutoML Benchmark.

AutoGluon 1.0 is capable of achieving the fastest inference throughput of any AutoML system while still obtaining state-of-the-art results. By specifying the infer_limit fit argument, users can trade off between accuracy and inference speed to meet their needs.

As seen in the below plot, AutoGluon 1.0 sets the Pareto Frontier for quality and inference throughput, achieving Pareto Dominance compared to all other AutoML systems. AutoGluon 1.0 High achieves superior performance to AutoGluon 0.8 Best with 8x faster inference and 8x less disk usage!

AutoGluon 1.0 AutoML Benchmark Plot

You can get more details on the results here.

We are excited to see what our users can accomplish with AutoGluon 1.0’s enhanced performance. As always, we will continue to improve AutoGluon in future releases to push the boundaries of AutoML forward for all.

AutoGluon Multimodal (AutoMM) Highlights in One Figure

AutoMM highlights

AutoMM Uniqueness

AutoGluon Multimodal (AutoMM) distinguishes itself from other open-source AutoML toolboxes like AutosSklearn, LightAutoML, H2OAutoML, FLAML, MLJAR, TPOT and GAMA, which mainly focus on tabular data for classification or regression. AutoMM is designed for fine-tuning foundation models across multiple modalities—image, text, tabular, and document, either individually or combined. It offers extensive capabilities for tasks like classification, regression, object detection, named entity recognition, semantic matching, and image segmentation. In contrast, other AutoML systems generally have limited support for image or text, typically using a few pretrained models like EfficientNet or hand-crafted rules like bag-of-words as feature extractors. They often rely on traditional models or simple neural networks. AutoMM provides a uniquely comprehensive and versatile approach to AutoML, being the only AutoML system to support flexible multimodality and support for a wide range of tasks. A comparative table detailing support for various data modalities, tasks, and model types is provided below.

Data

Task

Model

image

text

tabular

document

any combination

classification

regression

object detection

semantic matching

named entity recognition

image segmentation

traditional models

deep learning models

foundation models

LightAutoML

✓

✓

✓

✓

✓

✓

✓

H2OAutoML

✓

✓

✓

✓

FLAML

✓

✓

✓

✓

✓

✓

✓

MLJAR

✓

✓

✓

✓

AutoSklearn

✓

✓

✓

✓

✓

GAMA

✓

✓

✓

✓

TPOT

✓

✓

✓

✓

✓

✓

AutoMM

✓

✓

✓

✓

✓

✓

✓

✓

✓

✓

✓

✓

✓

Special Thanks

We would like to conclude this spotlight by thanking Pieter Gijsbers, Sébastien Poirier, Erin LeDell, Joaquin Vanschoren, and the rest of the AutoML Benchmark authors for their key role in providing a shared and extensive benchmark to monitor the progress of the AutoML field. Their support has been invaluable to the AutoGluon project’s continued growth.

We would also like to thank Frank Hutter, who continues to be a leader within the AutoML field, for organizing the AutoML Conference in 2022 and 2023 to bring the community together to share ideas and align on a compelling vision.

Finally, we would like to thank Alex Smola and Mu Li for championing open source software at Amazon to make this project possible.

Additional Special Thanks

  • Special thanks to @LennartPurucker for leading development of dynamic stacking

  • Special thanks to @geoalgo for co-authoring TabRepo to enable Zeroshot-HPO

  • Special thanks to @ddelange for helping to add Python 3.11 support

  • Special thanks to @mglowacki100 for providing numerous feedback and suggestions

  • Special thanks to @Harry-zzh for contributing the new semantic segmentation problem type

General

Highlights

Other Enhancements

Dependency Updates

Tabular

Highlights

AutoGluon 1.0 features major enhancements to predictive quality, establishing a new state-of-the-art in Tabular modeling. Refer to the spotlight section above for more details!

New Features

Performance Improvements

Other Enhancements

Bug Fixes / Code and Doc Improvements

AutoMM

AutoGluon Multimodal (AutoMM) is designed to simplify the fine-tuning of foundation models for downstream applications with just three lines of code. It seamlessly integrates with popular model zoos such as HuggingFace Transformers, TIMM, and MMDetection, providing support for a diverse range of data modalities, including image, text, tabular, and document data, whether used individually or in combination.

New Features

Performance Improvements

  • Improved default image backbones, achieving a 100% win-rate on the image benchmark. @taoyang1122 (#3738)

  • Replaced MLPs with FT-Transformer as the default tabular backbones, resulting in a 67% win-rate on the text+tabular benchmark. @taoyang1122 (#3732)

  • Using both the improved default image backbones and FT-Transformer achieves a 62% win-rate on the text+tabular+image benchmark. @taoyang1122 (#3732, #3738)

Stability Enhancements

Enhanced Usability

Improved Scalability

  • The introduction of the new learner class design facilitates easier support for new tasks and data modalities within AutoMM, enhancing overall scalability. @zhiqiangdon (#3650, #3685, #3735)

Other Enhancements

Code Improvements

Bug Fixes/Doc Improvements

TimeSeries

Highlights

AutoGluon 1.0 features numerous usability and performance improvements to the TimeSeries module. These include automatic handling of missing data and irregular time series, new forecasting metrics (including custom metric support), advanced time series cross-validation options, and new forecasting models. AutoGluon produces state-of-the-art results in forecast accuracy, achieving 70%+ win rate compared to other popular forecasting frameworks.

New features

  • Support for custom forecasting metrics @shchur (#3760, #3602)

  • New forecasting metrics WAPE, RMSSE, SQL + improved documentation for metrics @melopeo @shchur (#3747, #3632, #3510, #3490)

  • Improved robustness: TimeSeriesPredictor can now handle data with all pandas frequencies, irregular timestamps, or missing values represented by NaN @shchur (#3563, #3454)

  • New models: intermittent demand forecasting models based on conformal prediction (ADIDA, CrostonClassic, CrostonOptimized, CrostonSBA, IMAPA); WaveNet and NPTS from GluonTS; new baseline models (Average, SeasonalAverage, Zero) @canerturkmen @shchur (#3706, #3742, #3606, #3459)

  • Advanced cross-validation options: avoid retraining the models for each validation window with refit_every_n_windows or adjust the step size between validation windows with val_step_size arguments to TimeSeriesPredictor.fit @shchur (#3704, #3537)

Enhancements

  • Enable Ray Tune for deep-learning forecasting models @canerturkmen (#3705)

  • Support passing multiple evaluation metrics to TimeSeriesPredictor.evaluate @shchur (#3646)

  • Static features can now be passed directly to TimeSeriesDataFrame.from_path and TimeSeriesDataFrame.from_data_frame constructors @shchur (#3635)

Performance improvements

  • Much more accurate forecasts at low time limits thanks to new presets and updated logic for splitting the training time across models @shchur (#3749, #3657, #3741)

  • Faster training and prediction + lower memory usage for DirectTabular and RecursiveTabular models (#3740, #3620, #3559)

  • Enable early stopping and improve inference speed for GluonTS models @shchur (#3575)

  • Reduce import time for autogluon.timeseries by moving import statements inside model classes (#3514)

Bug Fixes / Code and Doc Improvements

EDA

The EDA module will be released at a later time, as it requires additional development effort before it is ready for 1.0. We will make an announcement when EDA is ready for release. For now, please continue to use "autogluon.eda==0.8.2".

Deprecations

General

  • autogluon.core.spaces has been deprecated. Please use autogluon.common.spaces instead @Innixma (#3701)

Tabular

Tabular will log warnings if using the deprecated methods. Deprecated methods are planned to be removed in AutoGluon 1.2 @Innixma (#3701)

  • autogluon.tabular.TabularPredictor

    • predictor.get_model_names() -> predictor.model_names()

    • predictor.get_model_names_persisted() -> predictor.model_names(persisted=True)

    • predictor.compile_models() -> predictor.compile()

    • predictor.persist_models() -> predictor.persist()

    • predictor.unpersist_models() -> predictor.unpersist()

    • predictor.get_model_best() -> predictor.model_best

    • predictor.get_pred_from_proba() -> predictor.predict_from_proba()

    • predictor.get_oof_pred_proba() -> predictor.predict_proba_oof()

    • predictor.get_oof_pred() -> predictor.predict_oof()

    • predictor.get_model_full_dict() -> predictor.model_refit_map()

    • predictor.get_size_disk() -> predictor.disk_usage()

    • predictor.get_size_disk_per_file() -> predictor.disk_usage_per_file()

    • predictor.leaderboard() silent argument deprecated, replaced by display, defaults to False

      • Same for predictor.evaluate() and predictor.evaluate_predictions()

AutoMM

  • Deprecated the FewShotSVMPredictor in favor of the new few_shot_classification problem type @zhiqiangdon (#3699)

  • Deprecated the AutoMMPredictor in favor of MultiModalPredictor @zhiqiangdon (#3650)

  • autogluon.multimodal.MultiModalPredictor

TimeSeries

  • autogluon.timeseries.TimeSeriesPredictor

    • Deprecated argument TimeSeriesPredictor(ignore_time_index: bool). Now, if the data contains irregular timestamps, either convert it to regular frequency with data = data.convert_frequency(freq) or provide frequency when creating the predictor as TimeSeriesPredictor(freq=freq).

    • predictor.evaluate() now returns a dictionary (previously returned a float)

    • predictor.score() -> predictor.evaluate()

    • predictor.get_model_names() -> predictor.model_names()

    • predictor.get_model_best() -> predictor.model_best

    • Metric "mean_wQuantileLoss" has been renamed to "WQL"

    • predictor.leaderboard() silent argument deprecated, replaced by display, defaults to False

    • When setting hyperparameters to a string in predictor.fit(), supported values are now "default", "light" and "very_light"

  • autogluon.timeseries.TimeSeriesDataFrame

    • df.to_regular_index() -> df.convert_frequency()

    • Deprecated method df.get_reindexed_view(). Please see deprecation notes for ignore_time_index under TimeSeriesPredictor above for information on how to deal with irregular timestamps

  • Models

    • All models based on MXNet (DeepARMXNet, MQCNNMXNet, MQRNNMXNet, SimpleFeedForwardMXNet, TemporalFusionTransformerMXNet, TransformerMXNet) have been removed

    • Statistical models from Statmodels (ARIMA, Theta, ETS) have been replaced by their counterparts from StatsForecast (#3513). Note that these models now have different hyperparameter names.

    • DirectTabular is now implemented using mlforecast backend (same as RecursiveTabular), most hyperparameter names for the model have changed.

  • autogluon.timeseries.TimeSeriesEvaluator has been deprecated. Please use metrics available in autogluon.timeseries.metrics instead.

  • autogluon.timeseries.splitter.MultiWindowSplitter and autogluon.timeseries.splitter.LastWindowSplitter have been deprecated. Please use num_val_windows and val_step_size arguments to TimeSeriesPredictor.fit instead (alternatively, use autogluon.timeseries.splitter.ExpandingWindowSplitter).

Papers

AutoGluon-TimeSeries: AutoML for Probabilistic Time Series Forecasting

We have published a paper on AutoGluon-TimeSeries at AutoML Conference 2023 (Paper Link, YouTube Video). In the paper, we benchmarked AutoGluon and popular open-source forecasting frameworks (including DeepAR, TFT, AutoARIMA, AutoETS, AutoPyTorch). AutoGluon produces SOTA results in point and probabilistic forecasting, and even achieves 65% win rate against the best-in-hindsight combination of models.

TabRepo: A Large Scale Repository of Tabular Model Evaluations and its AutoML Applications

We have published a paper on Tabular Zeroshot-HPO ensembling simulation to arXiv (Paper Link, GitHub). This paper is key to achieving the performance improvements seen in AutoGluon 1.0, and we plan to continue to develop the code-base to support future enhancements.

XTab: Cross-table Pretraining for Tabular Transformers

We have published a paper on tabular Transformer pre-training at ICML 2023 (Paper Link, GitHub). In the paper we demonstrate state-of-the-art performance for tabular deep learning models, including being able to match the performance of XGBoost and LightGBM models. While the pre-trained transformer is not yet incorporated into AutoGluon, we plan to integrate it in a future release.

Learning Multimodal Data Augmentation in Feature Space

Our paper on learning multimodal data augmentation was accepted at ICLR 2023 (Paper Link, GitHub). This paper introduces a plug-and-play module to learn multimodal data augmentation in feature space, with no constraints on the identities of the modalities or the relationship between modalities. We show that it can (1) improve the performance of multimodal deep learning architectures, (2) apply to combinations of modalities that have not been previously considered, and (3) achieve state-of-the-art results on a wide range of applications comprised of image, text, and tabular data. This work is not yet incorporated into AutoGluon, but we plan to integrate it in a future release.

Data Augmentation for Object Detection via Controllable Diffusion Models

Our paper on generative object detection data augmentation has been accepted at WACV 2024 (Paper and GitHub link will be available soon). This paper proposes a data augmentation pipeline based on controllable diffusion models and CLIP, with visual prior generation to guide the generation and post-filtering by category-calibrated CLIP scores to control its quality. We demonstrate that the performance improves across various tasks and settings when using our augmentation pipeline with different detectors. Although diffusion models are currently not integrated into AutoGluon, we plan to incorporate the data augmentation techniques in a future release.

Adapting Image Foundation Models for Video Understanding

We have published a paper on how to efficiently adapt image foundation models for video understanding at ICLR 2023 (Paper Link, GitHub). This paper introduces spatial adaptation, temporal adaptation and joint adaptation to gradually equip a frozen image model with spatiotemporal reasoning capability. The proposed method achieves competitive or even better performance than traditional full finetuning while largely saving the training cost of large foundation models.

v0.8.3

Version 0.8.3

v0.8.3 is a patch release to address security vulnerabilities.

See the full commit change-log here: https://github.com/autogluon/autogluon/compare/v0.8.2…v0.8.3

This version supports Python versions 3.8, 3.9, and 3.10.

Changes

  • transformers and other packages version upgrades + some fixes. @suzhoum (#4155)

v0.8.2

Version 0.8.2

v0.8.2 is a hot-fix release to pin pydantic version to avoid crashing during HPO

As always, only load previously trained models using the same version of AutoGluon that they were originally trained on. Loading models trained in different versions of AutoGluon is not supported.

See the full commit change-log here: https://github.com/autogluon/autogluon/compare/v0.8.1…v0.8.2

This version supports Python versions 3.8, 3.9, and 3.10.

Changes

v0.8.1

Version 0.8.1

v0.8.1 is a bug fix release.

As always, only load previously trained models using the same version of AutoGluon that they were originally trained on. Loading models trained in different versions of AutoGluon is not supported.

See the full commit change-log here: https://github.com/autogluon/autogluon/compare/v0.8.0…v0.8.1

This version supports Python versions 3.8, 3.9, and 3.10.

Changes

Documentation improvements

Bug Fixes / General Improvements

v0.8.0

Version 0.8.0

We’re happy to announce the AutoGluon 0.8 release.

Note: Loading models trained in different versions of AutoGluon is not supported.

This release contains 196 commits from 20 contributors!

See the full commit change-log here: https://github.com/autogluon/autogluon/compare/0.7.0…0.8.0

Special thanks to @geoalgo for the joint work in generating the experimental tabular Zeroshot-HPO portfolio this release!

Full Contributor List (ordered by # of commits):

@shchur, @Innixma, @yinweisu, @gradientsky, @FANGAreNotGnu, @zhiqiangdon, @gidler, @liangfu, @tonyhoo, @cheungdaven, @cnpgs, @giswqs, @suzhoum, @yongxinw, @isunli, @jjaeyeon, @xiaochenbin9527, @yzhliu, @jsharpna, @sxjscience

AutoGluon 0.8 supports Python versions 3.8, 3.9, and 3.10.

Changes

Highlights

  • AutoGluon TimeSeries introduced several major improvements, including new models, upgraded presets that lead to better forecast accuracy, and optimizations that speed up training & inference.

  • AutoGluon Tabular now supports calibrating the decision threshold in binary classification (API), leading to massive improvements in metrics such as f1 and balanced_accuracy. It is not uncommon to see f1 scores improve from 0.70 to 0.73 as an example. We strongly encourage all users who are using these metrics to try out the new decision threshold calibration logic.

  • AutoGluon MultiModal introduces two new features: 1) PDF document classification, and 2) Open Vocabulary Object Detection.

  • AutoGluon MultiModal upgraded the presets for object detection, now offering medium_quality, high_quality, and best_quality options. The empirical results demonstrate significant ~20% relative improvements in the mAP (mean Average Precision) metric, using the same preset.

  • AutoGluon Tabular has added an experimental Zeroshot HPO config which performs well on small datasets <10000 rows when at least an hour of training time is provided (~60% win-rate vs best_quality). To try it out, specify presets="experimental_zeroshot_hpo_hybrid" when calling fit().

  • AutoGluon EDA added support for Anomaly Detection and Partial Dependence Plots.

  • AutoGluon Tabular has added experimental support for TabPFN, a pre-trained tabular transformer model. Try it out via pip install autogluon.tabular[all,tabpfn] (hyperparameter key is “TABPFN”)!

General

Multimodal

AutoGluon MultiModal (also known as AutoMM) introduces two new features: 1) PDF document classification, and 2) Open Vocabulary Object Detection. Additionally, we have upgraded the presets for object detection, now offering medium_quality, high_quality, and best_quality options. The empirical results demonstrate significant ~20% relative improvements in the mAP (mean Average Precision) metric, using the same preset.

New Features

Performance Improvements

  • Upgrade the detection engine from mmdet 2.x to mmdet 3.x, and upgrade our presets @FANGAreNotGnu (#3262)

    • medium_quality: yolo-s -> yolox-l

    • high_quality: yolox-l -> DINO-Res50

    • best_quality: yolox-x -> DINO-Swin_l

  • Speedup fusion model training with deepspeed strategy. @liangfu (#2932)

  • Enable detection backbone freezing to boost finetuning speed and save GPU usage @FANGAreNotGnu (#3220)

Other Enhancements

  • Support passing data path to the fit() API @zhiqiangdon (#3006)

  • Upgrade TIMM to the latest v0.9.* @zhiqiangdon (#3282)

  • Support xywh output for object detection @FANGAreNotGnu (#2948)

  • Fusion model inference acceleration with TensorRT @liangfu (#2836, #2987)

  • Support customizing advanced image data augmentation. Users can pass a list of torchvision transform objects as image augmentation. @zhiqiangdon (#3022)

  • Add yoloxm and yoloxtiny @FangAreNotGnu (#3038)

  • Add MultiImageMix Dataset for Object Detection @FangAreNotGnu (#3094)

  • Support loading specific checkpoints. Users can load the intermediate checkpoints other than model.ckpt and last.ckpt. @zhiqiangdon (#3244)

  • Add some predictor properties for model statistics @zhiqiangdon (#3289)

    • trainable_parameters returns the number of trainable parameters.

    • total_parameters returns the number of total parameters.

    • model_size returns the model size measured by megabytes.

Bug Fixes / Code and Doc Improvements

Tabular

New Features

  • Added calibrate_decision_threshold (tutorial), which allows to optimize a given metric’s decision threshold for predictions to strongly enhance the metric score. @Innixma (#3298)

  • We’ve added an experimental Zeroshot HPO config, which performs well on small datasets <10000 rows when at least an hour of training time is provided. To try it out, specify presets="experimental_zeroshot_hpo_hybrid" when calling fit() @Innixma @geoalgo (#3312)

  • The TabPFN model is now supported as an experimental model. TabPFN is a viable model option when inference speed is not a concern, and the number of rows of training data is less than 10,000. Try it out via pip install autogluon.tabular[all,tabpfn]! @Innixma (#3270)

  • Backend support for distributed training, which will be available with the next Cloud module release. @yinweisu (#3054, #3110, #3115, #3131, #3142, #3179, #3216)

Performance Improvements

Other Enhancements

Bug Fixes / Code and Doc Improvements

TimeSeries

In v0.8 we introduce several major improvements to the Time Series module, including new models, upgraded presets that lead to better forecast accuracy, and optimizations that speed up training & inference.

Highlights

  • New models: PatchTST and DLinear from GluonTS, and RecursiveTabular based on integration with the mlforecast library @shchur (#3177, #3184, #3230)

  • Improved accuracy and reduced overall training time thanks to updated presets @shchur (#3281, #3120)

  • 3-6x faster training and inference for AutoARIMA, AutoETS, Theta, DirectTabular, WeightedEnsemble models @shchur (#3062, #3214, #3252)

New Features

  • Dramatically faster repeated calls to predict(), leaderboard() and evaluate() thanks to prediction caching @shchur (#3237)

  • Reduce overfitting by using multiple validation windows with the num_val_windows argument to fit() @shchur (#3080)

  • Exclude certain models from presets with the excluded_model_types argument to fit() @shchur (#3231)

  • New method refit_full() that refits models on combined train and validation data @shchur (#3157)

  • Train multiple configurations of the same model by providing lists in the hyperparameters argument @shchur (#3183)

  • Time limit set by time_limit is now respected by all models @shchur (#3214)

Enhancements

  • Improvements to the DirectTabular model (previously called AutoGluonTabular): faster featurization, trained as a quantile regression model if eval_metric is set to "mean_wQuantileLoss" @shchur (#2973, #3211)

  • Use correct seasonal period when computing the MASE metric @shchur (#2970)

  • Check the AutoGluon version when loading TimeSeriesPredictor from disk @shchur (#3233)

Minor Improvements / Documentation / Bug Fixes

Exploratory Data Analysis (EDA) tools

In 0.8 we introduce a few new tools to help with data exploration and feature engineering:

  • Anomaly Detection @gradientsky (#3124, #3137) - helps to identify unusual patterns or behaviors in data that deviate significantly from the norm. It’s best used when finding outliers, rare events, or suspicious activities that could indicate fraud, defects, or system failures. Check the Anomaly Detection Tutorial to explore the functionality.

  • Partial Dependence Plots @gradientsky (#3071, #3079) - visualize the relationship between a feature and the model’s output for each individual instance in the dataset. Two-way variant can visualize potential interactions between any two features. Please see this tutorial for more detail: Using Interaction Charts To Learn Information About the Data

Bug Fixes / Code and Doc Improvements