Version 0.5.1

We're happy to announce the AutoGluon 0.5 release. This release contains major optimizations and bug fixes to autogluon.multimodal and autogluon.timeseries modules, as well as inference speed improvements to autogluon.tabular.

This release is non-breaking when upgrading from v0.5.0. 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.

This release contains 58 commits from 14 contributors!

Full Contributor List (ordered by # of commits):

• @zhiqiangdon, @yinweisu, @Innixma, @canerturkmen, @sxjscience, @bryanyzhu, @jsharpna, @gidler, @gradientsky, @Linuxdex, @muxuezi, @yiqings, @huibinshen, @FANGAreNotGnu

This version supports Python versions 3.7 to 3.9.

See the full commit change-log here: v0.5.0...v0.5.1

AutoMM

Changed to a new namespace autogluon.multimodal (AutoMM), which is a deep learning "model zoo" of model zoos. On one hand, AutoMM can automatically train deep models for unimodal (image-only, text-only or tabular-only) problems. On the other hand, AutoMM can automatically solve multimodal (any combinations of image, text, and tabular) problems by fusing multiple deep learning models. In addition, AutoMM can be used as a base model in AutoGluon Tabular and participate in the model ensemble.

New features

• Supported zero-shot learning with CLIP (#1922) @zhiqiangdon

  • Users can directly perform zero-shot image classification with the CLIP model. Moreover, users can extract image and text embeddings with CLIP to do image-to-text or text-to-image retrieval.

• Improved efficient finetuning

  • Support “bit_fit”, “norm_fit“, “lora”, “lora_bias”, “lora_norm”. In four multilingual datasets (xnli, stsb_multi_mt, paws-x, amazon_reviews_multi), “lora_bias”, which is a combination of LoRA and BitFit, achieved the best overall performance. Compared to finetuning the whole network, “lora_bias” will only finetune <0.5% of the network parameters and can achieve comparable performance on “stsb_multi_mt” (#1780, #1809). @Raldir @zhiqiangdon
  • Support finetuning the mT5-XL model that has 1.7B parameters on a single NVIDIA G4 GPU. In AutoMM, we only use the T5-encoder (1.7B parameters) like Sentence-T5. (#1933) @sxjscience

• Added more data augmentation techniques

  • Mixup for image data. (#1730) @Linuxdex
  • TrivialAugment for both image and text data. (#1792) @lzcemma
  • Easy text augmentations. (#1756) @lzcemma

• Enhanced teacher-student model distillation

  • Support distilling the knowledge from a unimodal/multimodal teacher model to a student model. (#1670, #1895) @zhiqiangdon

More tutorials and examples

• Beginner tutorials of applying AutoMM to image, text, or multimodal (including tabular) data. (#1861, #1908, #1858, #1869) @bryanyzhu @sxjscience @zhiqiangdon

• A zero-shot image classification tutorial with the CLIP model. (#1942) @bryanyzhu

• A tutorial of using CLIP model to extract embeddings for image-text retrieval. (#1957) @bryanyzhu

• A tutorial to introduce comprehensive AutoMM configurations (#1861). @zhiqiangdon

• AutoMM for tabular data examples (#1752, #1893, #1903). @yiqings

• AutoMM distillation example (#1846). @FANGAreNotGnu

• A Kaggle notebook about how to use AutoMM to predict pet adoption: https://www.kaggle.com/code/linuxdex/use-autogluon-to-predict-pet-adoption. The model achieves the score equivalent to top 1% (20th/3537) in this kernel-only competition (test data is only available in the kernel without internet access) (#1796, #1847, #1894, #1943). @Linuxdex