Skip to content

v0.4.1
Compare
Choose a tag to compare
@gradientsky gradientsky released this 21 May 04:05
· 1367 commits to master since this release
v0.4.1
d32695f
This commit was created on GitHub.com and signed with GitHub’s verified signature. The key has expired.
GPG key ID: 4AEE18F83AFDEB23
Expired
Learn about vigilant mode.

Version 0.4.1

We're happy to announce the AutoGluon 0.4.1 release. 0.4.1 contains minor enhancements to Tabular, Text, Image, and Multimodal modules, along with many quality of life improvements and fixes.

This release is non-breaking when upgrading from v0.4.0. 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 55 commits from 10 contributors!

See the full commit change-log here: v0.4.0...v0.4.1

Special thanks to @yiqings, @leandroimail, @huibinshen who were first time contributors to AutoGluon this release!

Full Contributor List (ordered by # of commits):

This version supports Python versions 3.7 to 3.9.

Changes

AutoMM

New features

  • Added optimization.efficient_finetune flag to support multiple efficient finetuning algorithms. (#1666) @sxjscience

  • Enabled knowledge distillation for AutoMM (#1670) @zhiqiangdon

    • Distillation API for AutoMMPredictor reuses the .fit() function:
    from autogluon.text.automm import AutoMMPredictor
teacher_predictor = AutoMMPredictor(label="label_column").fit(train_data)
student_predictor = AutoMMPredictor(label="label_column").fit(
    train_data, 
    hyperparameters=student_and_distiller_hparams, 
    teacher_predictor=teacher_predictor,
)

  • Option to turn on returning feature column information (#1711) @zhiqiangdon

    • The feature column information is turned on for feature column distillation; for other cases it is turned off by default to reduce dataloader‘s latency.
    • Added a requires_column_info flag in data processors and a utility function to turn this flag on or off.

  • FT-Transformer implementation for tabular data in AutoMM (#1646) @yiqings

    • Yury Gorishniy, Ivan Rubachev, Valentin Khrulkov, Artem Babenko, "Revisiting Deep Learning Models for Tabular Data" 2022. (arxiv, official implementation)

  • Make CLIP support multiple images per sample (#1606) @zhiqiangdon

    • Added multiple images support for CLIP. Improved data loader robustness: added missing images handling to prevent training crashes.
    • Added the choice of using a zero image if an image is missing.

  • Avoid using eos as the sep token for CLIP. (#1710) @zhiqiangdon

  • Update fusion transformer in AutoMM (#1712) @yiqings

    • Support constant learning rate in polynomial_decay scheduler.
    • Update [CLS] token in numerical/categorical transformer.

  • Added more image augmentations: verticalflip, colorjitter, randomaffine (#1719) @Linuxdex, @sxjscience

  • Added prompts for the percentage of missing images during image column detection. (#1623) @zhiqiangdon

  • Support average_precision in AutoMM (#1697) @sxjscience

  • Convert roc_auc / average_precision to log_loss for torchmetrics (#1715) @zhiqiangdon

    • torchmetrics.AUROC requires both positive and negative examples are available in a mini-batch. When training a large model, the per gpu batch size is probably small, leading to an incorrect roc_auc score. Conversion from roc_auc to log_loss improves training stablility.

  • Added pytorch-lightning 1.6 support (#1716) @sxjscience

Checkpointing and Model Outputs Changes

  • Updated the names of top-k checkpoint average methods and support customizing model names for terminal input (#1668) @zhiqiangdon

    • Following paper: https://arxiv.org/pdf/2203.05482.pdf to update top-k checkpoint average names: union_soup -> uniform_soup and best_soup -> best.
    • Update function names (customize_config_names -> customize_model_names and verify_config_names -> verify_model_names) to make it easier to understand them.
    • Support customizing model names for the terminal input.

  • Implemented the GreedySoup algorithm proposed in paper. Added union_soup, greedy_soup, best_soup flags and changed the default value correspondingly. (#1613) @sxjscience

  • Updated the standalone flag in automm.predictor.save() to save the pertained model for offline deployment (#1575) @yiqings

    • An efficient implementation to save the donwloaded models from transformers for the offline deployment. Revised logic is in #1572, and discussed in #1572 (comment).

  • Simplified checkpoint template (#1636) @zhiqiangdon

    • Stopped using pytorch lightning's model checkpoint template in saving AutoMMPredictor's final model checkpoint.
    • Improved the logic of continuous training. We pass the ckpt_path argument to pytorch lightning's trainer only when resume=True.

  • Unified AutoMM's model output format and support customizing model names (#1643) @zhiqiangdon

    • Now each model's output is dictionary with the model prefix as the first level key. The format is uniform between single model and fusion model.
    • Now users can customize model names by using the internal registered names (timm_image, hf_text, clip, numerical_mlp, categorical_mlp, and fusion_mlp) as prefixes. This is helpful when users want to simultaneously use two models of the same type, e.g., hf_text. They can just use names hf_text_0 and hf_text_1.

  • Support standalone feature in TextPredictor (#1651) @yiqings

  • Fixed saving and loading tokenizers and text processors (#1656) @zhiqiangdon

    • Saved pre-trained huggingface tokenizers separately from the data processors.
    • This change is backwards-compatibile with checkpoints saved by verison 0.4.0.

  • Change load from a classmethod to staticmethod to avoid incorrect usage. (#1697) @sxjscience

  • Added AutoMMModelCheckpoint to avoid evaluating the models to obtain the scores (#1716) @sxjscience

    • checkpoint will save the best_k_models into a yaml file so that it can be loaded later to determine the path to model checkpoints.

  • Extract column features from AutoMM's model outputs (#1718) @zhiqiangdon

    • Add one util function to extract column features for both image and text.
    • Support extracting column features for models timm_image, hf_text, and clip.

  • Make AutoMM dataloader return feature column information (#1710) @zhiqiangdon

Bug fixes

  • Fixed calling save_pretrained_configs in AutoMMPrediction.save(standalone=True) when no fusion model exists (here) (#1651) @yiqings

  • Fixed error raising for setting key that does not exist in the configuration (#1613) @sxjscience

  • Fixed warning message about bf16. (#1625) @sxjscience

  • Fixed the corner case of calculating the gradient accumulation step (#1633) @sxjscience

  • Fixes for top-k averaging in the multi-gpu setting (#1707) @zhiqiangdon

Tabular

  • Limited RF max_leaf_nodes to 15000 (previously uncapped) (#1717) @Innixma

    • Previously, for very large datasets RF/XT memory and disk usage would quickly become unreasonable. This ensures that at a certain point RF and XT will no longer become larger given more rows of training data. Benchmark results showed that the change is an improvement, particularly for the high_quality preset.

  • Limit KNN to 32 CPUs to avoid OpenBLAS error (#1722) @Innixma

    • Issue #1020. When training K-nearest-neighbors (KNN) models, sometimes a rare error can occur that crashes the entire process:
    BLAS : Program is Terminated. Because you tried to allocate too many memory regions.
Segmentation fault: 11

    This error occurred when the machine had many CPU cores (>64 vCPUs) due to too many threads being created at once. By limiting to 32 cores used, the error is avoided.

  • Improved memory warning thresholds (#1626) @Innixma

  • Added get_results and model_base_kwargs (#1618) @Innixma

    • Added get_results to searchers, useful for debugging and for future extensions to HPO functionality.
      Added new way to init a BaggedEnsembleModel that avoids having to init the base model prior to initing the bagged ensemble model.

  • Update resource logic in models (#1689) @Innixma

    • Previous implementation would crash if user specified auto for resources, fixed in this PR.
    • Added get_minimum_resources to explicitly define minimum resource requirements within a method.

  • Updated feature importance default subsample_size 1000 -> 5000, num_shuffle_sets 3 -> 5 (#1708) @Innixma

    • This will improve the quality of the feature importance values by default, especially the 99% confidence bounds. The change increases the time taken by ~8x, but this is acceptable because of the numerous inference speed optimizations done since these defaults were first introduced.

  • Added notice to ensure serializable custom metrics (#1705) @Innixma

Bug fixes

  • Fixed evaluate when weight_evaluation=True (#1612) @Innixma

    • Previously, AutoGluon would crash if the user specified predictor.evaluate(...) or predictor.evaluate_predictions(...) when self.weight_evaluation==True.

  • Fixed RuntimeError: dictionary changed size during iteration (#1684, #1685) @leandroimail

  • Fixed CatBoost custom metric & F1 support (#1690) @Innixma

  • Fixed HPO not working for bagged models if the bagged model is loaded from disk (#1702) @Innixma

  • Fixed Feature importance erroring if self.model_best is None (can happen if no Weighted Ensemble is fit) (#1702) @Innixma

Documentation

  • updated the text tutorial of cutomizing hyperparameters (#1620) @zhiqiangdon

    • Added customizeable backbones from the Huggingface model zoo and how to use local backbones.

  • Improved implementations and docstrings of save_pretrained_models and convert_checkpoint_name. (#1656) @zhiqiangdon

  • Added cheat sheet to website (#1605) @yinweisu

  • Doc fix to use correct predictor when calling leaderboard (#1652) @Innixma

Miscellaneous changes

  • [security] updated pillow to 9.0.1+ (#1615) @gradientsky

  • [security] updated ray to 1.10.0+ (#1616) @yinweisu

  • Tabular regression tests improvements (#1555) @willsmithorg

    • Regression testing of model list and scores in tabular on small synthetic datasets (for speed).
    • Tests about 20 different calls to TabularPredictor on both regression and classification tasks, multiple presets etc.
    • When a test fails it dumps out the config change required to make it pass, for ease of updating.

  • Disabled image/text predictor when gpu is not available in TabularPredictor (#1676) @yinweisu

    • Resources are validated before bagging is started. Image/text predictor model would require minimum of 1 gpu.

  • Use class property to set keys in model classes. In this way, if we customize the prefix key, other keys are automatically updated. (#1669) @zhiqiangdon

Various bugfixes, documentation and CI improvements

Contributors

willsmithorg, canerturkmen, and 9 other contributors
Assets 2