[tune](deps): Bump autogluon-core from 0.0.16b20210113 to 0.3.2b20211022 in /python/requirements

[dependabot]

Bumps autogluon-core from 0.0.16b20210113 to 0.3.2b20211022.

Release notes

Sourced from autogluon-core's releases.

v0.3.1 is a hotfix release which fixes several major bugs as well as including several model quality improvements.

This release is non-breaking when upgrading from v0.3.0. 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 9 commits from 4 contributors.

See the full commit change-log here: autogluon/autogluon@v0.3.0...v0.3.1

Thanks to the 4 contributors that contributed to the v0.3.1 release!

Special thanks to @​yinweisu who is a first time contributor to AutoGluon and fixed a major bug in ImagePredictor HPO!

Full Contributor List (ordered by # of commits):

@​Innixma, @​gradientsky, @​yinweisu, @​sackoh

Changes

Tabular

• AutoGluon v0.3.1 has a 58% win-rate vs AutoGluon v0.3.0 for best_quality preset.
• AutoGluon v0.3.1 has a 75% win-rate vs AutoGluon v0.3.0 for high and good quality presets.
• Fixed major bug introduced in v0.3.0 with models trained in refit_full causing weighted ensembles to incorrectly weight models. This severely impacted accuracy and caused worse results for high and good quality presets. @​Innixma (#1293)
• Removed KNN from stacker models, resulting in stack quality improvement. @​Innixma (#1294)
• Added automatic detection and optimized usage of boolean features. @​Innixma (#1286)
• Improved handling of time limit in FastAI NN model to avoid edge cases where the model would use the entire time budget but fail to train. @​Innixma (#1284)
• Updated XGBoost to use -1 as n_jobs value instead of using os.cpu_count(). @​sackoh (#1289)

Vision

• Fixed major bug that caused HPO with time limits specified to return very poor models. @​yinweisu (#1282)

General

• Minor doc updates. @​gradientsky (#1288, #1290)

v0.3.0 introduces multi-modal image, text, tabular support to AutoGluon. In just a few lines of code, you can train a multi-layer stack ensemble using text, image, and tabular data! To our knowledge this is the first publicly available implementation of a model that handles all 3 modalities at once. Check it out in our brand new multimodal tutorial! v0.3.0 also features a major model quality improvement for Tabular, with a 57.6% winrate vs v0.2.0 on the AutoMLBenchmark, along with an up to 10x online inference speedup due to low level numpy and pandas optimizations throughout the codebase! This inference optimization enables AutoGluon to have sub 30 millisecond end-to-end latency for real-time deployment scenarios when paired with model distillation. Finally, AutoGluon can now train PyTorch image models via integration with TIMM. Specify any TIMM model to ImagePredictor or TabularPredictor to train them with AutoGluon!

This release is non-breaking when upgrading from v0.2.0. 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 70 commits from 10 contributors.

See the full commit change-log here: autogluon/autogluon@v0.2.0...v0.3.0

Thanks to the 10 contributors that contributed to the v0.3.0 release!

Special thanks to the 3 first-time contributors! @​rxjx, @​sallypannn, @​sarahyurick

Special thanks to @​talhaanwarch who opened 21 GitHub issues (!) and participated in numerous discussions during v0.3.0 development. His feedback was incredibly valuable when diagnosing issues and improving the user experience throughout AutoGluon!

... (truncated)

Commits

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