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Merged
merged 11 commits into from
Jan 31, 2024
Merged

Refactor README for 1.0 #3861

merged 11 commits into from
Jan 31, 2024

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Innixma
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@Innixma Innixma commented Jan 17, 2024 •
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Issue #, if available:
#3776

Description of changes:
Improve and update the README file with updated links and improved wording.

Quick link to the updated README: https://github.com/Innixma/autogluon/blob/update_readme_1.0/README.md

TODO:

  • Center Image, Title, Badges, Opener
  • Change the tagline, don't use "AutoML for Image, Text, Time Series and Tabular Data", maybe "AutoML in 3 lines of code", etc.
  • Change code example to "train.csv", "test.csv", without functioning code, 3 lines, predict() instead of leaderboard()
  • Move most tutorials/papers/etc. to AWESOME.md, link to them in README.md
  • Task Table: Rename first column to AutoGluon Predictor, Then introduce Task as the second column: Classification, Regression, TimeSeries: Time Series Forecasting, Multimodal: Images, Texts, Multimodal, Object Detection, Named Entity Recognition. Something short maybe: Image, Text, Multimodal. Highlight which tasks AutoGluon solves in 3 lines of code. Make Predictors clickable, potentially remove API column. Maybe a notes column with quick description.
  • Add details collapsible headers for 3 LoC, Tabular is open, others are closed by default but can be opened. Question: Can we have buttons where only one details is visible at a time. Or maybe just have Tabular and leave others to tutorials.
  • Structure the links nicer
  • Update videos / talks
  • Update papers
  • Update cloud links
  • Update articles (Remove old ones?)
  • Include links to KDD/NeurIPS/etc. tutorials
  • Performance comparisons
  • Move paper citations to a dedicated file and link to it
  • Cleanup tutorial links (Currently have a table and a bullet list, remove redundancy)
  • Add discord / community section, maybe add to website too as a link

Maybe:

  • Add feature checklist comparison with existing AutoML systems
  • Enhance description of AutoGluon to highlight key ideas that achieve performance gains (highlighting ensembling and diversity maximization)
  • Add image showing AutoMM Architecture (as in 1.0 release notes)
  • Refactor code contribution guidelines

By submitting this pull request, I confirm that you can use, modify, copy, and redistribute this contribution, under the terms of your choice.

@Innixma Innixma added the API & Doc Improvements or additions to documentation label Jan 17, 2024
@Innixma Innixma changed the title Refactor README for 1.0 WIP: Refactor README for 1.0 Jan 17, 2024
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Job PR-3861-892c7bc is done.
Docs are uploaded to http://autogluon-staging.s3-website-us-west-2.amazonaws.com/PR-3861/892c7bc/index.html

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Job PR-3861-81bd880 is done.
Docs are uploaded to http://autogluon-staging.s3-website-us-west-2.amazonaws.com/PR-3861/81bd880/index.html

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Job PR-3861-2084e52 is done.
Docs are uploaded to http://autogluon-staging.s3-website-us-west-2.amazonaws.com/PR-3861/2084e52/index.html

@Innixma Innixma changed the title WIP: Refactor README for 1.0 Refactor README for 1.0 Jan 30, 2024
shchur
shchur approved these changes Jan 30, 2024
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This looks amazing! Thank you @Innixma

README.md Outdated

## :zap: Quickstart

Solve ML tasks end-to-end in just 3 lines of code!
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What about "Build accurate end-to-end ML models in just 3 lines of code!"? I'm not sure what exactly solving ML tasks end-to-end means here.

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Nice idea! Updated to this wording

README.md Outdated

## 💾 Installation

AutoGluon is supported on Python 3.8 - 3.11 and is available on Linux, MacOS, and Windows OS.
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Suggested change
AutoGluon is supported on Python 3.8 - 3.11 and is available on Linux, MacOS, and Windows OS.
AutoGluon is supported on Python 3.8 - 3.11 and is available on Linux, MacOS, and Windows.

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Good catch, updated

zhiqiangdon
zhiqiangdon approved these changes Jan 30, 2024
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LGTM. Left some comments which may be addressed in future.

CITING.md
Comment on lines +36 to +47
Shi, Xingjian, et al. ["Benchmarking Multimodal AutoML for Tabular Data with Text Fields."](https://datasets-benchmarks-proceedings.neurips.cc/paper/2021/file/9bf31c7ff062936a96d3c8bd1f8f2ff3-Paper-round2.pdf) Advances in Neural Information Processing Systems 35, Datasets and Benchmarks Track (2021).

BibTeX entry:

```bibtex
@inproceedings{agmultimodaltext,
title={Benchmarking Multimodal AutoML for Tabular Data with Text Fields},
author={Shi, Xingjian and Mueller, Jonas and Erickson, Nick and Li, Mu and Smola, Alexander J},
journal={Advances in Neural Information Processing Systems Datasets and Benchmarks Track},
volume={35},
year={2021}
}
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This should be a temporary solution. We will replace this when the official AutoMM paper is available.

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Definitely! Once it is available, you can send a PR to update this section

AWESOME.md Outdated
Comment on lines 18 to 29
| [AutoGluon 1.0: AutoML at Your Fingertips](https://autogluon.github.io/neurips-autogluon-workshop/) | Tutorial | [NeurIPS 2023](https://neurips.cc/Expo/Conferences/2023/workshop/78401) | 2023/12/10 |
| :tv: [AutoGluon 1.0: Shattering the AutoML Ceiling with Zero Lines of Code](https://www.youtube.com/watch?v=5tvp_Ihgnuk) | Tutorial | [AutoML Conf 2023](https://2023.automl.cc/) | 2023/09/12 |
| :sound: [AutoGluon: The Story](https://automlpodcast.com/episode/autogluon-the-story) | Podcast | [The AutoML Podcast](https://automlpodcast.com/) | 2023/09/05 |
| :tv: [AutoGluon: AutoML for Tabular, Multimodal, and Time Series Data](https://youtu.be/Lwu15m5mmbs?si=jSaFJDqkTU27C0fa) | Tutorial | PyData Berlin | 2023/06/20 |
| :tv: [Solving Complex ML Problems in a few Lines of Code with AutoGluon](https://www.youtube.com/watch?v=J1UQUCPB88I) | Tutorial | PyData Seattle | 2023/06/20 |
| [AutoGluon: Empowering (Multimodal) AutoML for the Next 10 Million Users](https://autogluon.github.io/neurips2022-autogluon-workshop/) | Tutorial | [NeurIPS 2022](https://nips.cc/Expo/Conferences/2022/workshop/63089) | 2022/11/28 |
| :tv: [The AutoML Revolution](https://www.youtube.com/watch?v=VAAITEds-28) | Tutorial | [Fall AutoML School 2022](https://sites.google.com/view/automl-fall-school-2022) | 2022/10/18 |
| [Multimodal AutoML for Image, Text and Tabular Data](https://github.com/Innixma/kdd-2022-multimodal-automl-tutorial) | Tutorial | [KDD 2022](https://kdd.org/kdd2022/lectureTutorial.html) | 2022/08/14 |
| :tv: [Automating Machine Learning for the Rest of Us](https://www.youtube.com/watch?v=x-EEDBSl3OM) | Keynote | [AutoML Conf 2022](https://2022.automl.cc/index.html) | 2022/07/25 |
| :tv: [AutoGluon: re:MARS 2022 Keynote](https://www.youtube.com/watch?v=_wd0IJBTwbY&t=1277s) | Keynote | [Amazon re:MARS 2022](https://icml.cc/virtual/2020/workshop/5725) | 2022/06/24 |
| [Advancing the State of the Art in AutoML](https://developer.nvidia.com/blog/advancing-the-state-of-the-art-in-automl-now-10x-faster-with-nvidia-gpus-and-rapids/) | Talk | [GTC 2021](https://developer.nvidia.com/blog/advancing-the-state-of-the-art-in-automl-now-10x-faster-with-nvidia-gpus-and-rapids/) | 2021/06/09 |
| :tv: [AutoGluon and Distillation](https://icml.cc/virtual/2020/7045) | Keynote | [ICML 2020, AutoML Workshop](https://icml.cc/virtual/2020/workshop/5725) | 2020/07/18 |
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Looks like that a comprehensive AutoMM tutorial is lacking. Though some tutorials mention AutoMM to some degree, their introductions are inconsistent with the current AutoMM story and shape. We can add one when available.

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If any talk we have given is missing, let me know and we can add it. If we need to do one, an option is for you to record it and we can upload it to YouTube, I created an AutoGluon YouTube channel.

@Innixma Innixma merged commit 6d3cc70 into autogluon:master Jan 31, 2024
LennartPurucker pushed a commit to LennartPurucker/autogluon that referenced this pull request Jun 1, 2024
@Innixma @LennartPurucker
Refactor README for 1.0 (autogluon#3861)
2a32862
@Innixma Innixma deleted the update_readme_1.0 branch April 16, 2025 21:15
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@tonyhoo tonyhoo Awaiting requested review from tonyhoo

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@zhiqiangdon zhiqiangdon zhiqiangdon approved these changes

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