Skip to content

Innixma/fall-automl-2022-autogluon

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

3 Commits
 
 
 
 
 
 

Repository files navigation

Fall AutoML 2022 AutoGluon Hands-on Tutorial

Latest Release Continuous Integration Platform Tests Python Versions GitHub license Downloads Twitter

Live Talk Recording: https://www.youtube.com/watch?v=VAAITEds-28

Install Instructions | Documentation (Stable | Latest)

AutoGluon automates machine learning tasks enabling you to easily achieve strong predictive performance in your applications. With just a few lines of code, you can train and deploy high-accuracy machine learning and deep learning models on image, text, time series, and tabular data.

Example

# First install package from terminal:
# pip install -U pip
# pip install -U setuptools wheel
# pip install autogluon  # autogluon==0.5.2

from autogluon.tabular import TabularDataset, TabularPredictor
train_data = TabularDataset('https://autogluon.s3.amazonaws.com/datasets/Inc/train.csv')
test_data = TabularDataset('https://autogluon.s3.amazonaws.com/datasets/Inc/test.csv')
predictor = TabularPredictor(label='class').fit(train_data, time_limit=120)  # Fit models for 120s
leaderboard = predictor.leaderboard(test_data)
AutoGluon Task Quickstart API
TabularPredictor Quick Start API
TextPredictor Quick Start API
ImagePredictor Quick Start API
ObjectDetector Quick Start API
MultiModalPredictor Quick Start API
TimeSeriesPredictor Quick Start API

Tutorial Setup

It is recommended to use Google Colab to run the tutorials. To do so, go to the Jupyter Notebooks linked below, go to the notebook you wish to run, and click 'Open in Colab'.

If you want to run AutoGluon locally, follow the {install instructions}.

Tutorial Links

AutoGluon-Tabular Tutorials

All Jupyter Notebooks

Extra Links

AutoGluon Website: https://auto.gluon.ai/stable/index.html

AutoGluon GitHub: https://github.com/awslabs/autogluon/

Fall AutoML School 2022 Website: https://sites.google.com/view/automl-fall-school-2022

AutoGluon-Tabular Paper: https://arxiv.org/abs/2003.06505

About

No description, website, or topics provided.

Resources

License

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published