Metadata-Version: 2.1
Name: autogluon.vision
Version: 0.1.0b20210210
Summary: AutoML for Text, Image, and Tabular Data
Home-page: https://github.com/awslabs/autogluon
Author: AutoGluon Community
License: Apache-2.0
Description: 
        
        <div align="left">
          <img src="https://user-images.githubusercontent.com/16392542/77208906-224aa500-6aba-11ea-96bd-e81806074030.png" width="350">
        </div>
        
        ## AutoML for Text, Image, and Tabular Data
        
        [![Build Status](https://ci.gluon.ai/view/all/job/autogluon/job/master/badge/icon)](https://ci.gluon.ai/view/all/job/autogluon/job/master/)
        [![Pypi Version](https://img.shields.io/pypi/v/autogluon.svg)](https://pypi.org/project/autogluon/#history)
        ![Upload Python Package](https://github.com/awslabs/autogluon/workflows/Upload%20Python%20Package/badge.svg)
        
        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 text, image, and tabular data.
        
        ## Example
        
        ```python
        # First install package from terminal:
        # python3 -m pip install --upgrade pip
        # python3 -m pip install --upgrade setuptools
        # python3 -m pip install --upgrade "mxnet<2.0.0"
        # python3 -m pip install --pre autogluon
        
        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=60)  # Fit models for 60s
        leaderboard = predictor.leaderboard(test_data)
        ```
        ## News
        
        **Announcement for previous users:** The AutoGluon codebase has been modularized into [namespace packages](https://packaging.python.org/guides/packaging-namespace-packages/), which means you now only need those dependencies relevant to your prediction task of interest! For example, you can now work with tabular data without having to [install](https://auto.gluon.ai/dev/install.html) dependencies required for AutoGluon's computer vision tasks (and vice versa). Unfortunately this improvement required a minor API change (eg. instead of `from autogluon import TabularPrediction`, you should now do: `from autogluon.tabular import TabularPredictor`), for all versions newer than v0.0.15. Documentation/tutorials under the old API may still be viewed [for version 0.0.15](https://auto.gluon.ai/0.0.15/index.html) which is the last released version under the old API.
        
        
        ## Resources
        
        See the [AutoGluon Website](https://auto.gluon.ai/stable/index.html) for [documentation](https://auto.gluon.ai/stable/api/index.html) and instructions on:
        - [Installing AutoGluon](https://auto.gluon.ai/stable/index.html#installation)
        - [Learning with tabular data](https://auto.gluon.ai/stable/tutorials/tabular_prediction/tabular-quickstart.html)
          - [Tips to maximize accuracy](https://auto.gluon.ai/stable/tutorials/tabular_prediction/tabular-quickstart.html#maximizing-predictive-performance) (if **benchmarking**, make sure to run `fit()` with argument `presets='best_quality'`).  
        
        - [Learning with text data](https://auto.gluon.ai/stable/tutorials/text_prediction/beginner.html)
        - [Learning with image data](https://auto.gluon.ai/stable/tutorials/image_prediction/beginner.html)
        - More advanced topics such as [Neural Architecture Search](https://auto.gluon.ai/stable/tutorials/nas/index.html)
        
        ### Scientific Publications
        - [AutoGluon-Tabular: Robust and Accurate AutoML for Structured Data](https://arxiv.org/pdf/2003.06505.pdf) (*Arxiv*, 2020)
        
        ### Articles
        - [AutoGluon for tabular data: 3 lines of code to achieve top 1% in Kaggle competitions](https://aws.amazon.com/blogs/opensource/machine-learning-with-autogluon-an-open-source-automl-library/) (*AWS Open Source Blog*, Mar 2020)
        - [Accurate image classification in 3 lines of code with AutoGluon](https://medium.com/@zhanghang0704/image-classification-on-kaggle-using-autogluon-fc896e74d7e8) (*Medium*, Feb 2020)
        - [AutoGluon overview & example applications](https://towardsdatascience.com/autogluon-deep-learning-automl-5cdb4e2388ec?source=friends_link&sk=e3d17d06880ac714e47f07f39178fdf2) (*Towards Data Science*, Dec 2019)
        
        ### Hands-on Tutorials
        - [From HPO to NAS: Automated Deep Learning (CVPR 2020)](https://hangzhang.org/CVPR2020/)
        - [Practical Automated Machine Learning with Tabular, Text, and Image Data (KDD 2020)](https://jwmueller.github.io/KDD20-tutorial/)
        
        ### Train/Deploy AutoGluon in the Cloud
        - [AutoGluon-Tabular on AWS Marketplace](https://aws.amazon.com/marketplace/pp/prodview-n4zf5pmjt7ism)
        - [Running AutoGluon-Tabular on Amazon SageMaker](https://github.com/awslabs/amazon-sagemaker-examples/blob/master/advanced_functionality/autogluon-tabular/AutoGluon_Tabular_SageMaker.ipynb)
        - [Running AutoGluon Image Classification on Amazon SageMaker](https://github.com/zhanghang1989/AutoGluon-Docker)
        
        ## Citing AutoGluon
        
        If you use AutoGluon in a scientific publication, please cite the following paper:
        
        Erickson, Nick, et al. ["AutoGluon-Tabular: Robust and Accurate AutoML for Structured Data."](https://arxiv.org/abs/2003.06505) arXiv preprint arXiv:2003.06505 (2020).
        
        BibTeX entry:
        
        ```bibtex
        @article{agtabular,
          title={AutoGluon-Tabular: Robust and Accurate AutoML for Structured Data},
          author={Erickson, Nick and Mueller, Jonas and Shirkov, Alexander and Zhang, Hang and Larroy, Pedro and Li, Mu and Smola, Alexander},
          journal={arXiv preprint arXiv:2003.06505},
          year={2020}
        }
        ```
        
        ## AutoGluon for Hyperparameter and Neural Architecture Search (HNAS)
        
        AutoGluon also provides state-of-the-art tools for neural hyperparameter and architecture search, such as for example ASHA, Hyperband, Bayesian Optimization and BOHB. To get started, checkout the following resources
        
        - [General introduction into HNAS](https://www.youtube.com/watch?v=pB1LmZWK_N8&feature=youtu.be)
        - [Introduction into HNAS with AutoGluon](https://www.youtube.com/watch?v=GJVwUyVWZas)
        - [Example notebook](https://github.com/zhanghang1989/HPO2NAS-Tutorial-CVPR-ECCV2020/blob/master/mlp.ipynb)
        - [Example scripts for efficient multi-fidelity HNAS of PyTorch neural network models](https://github.com/awslabs/autogluon/tree/master/examples/hnas/)
        
        Also have a look at our paper ["Model-based Asynchronous Hyperparameter and Neural Architecture Search"](https://arxiv.org/abs/2003.10865) arXiv preprint arXiv:2003.10865 (2020).
        
        ```bibtex
        @article{abohb,
          title={Model-based Asynchronous Hyperparameter and Neural Architecture Search},
          author={Klein, Aaron and Tiao, Louis and Lienart, Thibaut and Archambeau, Cedric and Seeger, Matthias},
          journal={arXiv preprint arXiv:2003.10865},
          year={2020}
        }
        ```
        
        ## License
        
        This library is licensed under the Apache 2.0 License.
        
        ## Contributing to AutoGluon
        
        We are actively accepting code contributions to the AutoGluon project. If you are interested in contributing to AutoGluon, please read the [Contributing Guide](https://github.com/awslabs/autogluon/blob/master/CONTRIBUTING.md) to get started.
        
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Requires-Python: >=3.6, <3.8
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