Metadata-Version: 2.1
Name: autogluon.cloud
Version: 0.1b20230123
Summary: Train and deploy AutoGluon backed models on the cloud
Home-page: https://github.com/autogluon/autogluon-cloud
Author: AutoGluon Community
License: Apache-2.0
Project-URL: Documentation, https://auto.gluon.ai
Project-URL: Bug Reports, https://github.com/autogluon/autogluon-cloud/issues
Project-URL: Source, https://github.com/autogluon/autogluon-cloud/
Project-URL: Contribute!, https://github.com/autogluon/autogluon-cloud/blob/master/CONTRIBUTING.md
Description: 
        
        <div align="left">
          <img src="https://user-images.githubusercontent.com/16392542/77208906-224aa500-6aba-11ea-96bd-e81806074030.png" width="350">
        </div>
        
        # AutoGluon-Cloud
        
        [![Continuous Integration](https://github.com/autogluon/autogluon-cloud/actions/workflows/continuous_integration.yml/badge.svg)](https://github.com/autogluon/autogluon-cloud/actions/workflows/continuous_integration.yml)
        
        AutoGluon-Cloud aims to provide user tools to train, fine-tune and deploy [AutoGluon](https://auto.gluon.ai/stable/index.html) backed models on the cloud. With just a few lines of codes, users could train a model and perform inference on the cloud without worrying about MLOps details such as resource management.
        
        Currently, AutoGluon-Cloud supports [AWS SageMaker](https://aws.amazon.com/sagemaker/) as the cloud backend.
        
        ## Example
        ```python
        # First install package from terminal:
        # pip install -U pip
        # pip install -U setuptools wheel
        # pip install --pre autogluon.cloud  # You don't need to install autogluon itself locally
        
        from autogluon.cloud import TabularCloudPredictor
        train_data = 'train.csv'
        test_data = 'test.csv'
        predictor_init_args = {label='label'}  # init args you would pass to AG TabularPredictor
        predictor_fit_args = {train_data, time_limit=120}  # fit args you would pass to AG TabularPredictor
        # Train
        cloud_predictor = TabularCloudPredictor(cloud_output_path='YOUR_S3_BUCKET_PATH').fit(predictor_init_args, predictor_fit_args)
        # Deploy the endpoint
        cloud_predictor.deploy()
        # Real-time inference with the endpoint
        result = cloud_predictor.predict_real_time('test.csv')
        print(result)
        # Cleanup the endpoint
        cloud_predictor.cleanup_deployment()
        # Batch inference
        cloud_predictor.predict('test.csv')  # results will be stored in s3 bucket
        cloud_predictor.download_predict_results()  # download the results to your local machine
        ```
        
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Education
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Customer Service
Classifier: Intended Audience :: Financial and Insurance Industry
Classifier: Intended Audience :: Healthcare Industry
Classifier: Intended Audience :: Telecommunications Industry
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Operating System :: MacOS
Classifier: Operating System :: Microsoft :: Windows
Classifier: Operating System :: POSIX
Classifier: Operating System :: Unix
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Topic :: Software Development
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Scientific/Engineering :: Information Analysis
Classifier: Topic :: Scientific/Engineering :: Image Recognition
Requires-Python: >=3.7, <3.10
Description-Content-Type: text/markdown
Provides-Extra: tests
