Metadata-Version: 2.1
Name: autogluon.cloud
Version: 0.2.1b20231117
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
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 :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Topic :: Software Development
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Scientific/Engineering :: Information Analysis
Classifier: Topic :: Scientific/Engineering :: Image Recognition
Requires-Python: >=3.8, <3.11
Description-Content-Type: text/markdown
Requires-Dist: autogluon.common <1.0,>=0.7
Requires-Dist: boto3 <2.0,>=1.10
Requires-Dist: numpy <1.27,>=1.21
Requires-Dist: packaging <24.0,>=23.0
Requires-Dist: pandas <1.6,>=1.4.1
Requires-Dist: sagemaker <3.0,>=2.126.0
Requires-Dist: pyarrow <11.1,>=11.0
Requires-Dist: PyYAML ~=6.0
Requires-Dist: Pillow <10.0,>=9.3.0
Requires-Dist: ray[default] <2.4.0,>=2.3.0
Provides-Extra: all
Requires-Dist: autogluon <1.0,>=0.7 ; extra == 'all'
Provides-Extra: tests
Requires-Dist: moto[all] ; extra == 'tests'
Requires-Dist: pytest-cov ; extra == 'tests'
Requires-Dist: pytest ; extra == 'tests'
Requires-Dist: tox ; extra == 'tests'
Requires-Dist: autogluon.common <1.0,>=0.7.0b ; extra == 'tests'



<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 autogluon.cloud==0.2.0  # You don't need to install autogluon itself locally

from autogluon.cloud import TabularCloudPredictor
import pandas as pd
train_data = pd.read_csv("https://autogluon.s3.amazonaws.com/datasets/Inc/train.csv")
test_data = pd.read_csv("https://autogluon.s3.amazonaws.com/datasets/Inc/test.csv")
predictor_init_args = {"label": "class"}  # init args you would pass to AG TabularPredictor
predictor_fit_args = {"train_data": train_data, "time_limit": 120}  # fit args you would pass to AG TabularPredictor
cloud_predictor = TabularCloudPredictor(cloud_output_path='YOUR_S3_BUCKET_PATH')
cloud_predictor.fit(predictor_init_args=predictor_init_args, predictor_fit_args=predictor_fit_args)
cloud_predictor.deploy()
result = cloud_predictor.predict_real_time(test_data)
cloud_predictor.cleanup_deployment()
# Batch inference
result = cloud_predictor.predict(test_data)
```


