https://github.com/autogluon/autogluon-cloud

Autogluon-cloud aims to provide user tools to train, fine-tune and deploy AutoGluon 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
https://github.com/autogluon/autogluon-cloud

Last synced: about 1 year ago
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Autogluon-cloud aims to provide user tools to train, fine-tune and deploy AutoGluon 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

• Host: GitHub
• URL: https://github.com/autogluon/autogluon-cloud
• Owner: autogluon
• License: apache-2.0
• Created: 2022-12-14T22:01:18.000Z (over 3 years ago)
• Default Branch: master
• Last Pushed: 2024-05-21T08:48:11.000Z (about 2 years ago)
• Last Synced: 2024-05-22T09:17:10.290Z (about 2 years ago)
• Language: Python
• Size: 303 KB
• Stars: 16
• Watchers: 10
• Forks: 10
• Open Issues: 15
• Metadata Files:
  • Readme: README.md
  • Contributing: CONTRIBUTING.md
  • License: LICENSE
  • Code of conduct: CODE_OF_CONDUCT.md

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# 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 Documentation](https://auto.gluon.ai/cloud/stable/index.html) | [AutoGluon Documentation](https://auto.gluon.ai)

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 [Amazon SageMaker](https://aws.amazon.com/sagemaker/) as the cloud backend.

## Installation

```bash

pip install -U pip

pip install -U setuptools wheel

pip install autogluon.cloud

```

## Example

```python

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")

test_data.drop(columns=['class'], inplace=True)

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)

```