AutoGluon-Cloud: Train and Deploy AutoGluon on the Cloud

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.

Note

Example using AutoGluon-Cloud to train and deploy an AutoGluon backed model on AWS SageMaker:

>>> 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)

Installation

Note

>>> pip install -U pip
>>> pip install -U setuptools wheel
>>> pip install autogluon.cloud  # You don't need to install autogluon itself locally