Set Up AutoGluon-Cloud on AWS

AutoGluon-Cloud trains and deploys models on AWS SageMaker on your behalf. To do that, every CloudPredictor or FoundationModel you create needs two AWS resources:

• An IAM role that SageMaker assumes to run training and inference jobs.

• An S3 bucket to stage training data and store trained models.

You have two options for supplying them:

1. Save them once to ~/.autogluon/cloud.yaml, and AutoGluon-Cloud will pick them up automatically on every call. This is the recommended path — set it up with bootstrap or register below.

2. Pass them explicitly to each CloudPredictor / FoundationModel, e.g. CloudPredictor(role="arn:aws:iam::...", cloud_output_path="s3://my-bucket/..."). Useful if you need different roles or buckets per call, or if you don’t want a config file on disk.

The rest of this page covers option 1.

Commands

AutoGluon-Cloud ships four commands for managing the saved configuration:

Command	What it does	When to use it
bootstrap	Provisions a role and bucket via CloudFormation, then saves them.	First-time setup with no existing AWS resources.
register	Saves an existing role and bucket without provisioning anything.	Your platform team already gave you a role and bucket.
status	Verifies the saved resources still exist and are accessible.	Sanity-check before training, or after IAM/S3 changes.
teardown	Deletes resources created by bootstrap and the saved config.	Cleanup when you’re done with AutoGluon-Cloud.

Each command is available both as a CLI subcommand (autogluon-cloud <command>) and as a Python function (from autogluon.cloud import <command>). The sections below show both forms.

Install

pip install -U autogluon.cloud

This installs the autogluon-cloud CLI alongside the Python API.

bootstrap

Provisions an IAM role and S3 bucket via CloudFormation, then saves them to ~/.autogluon/cloud.yaml. Use this if you don’t already have AWS resources for AutoGluon-Cloud.

bootstrap uses the standard boto3 credential resolution order to find your AWS credentials, so anything that works for the AWS CLI or boto3 will work here (aws configure, AWS_* environment variables, an active SSO session, or an instance profile). Run:

CLI

autogluon-cloud bootstrap

Python

from autogluon.cloud import bootstrap

bootstrap()

The CloudFormation stack is named ag-cloud-sagemaker by default. Subsequent CloudPredictor calls pick the saved values up automatically.

Note

Review the CloudFormation template before deploying: src/autogluon/cloud/templates/ag_cloud_sagemaker.yaml.

register

Tells AutoGluon-Cloud to use an IAM role and S3 bucket you already have. Use this when your platform team has provisioned them for you and you want to skip CloudFormation.

CLI

autogluon-cloud register \
    --role arn:aws:iam::222222222222:role/MyAutoGluonRole \
    --bucket my-autogluon-bucket \
    --region us-east-1

Python

from autogluon.cloud import register

register(
    role="arn:aws:iam::222222222222:role/MyAutoGluonRole",
    bucket="my-autogluon-bucket",
    region="us-east-1",
)

register makes no AWS calls — it only persists the values to ~/.autogluon/cloud.yaml. The IAM role must trust sagemaker.amazonaws.com and have permissions equivalent to AWS’s AmazonSageMakerFullAccess managed policy plus read/write access to your bucket.

status

Verifies that the saved IAM role, S3 bucket, and (if applicable) CloudFormation stack still exist and are accessible.

CLI

autogluon-cloud status

Python

from autogluon.cloud import status

reports = status()

ok means the resource exists; ok (unverified ...) means the caller lacks the IAM permission to verify (the resource is probably fine, but status couldn’t confirm).

teardown

Deletes the CloudFormation stacks created by bootstrap and removes ~/.autogluon/cloud.yaml. Backends added via register only have their config entry removed — your existing role and bucket are left untouched.

CLI

autogluon-cloud teardown

Python

from autogluon.cloud import teardown

teardown()

Warning

CloudFormation refuses to delete non-empty S3 buckets. If your bucket holds training artifacts you want to discard, empty it first with aws s3 rm s3://<bucket> --recursive.

Where the config lives

bootstrap and register both write to ~/.autogluon/cloud.yaml. The file is keyed by backend, so you can have separate entries for different backends side by side. Override the directory with the AG_CONFIG_DIR environment variable.