Set Up AutoGluon-Cloud on AWS

AutoGluon-Cloud needs two AWS resources to operate:

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

• An S3 bucket where training artifacts and trained models are stored.

The fastest way to set both up is the autogluon-cloud bootstrap command shipped with the package. If you already have a role and bucket, use register instead. This page walks through both paths and the day-2 commands (status, teardown).

Install

pip install -U autogluon.cloud

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

Quickstart: bootstrap

If you have AWS credentials configured (via aws configure, AWS_* env vars, SSO, or an instance profile), run:

CLI

autogluon-cloud bootstrap

Python

from autogluon.cloud import bootstrap

bootstrap()

This deploys a CloudFormation stack (ag-cloud-sagemaker by default), creates the IAM role and S3 bucket, and saves both to ~/.autogluon/cloud.yaml. Subsequent CloudPredictor calls pick the saved values up automatically.

Note

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

Already have a role and bucket? Use register

If your platform team has provisioned an IAM role and S3 bucket for you, skip CloudFormation entirely and just tell AutoGluon-Cloud about them:

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.

Check your setup: status

Verify the saved resources still exist and are accessible:

CLI

autogluon-cloud status

Python

from autogluon.cloud import status

reports = status()

Each backend’s bucket, role, and (if applicable) CloudFormation stack are checked. 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).

Tear down: teardown

When you’re done with AutoGluon-Cloud and want to remove everything it created:

CLI

autogluon-cloud teardown

Python

from autogluon.cloud import teardown

teardown()

This deletes the CloudFormation stack(s) created by bootstrap and removes ~/.autogluon/cloud.yaml. Backends added via register (no stack) only have their config entry removed — your existing role and bucket are left untouched.

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.