Set Up AutoGluon-Cloud on AWS

First, install the autogluon.cloud package:

pip install autogluon.cloud

AutoGluon-Cloud runs training and inference on Amazon SageMaker on your behalf. 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 data and store trained models

Attention

SageMaker compute and S3 storage are billed to your AWS account. AutoGluon-Cloud is a free wrapper, but it’s your responsibility to monitor usage and delete endpoints when no longer needed.

There are three ways to supply these resources — if you’re unsure, start with option 1.

1. Create new resources with bootstrap()

Run this if you don’t yet have an IAM role and S3 bucket set up for SageMaker. The role and bucket are provisioned on your account from a CloudFormation template and saved under ~/.autogluon/cloud.yaml for future calls.

Python

from autogluon.cloud import bootstrap

bootstrap()

CLI

autogluon-cloud bootstrap

2. Use existing resources with register()

Run this if you already have an IAM role and S3 bucket that you want to use with AutoGluon-Cloud. The values are saved under ~/.autogluon/cloud.yaml for future calls.

Python

from autogluon.cloud import register

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

CLI

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

The role must trust the sagemaker.amazonaws.com principal and grant the permissions AutoGluon-Cloud needs to run SageMaker jobs plus read/write access to your bucket — for example, a SageMaker execution role. For the exact set of permissions, see the CloudFormation template that bootstrap() uses. The region where the jobs are executed must match the bucket’s region.

3. Pass resources on each call

Skip the saved config entirely and provide the role and bucket every time you create a CloudPredictor or FoundationModel.

from autogluon.cloud import SageMakerConfig, TabularCloudPredictor

predictor = TabularCloudPredictor(
    cloud_output_path="s3://my-autogluon-bucket/output",
    backend=SageMakerConfig(
        role_arn="arn:aws:iam::222222222222:role/MyAutoGluonRole",
        region="us-east-1",
    ),
)

Useful for one-off scripts or when you need different roles and buckets per call. The same role and bucket requirements as option 2 apply.

Share backend settings across workflows

SageMakerConfig works with both cloud predictors and foundation models. It holds the region, execution role, VPC, encryption keys, and resource tags. You can reuse it across objects; each object gets its own backend, jobs, and endpoint state.

from autogluon.cloud import SageMakerConfig, TabularCloudPredictor, TimeSeriesFoundationModel

backend = SageMakerConfig(
    region="us-east-1",
    role_arn="arn:aws:iam::222222222222:role/MyAutoGluonRole",
    vpc_config={"subnets": ["subnet-..."], "security_group_ids": ["sg-..."]},
    output_kms_key="arn:aws:kms:us-east-1:222222222222:key/...",
    tags={"team": "forecasting"},
)

predictor = TabularCloudPredictor(
    backend=backend,
    cloud_output_path="s3://my-autogluon-bucket/training",
)
model = TimeSeriesFoundationModel(
    "chronos-2",
    backend=backend,
    cloud_output_path="s3://my-autogluon-bucket/inference",
)

The role and region you set explicitly take precedence over the saved configuration. Leaving them unset uses the existing saved-config and AWS-identity fallbacks. backend="sagemaker" is shorthand for backend=SageMakerConfig().

output_kms_key encrypts training artifacts, batch transform outputs, and repacked or cached model artifacts in S3. volume_kms_key separately controls training, batch transform, and realtime endpoint storage encryption; leave it unset for instances with local NVMe storage. Resource sizes, container environment variables, spot training, and serverless settings remain arguments to the individual fit(), predict(), and deploy() calls.

Advanced provider settings

Use backend_overrides for SageMaker request fields without a named argument. It maps request names (the boto3 SageMaker client methods) to request fields in the PascalCase format of the SageMaker API and boto3:

predictions = model.predict(
    data,
    prediction_length=24,
    backend_overrides={
        "create_training_job": {
            "RetryStrategy": {"MaximumRetryAttempts": 2},
        },
    },
)

Foundation-model predictions and predictor training use create_training_job. Predictor batch transform uses create_model and create_transform_job. Deployment accepts create_model, production_variant, create_endpoint_config, and create_endpoint.

Only requests used by the operation are accepted. Nested dictionaries merge recursively over the generated request; other values, including lists, replace the generated value. Overrides take precedence over backend settings and named arguments.

Managing the saved config

Once bootstrap() or register() has written to ~/.autogluon/cloud.yaml, you may want to check that the role and bucket are still healthy before a long training run, or clean everything up when you’re done with AutoGluon-Cloud. Two helper commands cover both:

• status() checks that the saved role and bucket still exist and are accessible — handy after IAM or S3 changes.

• teardown() deletes the CloudFormation stack created by bootstrap() and clears the saved config. Resources registered via register() are left untouched, since you own them.

The config path can be overridden with the AG_CONFIG_DIR environment variable if you’d rather keep it somewhere other than ~/.autogluon/.