Train

AutoGluon-Cloud lets you train and deploy state-of-the-art ML models in the cloud in a few lines of code. Run AutoGluon on Amazon SageMaker without worrying about infrastructure, dependencies, or a heavy local ML environment. It supports two workflows:

• Train your own predictor — the same fit → deploy → predict workflow as local AutoGluon, with all the heavy lifting offloaded to SageMaker.
• Run pretrained foundation models — deploy state-of-the-art pretrained models like Chronos-2 for zero-shot inference, with no training required.

💾 Installation & setup

pip install autogluon.cloud

Then provision the IAM role and S3 bucket AutoGluon-Cloud needs to run on AWS:

from autogluon.cloud import bootstrap

bootstrap()

See the Setup tutorial for the full walkthrough, including how to register an existing role and bucket instead.

⚙️ Train your own model

Train an AutoGluon predictor on your data and serve it from a SageMaker endpoint — same API as local AutoGluon, all heavy lifting on AWS. Full walkthrough: tabular, time series.

from autogluon.cloud import TabularCloudPredictor

# `train_data` and `test_data` can be a local path, S3 URL, or pandas DataFrame
train_data = "https://autogluon.s3.amazonaws.com/datasets/Inc/train.csv"
test_data = "https://autogluon.s3.amazonaws.com/datasets/Inc/test.csv"

# Train
cloud_predictor = TabularCloudPredictor()
cloud_predictor.fit(
    train_data=train_data,
    predictor_init_args={"label": "class"},  # passed to TabularPredictor()
    predictor_fit_args={"time_limit": 120},  # passed to TabularPredictor.fit()
)

# Real-time inference endpoint
cloud_predictor.deploy()
result = cloud_predictor.predict_real_time(test_data)
cloud_predictor.cleanup_deployment()

# Batch prediction
result = cloud_predictor.predict(test_data)

🚀 Run a pretrained foundation model

Skip training entirely — deploy a pretrained model like Chronos-2 to SageMaker and get zero-shot predictions out of the box. Full walkthrough: time series.

from autogluon.cloud import TimeSeriesFoundationModel

# `data` can be a local path, S3 URL, or pandas DataFrame
data = "https://autogluon.s3.amazonaws.com/datasets/timeseries/m4_hourly_tiny/train.csv"

model = TimeSeriesFoundationModel("chronos-2")

# Batch prediction — no training required
predictions = model.predict(
    data=data,
    target="target",
    prediction_length=24,
)

# Real-time inference endpoint
endpoint = model.deploy()
predictions = endpoint.predict(
    data=data,
    target="target",
    prediction_length=24,
)
endpoint.delete_endpoint()