AutoGluon-Cloud lets you train and deploy state-of-the-art ML models for classification, regression, and time series forecasting on Amazon SageMaker. All it takes is a few lines of code; AutoGluon-Cloud handles the infrastructure, dependencies, and glue code for you.
💡 Why AutoGluon-Cloud?
- Works like local AutoGluon. Pass in DataFrames, get predictions back — as convenient as working locally, with the compute handled by AWS.
- No boilerplate. No training scripts, inference handlers, or serialization code to write and maintain.
- Official AWS containers. Everything runs in the AutoGluon Deep Learning Containers, maintained and security-patched by AWS.
- Sensible defaults, fully configurable. Under the hood it's just SageMaker running in your AWS account, so you stay in full control.
💾 Installation
pip install autogluon.cloud
autogluon-cloud bootstrap # one-time setup for IAM role and S3 bucket
See the Setup tutorial for more details.
🚀 Foundation models
Zero-shot forecasts with a pretrained model like Chronos-2 — no training required. Full walkthrough.
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
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()
⚙️ Train your own predictor
Train an AutoGluon predictor on your data and serve it from SageMaker. Full walkthrough: time series, tabular.
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()
)
# Batch prediction
result = cloud_predictor.predict(test_data)
# Real-time inference endpoint
endpoint = cloud_predictor.deploy()
result = endpoint.predict(test_data)
endpoint.delete_endpoint()
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Release files for autogluon.cloud 0.7.0
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| autogluon_cloud-0.7.0-py3-none-any.whl | Python 3 | none | any | Details |
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