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bentoctl helps deploy any machine learning models as production-ready API endpoints on the cloud, supporting AWS SageMaker, AWS Lambda, EC2, Google Compute Engine, Azure, Heroku and more.
Join our Slack community today!
Looking deploy your ML service quickly? You can checkout BentoML Cloud for the easiest and fastest way to deploy your bento. It's a full featured, serverless environment with a model repository and built in monitoring and logging.
Users can built custom bentoctl plugin from the deployment operator template to deploy to cloud platforms not yet supported or to internal infrastructure.
If you are looking for deploying with Kubernetes, check out Yatai: Model deployment at scale on Kubernetes.
pip install bentoctl
| bentoctl designed to work with BentoML version 1.0.0 and above. For BentoML 0.13 or below, you can use the
pre-v1.0 branch in the operator repositories and follow the instruction in the README. You can also check out the quickstart guide for 0.13 here.
There are many ways to contribute to the project:
BentoML and bentoctl collects usage data that helps our team to
improve the product. Only bentoctl's CLI commands calls are being reported. We
strip out as much potentially sensitive information as possible, and we will
never collect user code, model data, model names, or stack traces. Here's the
code for usage tracking. You can opt-out of
usage tracking by setting environment variable