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⚠️ BentoCTL project has been deprecated

Plese see the latest BentoML documentation on OCI-container based deployment workflow: https://docs.bentoml.com/

🚀 Fast model deployment on any cloud

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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.

Highlights

  • Framework-agnostic model deployment for Tensorflow, PyTorch, XGBoost, Scikit-Learn, ONNX, and many more via BentoML: the unified model serving framework.
  • Simplify the deployment lifecycle of deploy, update, delete, and rollback.
  • Take full advantage of BentoML's performance optimizations and cloud platform features out-of-the-box.
  • Tailor bentoctl to your DevOps needs by customizing deployment operator and Terraform templates.

Getting Started

Supported Platforms:

Community

Contributing

There are many ways to contribute to the project:

  • Create and share new operators. Use deployment operator template to get started.
  • If you have any feedback on the project, share it with the community in Github Discussions under the BentoML repo.
  • Report issues you're facing and "Thumbs up" on issues and feature requests that are relevant to you.
  • Investigate bugs and reviewing other developer's pull requests.

Licence

Elastic License 2.0 (ELv2)