β οΈ BentoCTL project has been deprecated
Plese see the latest BentoML documentation on OCI-container based deployment workflow: https://docs.bentoml.com/
π Fast model deployment on any cloud
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.
π Join our Slack community today!
β¨ 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)