bentoctl is a CLI tool for deploying your machine-learning models to any cloud platforms and serving predictions via REST APIs. It is built on top of BentoML: the unified model serving framework, and makes it easy to bring any BentoML packaged model to production.
👉 Pop into our Slack community! We're happy to help with any issue you face or even just to meet you and hear what you're working on :)
- Supports major cloud providers: AWS, Azure, Google Cloud, and more.
- Easy to deploy, update and reproduce model deployments.
- Optimized for CI/CD workflow.
- Extensible with custom operators.
- High performance serving powered by BentoML
- AWS EC2
- AWS Lambda
- AWS SageMaker
- Azure Functions
- Azure Container Instances
- Google Cloud Run
- Google Compute Engine
- Heroku
- Knative (WIP)
- Looking for Kubernetes? Try out Yatai: Model deployment at scale on Kubernetes.
- Customize deploy target by creating bentoctl plugin from the deployment operator template.
pip install --pre bentoctl| 💡 bentoctl is in pre-release stage, use the --pre to install the pre-release version.
- Quickstart Guide walks through a series of steps to deploy a bento to AWS Lambda as API server.
- Core Concepts explains the core concepts in bentoctl.
- Operator List lists official operators and their current status.
- To report a bug or suggest a feature request, use GitHub Issues.
- For other discussions, use Github Discussions under the BentoML repo
- To receive release announcements and get support, join us on Slack.
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.