bentoctl 0.3.3

Fast model deployment with BentoML on cloud platforms.

bentoctl

Fast model deployment with BentoML on cloud platforms

bentoctl is a CLI tool for deploying your machine-learning models to any cloud platforms. It 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 :)

Features:

• Supports major cloud providers: AWS, Azure, Google Cloud, and more.
• Easy to deploy, update and reproduce model deployments.
• First class integration with Terraform.
• Optimized for CI/CD workflow.
• Extensible with custom operators.
• High performance serving powered by BentoML

Supported Platforms:

• AWS Lambda
• AWS SageMaker
• AWS EC2
• Google Cloud Run
• Google Compute Engine
• Azure Container Instances
• Heroku
• Looking for Kubernetes? Try out Yatai: Model deployment at scale on Kubernetes.
• Customize deploy target by creating bentoctl plugin from the deployment operator template.

Upcoming:

• Azure Functions
• Knative

Install bentoctl

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.

Next steps

• 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.

Community

• 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.

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.