Principle:Bentoml BentoML Cloud Deployment Creation
Overview
Cloud Deployment Creation is the principle of transforming a built BentoML service artifact into a running, accessible cloud endpoint through a single API call that abstracts away all infrastructure provisioning concerns.
Concept
Deploying a BentoML service to a managed cloud inference platform should be as simple as specifying what to deploy and how it should be scaled. The platform handles all underlying infrastructure concerns including container orchestration, networking, GPU allocation, and health monitoring.
Theory
Cloud deployment abstracts away infrastructure provisioning, auto-scaling, and load balancing. A single API call transforms a built Bento artifact into a running, accessible endpoint with GPU support, monitoring, and lifecycle management. This abstraction provides:
- One-command deployment - A single function call or CLI command creates a fully operational inference endpoint
- Automatic infrastructure - The platform provisions compute resources, configures networking, sets up load balancers, and allocates GPUs as needed
- Built-in auto-scaling - Horizontal scaling is managed automatically based on traffic patterns and configured scaling policies
- Health monitoring - The platform monitors service health, restarts failed instances, and provides observability dashboards
- Rolling updates - New versions can be deployed with zero-downtime rolling update strategies
Deployment Flow
The deployment creation process follows these steps:
- Bento resolution - The specified Bento tag is resolved to a built artifact (local or already pushed to BentoCloud)
- Bento push - If the Bento exists only locally, it is automatically pushed to the BentoCloud registry
- Configuration validation - Deployment parameters are validated against the target cluster's capabilities
- Resource provisioning - The platform allocates compute instances, GPUs, and networking resources
- Service startup - The Bento is loaded and the service begins accepting traffic
- Endpoint exposure - A public or private URL is assigned to the deployment
Deployment Object
Upon successful creation, a Deployment object is returned containing:
- name - The unique deployment identifier
- admin_console - URL to the BentoCloud management dashboard for this deployment
- cluster - The cluster where the deployment is running
- status - Current deployment status (e.g., deploying, running, failed)
Metadata
| Property | Value |
|---|---|
| Principle | Cloud Deployment Creation |
| Domain | ML_Serving, Cloud_Deployment |
| Workflow | BentoCloud_Deployment |
| Related Concepts | Container Orchestration, Auto-scaling, Load Balancing, GPU Scheduling |
| Implementation | Implementation:Bentoml_BentoML_Deployment_Create |