Principle:Togethercomputer Together python Endpoint Management
| Knowledge Sources | |
|---|---|
| Domains | Infrastructure, Model_Deployment |
| Last Updated | 2026-02-15 16:00 GMT |
Overview
Principle for managing the lifecycle of dedicated inference endpoints, including creation, scaling, monitoring, and teardown of model deployments.
Description
Endpoint Management covers the full lifecycle of deploying models as dedicated inference endpoints on a cloud platform. This includes selecting appropriate hardware (GPU type and count), configuring autoscaling policies (min/max replicas), managing endpoint state transitions (start, stop, delete), and querying infrastructure availability. The principle is infrastructure-agnostic in theory but maps to specific cloud GPU deployment patterns.
Usage
Apply this principle when you need to deploy a model for production or development inference with dedicated resources, rather than using shared serverless endpoints. This is the right approach when you need guaranteed capacity, custom scaling behavior, or specific hardware requirements.
Theoretical Basis
Endpoint management follows a standard resource lifecycle pattern:
Pseudo-code Logic:
# Abstract endpoint lifecycle
endpoint = create_endpoint(model, hardware, scaling_config)
wait_until(endpoint.state == "STARTED")
# Use endpoint for inference...
# Scale as needed
update_endpoint(endpoint, new_scaling_config)
# Cleanup
stop_endpoint(endpoint)
delete_endpoint(endpoint)
Key considerations:
- Hardware Selection: Match GPU type and count to model requirements
- Autoscaling: Configure min_replicas (cost floor) and max_replicas (capacity ceiling)
- State Management: Endpoints transition through PENDING → STARTING → STARTED → STOPPING → STOPPED
- Availability Zones: Deploy in specific regions for latency or compliance requirements