dynamo-router-starter

Dynamo Router Starter

Purpose

Make Dynamo routing feel easy by getting a baseline router mode running, enabling KV-aware routing when appropriate, and proving the endpoint works. Keep the user focused on exact commands and success signals, not router internals.

Prerequisites

• Python 3.10+ with the dynamo package importable (python3 -m dynamo.frontend --help works).
• For Kubernetes runs: kubectl configured with access to the target namespace and a deployed Dynamo recipe.
• Network reachability to the frontend service (port-forward or direct).
• A model already loaded into at least one worker (/v1/models returns at least one entry).

Required Inputs

Collect or infer:

• local Python/CLI or Kubernetes recipe path
• desired mode: round-robin, kv, least-loaded, device-aware-weighted, direct, or random
• frontend port or Kubernetes frontend service
• whether workers publish KV events; if not, use approximate KV mode
• model name for smoke requests, if /v1/models cannot discover it

Instructions

1. Establish A Baseline

For local bring-up with already registered workers:

python3 -m dynamo.frontend --router-mode round-robin --http-port 8000

For Kubernetes, inspect the selected recipe deploy.yaml and locate the frontend service. If the recipe is not already deployed, use dynamo-recipe-runner first.

2. Enable KV Routing

For local frontend:

python3 -m dynamo.frontend --router-mode kv --http-port 8000

For Kubernetes, patch only the frontend service env:

If backend workers are not publishing KV cache events, set approximate mode instead of leaving the router waiting for events:

3. Smoke Test

After port-forwarding the frontend service or starting local frontend, run:

This must verify /v1/models and, when a model is discoverable, one /v1/chat/completions request.

4. Compare Modes Carefully

When comparing round-robin vs KV routing:

• use the same model, workers, prompt set, concurrency, and sampling settings
• send repeated-prefix prompts if demonstrating KV reuse
• label the result as a smoke comparison unless enough benchmark samples were collected
• do not claim throughput improvement from a single chat request

If the endpoint is unhealthy or workers are missing, switch to dynamo-troubleshoot.

Available Scripts

Invoke via the agentskills.io run_script() protocol:

Examples

Local KV-routed frontend on port 8000, then smoke-test it:

Kubernetes-deployed frontend reachable via port-forward:

Equivalent through the agent protocol:

Output Contract

Return:

• mode selected and why
• local command or Kubernetes env patch
• frontend service or URL
• smoke-test result
• any limitation, such as approximate KV mode or missing worker KV events
• next command to run for a fuller comparison

Limitations

• Smoke test is one chat completion; it is not a benchmark. Use dynamo-benchmark for throughput/latency numbers.
• KV-aware mode without worker KV-event publication degrades to approximate mode; this skill flags but does not fix the underlying worker config.
• Mode comparisons require matched workloads; cross-mode latency claims need separate benchmark runs.

Troubleshooting

Benchmark

See BENCHMARK.md for the NVCARPS-EVAL performance report (auto-generated by the NVSkills CI pipeline). To refresh, re-run /nvskills-ci on an upstream PR touching this skill.

References

• Read references/router-modes.md for the compact mode/env map.
• Use scripts/check_router_health.py for endpoint smoke tests.