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title Router Examples

For quick start instructions, see the Router README. This document provides further examples for using the Dynamo Router, including Python API usage, Kubernetes deployments, and custom routing patterns.

Table of Contents

Using KvRouter Python API

Instead of launching the KV Router via command line, you can create a KvRouter object directly in Python. This allows per-request routing configuration overrides.

Warning

Multiple Routers in Same Process: If you need to run multiple KvRouter instances for fault tolerance or load distribution, you must launch them in separate processes (e.g., using python -m dynamo.frontend with different ports). Creating multiple KvRouter objects in the same Python process is not supported - they share the same cancellation token from the component's primary lease, so dropping one router will cancel all routers in that process. For in-process routing, use a single KvRouter instance.

Methods

The KvRouter provides the following methods:

  • generate(token_ids, model, ...): Route and execute a request, returning an async stream of responses. Automatically handles worker selection, state tracking, and lifecycle management.

  • best_worker(token_ids, router_config_override=None, request_id=None): Query which worker would be selected for given tokens. Returns (worker_id, dp_rank, overlap_blocks).

    • Without request_id: Query-only, doesn't update router state
    • With request_id: Updates router state to track the request. Note: If used with request_id, you must call mark_prefill_complete() and free() at the appropriate lifecycle points to maintain accurate load tracking

  • get_potential_loads(token_ids): Get detailed load information for all workers, including potential prefill tokens and active decode blocks. Returns a list of load dictionaries.

  • mark_prefill_complete(request_id): Signal that a request has completed its prefill phase. Only used for manual lifecycle management when using best_worker() for manual routing instead of generate().

  • free(request_id): Signal that a request has completed and its resources should be released. Only used for manual lifecycle management when using best_worker() for manual routing instead of generate().

  • dump_events(): Dump all KV cache events from the router's indexer as a JSON string. Useful for debugging and analysis.

Setup

First, launch your backend engines:

python -m dynamo.vllm --model meta-llama/Llama-2-7b-hf

Example Script

import asyncio
from dynamo.runtime import DistributedRuntime
from dynamo.llm import KvRouter, KvRouterConfig

async def main():
    # Get runtime and create endpoint
    loop = asyncio.get_running_loop()
    runtime = DistributedRuntime(loop, "etcd", "nats")
    endpoint = runtime.endpoint("dynamo.backend.generate")

    # Create KV router
    kv_router_config = KvRouterConfig()
    router = KvRouter(
        endpoint=endpoint,
        block_size=16,
        kv_router_config=kv_router_config
    )

    # Your input tokens
    token_ids = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]

    # Generate with per-request routing override
    stream = await router.generate(
        token_ids=token_ids,
        model="meta-llama/Llama-2-7b-hf",
        stop_conditions={
            "max_tokens": 20,        # Generate exactly 20 tokens
            "ignore_eos": True,      # Don't stop at EOS token
        },
        sampling_options={
            "temperature": 0.7,
            "top_p": 0.9,
        },
        router_config_override={
            "overlap_score_weight": 2.0,    # Prioritize cache hits for this request
            "router_temperature": 0.5,       # Add routing randomness
        }
    )

    # Collect generated tokens
    generated_tokens = []
    async for response in stream:
        if isinstance(response, dict) and "token_ids" in response:
            generated_tokens.extend(response["token_ids"])

    print(f"Generated {len(generated_tokens)} tokens: {generated_tokens}")

if __name__ == "__main__":
    asyncio.run(main())

K8s Examples

For basic Kubernetes deployment with the KV Router, see the Kubernetes Deployment section in the Quick Start guide.

Complete K8s Examples

For A/B Testing and Advanced K8s Setup: See the comprehensive KV Router A/B Benchmarking Guide for step-by-step instructions on deploying, configuring, and benchmarking the KV router in Kubernetes.

Example with Advanced Configuration

apiVersion: nvidia.com/v1alpha1
kind: DynamoGraphDeployment
metadata:
  name: my-deployment
spec:
  services:
    Frontend:
      dynamoNamespace: my-namespace
      componentType: frontend
      replicas: 1
      envs:
        - name: DYN_ROUTER_MODE
          value: kv
        - name: DYN_ROUTER_TEMPERATURE
          value: "0.5"  # Add some randomness to prevent worker saturation
        - name: DYN_ROUTER_KV_OVERLAP_SCORE_WEIGHT
          value: "1.5"  # Prioritize TTFT over ITL
        - name: DYN_KV_CACHE_BLOCK_SIZE
          value: "16"
      extraPodSpec:
        mainContainer:
          image: nvcr.io/nvidia/ai-dynamo/vllm-runtime:0.6.0

Alternative: Using Command Args in K8s

You can also pass CLI arguments directly in the container command:

extraPodSpec:
  mainContainer:
    image: nvcr.io/nvidia/ai-dynamo/vllm-runtime:0.6.0
    command:
      - /bin/sh
      - -c
    args:
      - "python3 -m dynamo.frontend --router-mode kv --router-temperature 0.5 --http-port 8000"

Recommendation: Use environment variables for easier configuration management and consistency with Dynamo's K8s patterns.

Routing Patterns

The KvRouter supports multiple usage patterns depending on your control requirements:

1. Automatic Routing (Recommended)

Call generate() directly and let the router handle everything:

stream = await router.generate(token_ids=tokens, model="model-name")
  • Best for: Most use cases
  • Router automatically: Selects best worker, updates state, routes request, tracks lifecycle

2. Manual State Management (Advanced)

Use best_worker(request_id=...) to select and track, then manage the request yourself:

worker_id, _dp_rank, overlap = await router.best_worker(tokens, request_id="req-123")
response = await client.generate(tokens, request_id="req-123")
# await anext(response)  # Get first token
await router.mark_prefill_complete("req-123")  # After first token
# async for _ in response:  # Continue generating
#     ...
await router.free("req-123")  # After completion
  • Best for: Custom request handling with router state tracking
  • Requires: Calling mark_prefill_complete() and free() at correct lifecycle points
  • Caution: Incorrect lifecycle management degrades load balancing accuracy

3. Hierarchical Router Probing

Query without state updates, then route through a chosen router:

# Probe multiple routers without updating state
worker_id_1, dp_rank, overlap_1 = await router_1.best_worker(tokens)  # No request_id
worker_id_2, dp_rank, overlap_2 = await router_2.best_worker(tokens)

# Pick the best router and corresponding worker based on results
if overlap_1 > overlap_2:
    chosen_router, chosen_worker = router_1, worker_id_1
else:
    chosen_router, chosen_worker = router_2, worker_id_2
stream = await chosen_router.generate(tokens, model="model-name", worker_id=chosen_worker)
  • Best for: Multi-tier deployments (e.g., Envoy Gateway routing to multiple router groups)
  • Advantage: Query multiple routers before committing to one

4. Custom Load-Based Routing

Use get_potential_loads() to implement custom routing logic:

loads = await router.get_potential_loads(tokens)
# Apply custom logic (e.g., weighted scoring, constraints)
best_worker = min(loads, key=lambda x: custom_cost_fn(x))
stream = await router.generate(tokens, model="model-name", worker_id=best_worker['worker_id'])
  • Best for: Custom optimization strategies beyond the built-in cost function
  • Advantage: Full control over worker selection logic
  • See also: Detailed example below in "Custom Routing Example: Minimizing TTFT"

All patterns support router_config_override to adjust routing behavior per-request without recreating the router.

Custom Routing Example: Minimizing TTFT

Here's an example of using get_potential_loads() to implement custom routing that minimizes Time To First Token (TTFT) by selecting the worker with the least prefill work:

import asyncio
from dynamo.runtime import DistributedRuntime
from dynamo.llm import KvRouter, KvRouterConfig

async def minimize_ttft_routing():
    # Setup router
    loop = asyncio.get_running_loop()
    runtime = DistributedRuntime(loop, "etcd", "nats")
    endpoint = runtime.endpoint("dynamo.backend.generate")

    router = KvRouter(
        endpoint=endpoint,
        block_size=16,
        kv_router_config=KvRouterConfig()
    )

    # Your input tokens
    token_ids = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]

    # Get potential loads for all workers
    potential_loads = await router.get_potential_loads(token_ids)

    # Find worker with minimum prefill tokens (best for TTFT)
    best_worker = min(potential_loads, key=lambda x: x['potential_prefill_tokens'])

    print(f"Worker loads: {potential_loads}")
    print(f"Selected worker {best_worker['worker_id']} with {best_worker['potential_prefill_tokens']} prefill tokens")

    # Route directly to the selected worker
    stream = await router.generate(
        token_ids=token_ids,
        model="meta-llama/Llama-2-7b-hf",
        worker_id=best_worker['worker_id'],  # Force routing to optimal worker
        stop_conditions={"max_tokens": 20}
    )

    # Process response
    async for response in stream:
        if isinstance(response, dict) and "token_ids" in response:
            print(f"Generated tokens: {response['token_ids']}")

if __name__ == "__main__":
    asyncio.run(minimize_ttft_routing())

This approach gives you complete control over routing decisions, allowing you to optimize for different metrics based on your specific requirements. As some examples:

  • Minimize TTFT: Select worker with lowest potential_prefill_tokens
  • Maximize cache reuse: Use best_worker() which considers both prefill and decode loads
  • Balance load: Consider both potential_prefill_tokens and potential_decode_blocks together

See Router Design for architecture details and the cost function algorithm.

KV Event Publishing for Custom Engines

For full documentation on implementing KV event publishing for custom inference engines, see the dedicated KV Event Publishing for Custom Engines guide. It covers:

  • Direct publishing: Call publish_stored() / publish_removed() to push events over the Dynamo event plane
  • ZMQ relay: For engines that emit raw KV events over ZMQ (like SGLang and vLLM), the same KvEventPublisher subscribes to the ZMQ socket and relays events automatically
  • API reference, event structure, ZMQ wire format, and best practices

Global Router (Hierarchical Routing)

For deployments with multiple worker pools, the Global Router enables hierarchical routing by sitting between the frontend and local routers. It selects the appropriate pool for each request based on configurable policies, supporting disaggregated topologies where pools are tuned for different workload characteristics.

See Also