literegistry 1.0.3

Package for implementing service discovery in a really lite way.

Lightweight service registry and discovery system for distributed model inference clusters. Built for deployments on HPC environments with load balancing and automatic failover.

Installation

pip install literegistry

Quick Start

Complete workflow for deploying distributed model inference:

1. Start Redis Server

literegistry redis --port 6379

2. Launch vLLM/SGLang Instances (supports all standard vLLM/SGLang arguments)

literegistry vllm \
  --model "meta-llama/Llama-3.1-8B-Instruct" \
  --registry redis://login-node:6379 \
  --tensor-parallel-size 4

3. Start Gateway Server

literegistry gateway \
  --registry redis://login-node:6379 \
  --host 0.0.0.0 \
  --port 8080

4. Interact with Gateway

The gateway provides OpenAI-compatible HTTP endpoints that work with existing tools:

# Send completion request
curl -X POST http://localhost:8080/v1/completions \
  -H "Content-Type: application/json" \
  -d '{"model": "meta-llama/Llama-3.1-8B-Instruct", "prompt": "Hello"}'

# List all available models
curl http://localhost:8080/v1/models

# Check gateway health
curl http://localhost:8080/health

The gateway automatically routes requests to the appropriate model server based on the model field.

5. Monitor Cluster

# Summary view
literegistry summary --registry redis://login-node:6379

Using the Python API

Writting new servers

from literegistry import RegistryClient, get_kvstore
import asyncio

async def main():
    # Auto-detect backend (redis:// or file path)
    store = get_kvstore("redis://localhost:6379")
    client = RegistryClient(store, service_type="model_path")
    
    # Register a server
    await client.register(
        port=8000,
        metadata={"model_path": "meta-llama/Llama-3.1-8B-Instruct"}
    )
    
    # List available models
    models = await client.models()
    print(models)

asyncio.run(main())

HTTP Client with Automatic Failover

from literegistry import RegistryHTTPClient

async with RegistryHTTPClient(client, "meta-llama/Llama-3.1-8B-Instruct") as http_client:
    result, _ = await http_client.request_with_rotation(
        "v1/completions",
        {"prompt": "Hello"},
        timeout=30,
        max_retries=3
    )

Storage Backends

LiteRegistry supports different backends depending on your deployment:

FileSystem - For single-node or shared filesystem environments

from literegistry import FileSystemKVStore
store = FileSystemKVStore("registry_data")

Use when: Running on a single machine or when all nodes share a filesystem (common in HPC clusters with NFS). Note: Can bottleneck with high concurrency.

Redis - For distributed multi-node clusters

from literegistry import RedisKVStore
store = RedisKVStore("redis://localhost:6379")

Use when: Running across multiple nodes without shared storage, or need high-concurrency access. Recommended for production HPC deployments.

Citation

If you use LiteRegistry in your research, please cite:

@software{literegistry2025,
  title={literegistry: Lightweight Service Discovery for Distributed Model Inference},
  author={Faria, Gonçalo and Smith, Noah},
  year={2025},
  url={https://github.com/goncalorafaria/literegistry}
}

Contributing

Contributions welcome! Please submit a Pull Request.

License

MIT License - see LICENSE file for details