literegistry 1.0.23

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

By default this starts Redis inside Apptainer using the official Redis image redis_7-alpine.sif, pulled from docker://redis:7-alpine. To use a host Redis binary instead:

literegistry redis --runtime local --port 6379

To keep Redis attached to the current terminal/process, run it in foreground mode:

literegistry redis --runtime local --foreground --port 6379

Redis startup prints a machine-readable registry URL that includes the selected port:

REDIS_URL=redis://hostname: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

To launch vLLM inside Apptainer, choose the Apptainer runtime and provide any binds or container environment variables. The default vLLM Apptainer image is vllm-openai_latest-cu129-ubuntu2404.sif, pulled from docker://vllm/vllm-openai:latest-cu129-ubuntu2404. Apptainer launches also bind $HOME plus the shell-derived Hugging Face cache paths by default. If HF_HOME, HF_CACHE, HUGGINGFACE_HUB_CACHE, HF_HUB_CACHE, TRANSFORMERS_CACHE, or VLLM_CACHE_ROOT are set in the launching shell, those values are passed into the container; otherwise LiteRegistry falls back to cache paths under $HOME/.cache.

literegistry vllm \
  --runtime apptainer \
  --model /mmfs1/gscratch/ark/graf/judges-that-code/thinker/tinker-sft-demo_vllm_model \
  --registry redis://login-node:6379 \
  --port 7248 \
  --tensor-parallel-size 1 \
  --dtype float16 \
  --max-model-len 4096 \
  --trust-remote-code \
  --language-model-only \
  --safetensors-load-strategy prefetch

For SGLang, the default Apptainer image is sglang_latest.sif, pulled from the official docker://lmsysorg/sglang:latest image. It uses the same shared Hugging Face cache defaults.

3. Start Gateway Server

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

Start Python Code Executor

LiteRegistry can also register a stateless Python code execution service. The service registers itself under model_path="python" so the gateway can route POST /python requests to available executor workers.

literegistry code --registry redis://klone-login01.hyak.local:6379

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

# Execute Python through the gateway
curl -X POST http://localhost:8080/python \
  -H "Content-Type: application/json" \
  -d '{"code": "print(2 + 2)", "max_runtime": 1.0}'

# Execute Python with a context payload
curl -X POST http://localhost:8080/python \
  -H "Content-Type: application/json" \
  -d '{"code": "data = json.loads(context)\nprint(data[\"name\"])\nprint(data[\"score\"] + 1)", "context_payload": "{\"name\": \"alice\", \"score\": 41}", "max_runtime": 3}'

The gateway automatically routes requests to the appropriate model server based on the model field. For code execution, it routes /python requests to services registered as python.

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