lumen-resources 0.4.1

Unified model resource management for Lumen ML services

Lumen Resources

Lightweight tooling for shipping Lumen ML services. This package centralizes how models are described, validated, downloaded, and cached so every service (CLIP, face, etc.) follows the same playbook—whether weights live on Hugging Face, ModelScope, or a private registry.

Why use it?

• Single source of truth – YAML configs describing deployments, devices, runtimes, and model aliases.
• Schema-backed validation – JSON Schema plus Pydantic to catch errors before runtime.
• Cross-platform downloads – Intelligent routing between Hugging Face and ModelScope with caching/resume support.
• CLI + Python API – Automate in CI or embed in service bootstraps.
• Result schemas – Typed response validators (EmbeddingV1, FaceV1, LabelsV1) for downstream services.

Installation

# project install
pip install "lumen-resources @ git+https://github.com/EdwinZhanCN/Lumen.git@main#subdirectory=lumen-resources"

# dev install
git clone https://github.com/EdwinZhanCN/Lumen.git
cd Lumen/lumen-resources
pip install -e ".[dev,config]"

Optional extras depending on your targets:

pip install huggingface_hub
pip install modelscope
pip install torch torchvision
pip install onnxruntime

Usage

CLI

# download everything defined in config.yaml
lumen-resources download config.yaml

# strict config validation
lumen-resources validate config.yaml

# validate a model_info.json
lumen-resources validate-model-info path/to/model_info.json

# inspect cache contents (defaults to ~/.lumen/)
lumen-resources list ~/.lumen/

Python API

from lumen_resources import (
    load_and_validate_config,
    Downloader,
    load_and_validate_model_info,
    EmbeddingV1,
)

config = load_and_validate_config("config.yaml")
downloader = Downloader(config, verbose=True)
results = downloader.download_all(force=False)

model_info = load_and_validate_model_info("model_info.json")

Configuration essentials

metadata:
  region: "other"      # or "cn" to prefer ModelScope
  cache_dir: "~/.lumen/models"

deployment:
  mode: "single"       # or "hub"
  service: "clip"

services:
  clip:
    enabled: true
    package: "lumen_clip"
    backend_settings:
      device: "cuda"
      batch_size: 16
      onnx_providers: ["CUDAExecutionProvider", "CPUExecutionProvider"]
    models:
      default:
        model: "ViT-B-32"
        runtime: "torch"
      fp16:
        model: "ViT-B-32"
        runtime: "onnx"

• metadata.region decides whether downloads prefer ModelScope or Hugging Face.
• backend_settings lets you declare execution providers, batch sizes, devices, etc.
• Each entry in models becomes a cache namespace (clip/default, clip/fp16, …).

Reference

• Source: src/lumen_resources/
  • lumen_config.py – Typed config models
  • downloader.py – Platform abstraction + caching
  • cli.py – Command entrypoint
  • result_schemas/ – Response validators
• Docs: https://doc.lumilio.org
• Issues & support: open a ticket in the main Lumen monorepo.