Model Management
Three Architectures for Model Management
Architecture 1: Model Registry with Static Analysis
Approach: Analyze models at registration time, store metadata in a local database.
┌─────────────────────────────────────────────────────────────────┐ │ Model Registry │ ├─────────────────────────────────────────────────────────────────┤ │ branes model register model.pt —name “perception_v1” │ │ │ │ │ ▼ │ │ ┌─────────────────────────────────────────────────────────┐ │ │ │ Model Analyzer (static) │ │ │ │ - torch.jit.script() or trace for structure │ │ │ │ - Count params, layers, ops (FLOPs) │ │ │ │ - Detect architecture patterns (CNN, Transformer, etc) │ │ │ │ - Extract input/output shapes │ │ │ └─────────────────────────────────────────────────────────┘ │ │ │ │ │ ▼ │ │ ┌─────────────────────────────────────────────────────────┐ │ │ │ SQLite/JSON Knowledge Base │ │ │ │ { │ │ │ │ “name”: “perception_v1”, │ │ │ │ “path”: “/models/yolo.pt”, │ │ │ │ “params”: 7_200_000, │ │ │ │ “flops”: 8.9e9, │ │ │ │ “architecture”: “yolov8”, │ │ │ │ “input_shape”: [1, 3, 640, 640], │ │ │ │ “layers”: {“Conv2d”: 53, “BatchNorm2d”: 53, …} │ │ │ │ } │ │ │ └─────────────────────────────────────────────────────────┘ │ └─────────────────────────────────────────────────────────────────┘
Pros:
- Simple, no runtime overhead
- Works offline
- Easy to query and filter
Cons:
- Static analysis can miss dynamic behavior
- Requires explicit registration
- Limited reasoning capability
Implementation: ~500 LOC, extends existing ModelAnalyzer agent
Architecture 2: LLM-Augmented Model Understanding
Approach: Use LLM to interpret model structure and generate semantic descriptions.
┌─────────────────────────────────────────────────────────────────┐ │ LLM Model Intelligence │ ├─────────────────────────────────────────────────────────────────┤ │ │ │ ┌──────────────┐ ┌──────────────┐ ┌──────────────┐ │ │ │ Model Loader │───▶│ Introspector │───▶│ LLM Analyst │ │ │ │ │ │ │ │ │ │ │ │ - state_dict │ │ - Graph trace│ │ - Interpret │ │ │ │ - JIT load │ │ - Shape infer│ │ - Classify │ │ │ │ - ONNX load │ │ - Op census │ │ - Recommend │ │ │ └──────────────┘ └──────────────┘ └──────────────┘ │ │ │ │ │ ▼ │ │ ┌─────────────────────────────────────────────────────────┐ │ │ │ Semantic Model Card │ │ │ │ │ │ │ │ “This is a YOLOv8-small object detection model. │ │ │ │ It uses a CSPDarknet backbone with 53 convolutional │ │ │ │ layers and a PANet neck. Suitable for real-time │ │ │ │ detection on edge devices with >2 TOPS compute. │ │ │ │ │ │ │ │ Recommended hardware: Jetson Orin Nano, Hailo-8 │ │ │ │ Expected latency: 15-25ms @ 640x640 input” │ │ │ └─────────────────────────────────────────────────────────┘ │ │ │ │ │ ▼ │ │ ┌─────────────────────────────────────────────────────────┐ │ │ │ Tool: model_query │ │ │ │ User: “What models can run under 20ms on Jetson?” │ │ │ │ LLM: Queries registry, reasons about constraints │ │ │ └─────────────────────────────────────────────────────────┘ │ └─────────────────────────────────────────────────────────────────┘
Pros:
- Rich semantic understanding
- Natural language queries
- Can reason about tradeoffs
Cons:
- Requires LLM calls (cost, latency)
- Hallucination risk for unfamiliar architectures
- Needs good prompts
Implementation: ~800 LOC, integrates with existing ArchitectAgent
Architecture 3: Federated Model Graph with Roofline Integration
Approach: Build a graph database linking models ↔ hardware ↔ benchmarks, with roofline analysis.
┌─────────────────────────────────────────────────────────────────┐ │ Federated Model-Hardware Graph │ ├─────────────────────────────────────────────────────────────────┤ │ │ │ ┌─────────┐ ┌─────────┐ ┌─────────┐ │ │ │ Model │────────▶│ Roofline │◀────────│Hardware │ │ │ │ Node │ │ Edge │ │ Node │ │ │ └─────────┘ └─────────┘ └─────────┘ │ │ │ │ │ │ │ │ ┌──────────────┴──────────────┐ │ │ │ │ │ │ │ │ │ ▼ ▼ ▼ ▼ │ │ ┌─────────────────────────────────────────────────┐ │ │ │ Model-Hardware Fit │ │ │ │ │ │ │ │ model: yolov8s │ │ │ │ hardware: jetson_orin_nano │ │ │ │ arithmetic_intensity: 45.2 ops/byte │ │ │ │ peak_throughput: 2.1 TOPS (INT8) │ │ │ │ memory_bandwidth: 68 GB/s │ │ │ │ roofline_bound: compute │ │ │ │ predicted_latency_ms: 18.3 │ │ │ │ measured_latency_ms: 21.7 (benchmark) │ │ │ └─────────────────────────────────────────────────┘ │ │ │ │ │ ▼ │ │ ┌─────────────────────────────────────────────────┐ │ │ │ Graph Queries (Cypher-like) │ │ │ │ │ │ │ │ MATCH (m:Model)-[r:RUNS_ON]->(h:Hardware) │ │ │ │ WHERE r.latency_ms < 30 AND h.power_w < 15 │ │ │ │ RETURN m.name, h.name, r.latency_ms │ │ │ └─────────────────────────────────────────────────┘ │ └─────────────────────────────────────────────────────────────────┘
Integration with existing repos: - embodied-schemas: HardwareEntry, ModelEntry definitions - graphs: roofline model, ops_per_clock calibration data
Pros:
- Rich relational queries
- Integrates theory (roofline) with empirical (benchmarks)
- Supports multi-repo architecture you already have
Cons:
- Complex to implement
- Requires graph DB or custom indexing
- More infrastructure
Implementation: ~1,500 LOC, spans embodied-schemas + graphs + this repo
Comparison Matrix
| Aspect | Static Registry | LLM-Augmented | Federated Graph |
|---|---|---|---|
| Complexity | Low | Medium | High |
| Query Power | Filter/sort | Natural language | Graph traversal |
| Reasoning | None | LLM-driven | Roofline + LLM |
| Offline | Yes | No | Partial |
| Multi-repo | No | No | Yes |
| LOC estimate | ~500 | ~800 | ~1,500 |
My Recommendation
Start with Architecture 1 (Static Registry), then layer on Architecture 2 (LLM) for reasoning:
Phase 1: Model Registry (~500 LOC) - branes model register/list/show/remove - Static analysis: params, FLOPs, shapes, layer census - JSON/SQLite storage
Phase 2: LLM Integration (~300 LOC additional) - Add model_query tool to ArchitectAgent - LLM interprets registry data for recommendations - Natural language queries
Phase 3: Graph Integration (future) - Connect to embodied-schemas Registry - Add roofline edges from graphs repo
This gives you immediate value (fix the current error, build knowledge base) while preserving the path to richer reasoning.