diff --git a/README.md b/README.md index 66a7cb7..a0828e3 100644 --- a/README.md +++ b/README.md @@ -66,11 +66,53 @@ Repository roles are deliberately split: - **InferEdgeEnv:** v0.1.5 v1-complete experiment hygiene / comparability layer; local-first run evidence registry and comparability checker for Edge AI inference benchmark results. - **InferEdgeOrchestrator:** supplemental operation context provider for queue, deadline, fallback, thermal, and resource evidence. It is not a comparability owner or deployment decision owner. -Portfolio boundary: InferEdgeLab is the validation / decision layer. InferEdgeEnv is the v0.1.5 v1-complete experiment hygiene / comparability layer. InferEdge validates whether a model is deployable; InferEdgeEnv records whether benchmark evidence can be trusted and compared. +### Current boundaries -Runtime Intelligence boundary: the current smoke chain preserves Orchestrator `edgeenv_runtime_telemetry_feed` as supplemental operation context, EdgeEnv `runtime_telemetry_context.history.telemetry_coverage` and `runtime_telemetry_history_seed` as producer-owned replay evidence, AIGuard `guard_analysis` as deterministic diagnosis evidence, and Lab as the final report/deployment decision owner. This is local-first runtime evidence automation, not production observability or a runtime control plane. +InferEdgeLab is the validation and decision layer. InferEdgeEnv is the experiment hygiene and comparability layer. -Implemented today: Lab API response contract, `/api/compare`, `/api/analyze` in-memory jobs, worker request/response mappings, Runtime dry-run validation/export, Forge worker/runtime summary, AIGuard provenance mismatch diagnosis, Lab decision/report evidence smoke coverage, dev-only Lab -> Runtime ONNX Runtime smoke using `yolov8n.onnx`, manual Jetson TensorRT Runtime smoke using a Forge manifest plus TensorRT engine artifact, Runtime source-model identity preservation for compare-ready TensorRT engine results, and the Runtime Intelligence smoke chain from Orchestrator operation feed through EdgeEnv/AIGuard into a Lab-owned risk summary. +In practice: + +- InferEdge validates whether a model candidate is deployable. +- InferEdgeEnv records whether benchmark evidence can be trusted and compared. +- AIGuard adds deterministic diagnosis evidence when available. +- Orchestrator contributes supplemental operation context, not final verdicts. +- Lab remains the final deployment decision owner. + +Runtime Intelligence is implemented as local-first evidence automation: + +```text +Orchestrator supplemental operation context +-> EdgeEnv telemetry history / regression evidence +-> optional AIGuard deterministic diagnosis evidence +-> Lab Runtime Intelligence Risk Summary +``` + +This is not a production observability platform or runtime control plane. + +### Implemented today + +Core Lab workflow: + +- API response contract +- `/api/compare` and `/api/analyze` in-memory jobs +- worker request/response mappings +- compare/report/deployment decision smoke coverage + +Cross-repo evidence: + +- Runtime dry-run validation/export +- Forge worker/runtime summary +- AIGuard provenance mismatch diagnosis +- dev-only Lab -> Runtime ONNX Runtime smoke using `yolov8n.onnx` +- manual Jetson TensorRT Runtime smoke using a Forge manifest plus TensorRT engine artifact +- Runtime source-model identity preservation for compare-ready TensorRT engine results + +Runtime Intelligence smoke: + +- Orchestrator operation feed preserved as supplemental context +- EdgeEnv telemetry history/regression evidence ingested by Lab +- AIGuard deterministic runtime evidence preserved when available +- Lab-owned Runtime Intelligence Risk Summary generated without changing existing JSON contracts Runtime identity polish: when a Forge manifest is applied, Runtime now preserves the manifest `source_model.path` identity for comparison naming. A TensorRT artifact such as `model.engine` can therefore keep `compare_model_name=yolov8n` and `compare_key=yolov8n__b1__h640w640__fp32` instead of degrading to `model__...`. This is provenance/compare-readiness polish, not production SaaS infrastructure.