From b3c59af17fa57c1136a9f6b7bca040aae1b56ded Mon Sep 17 00:00:00 2001 From: hyeokjun32 Date: Tue, 2 Jun 2026 22:44:53 +0900 Subject: [PATCH] docs: add korean portfolio entry guides --- README.md | 8 ++-- .../inferedge_1page_architecture.ko.md | 43 +++++++++++++++++ .../portfolio/inferedge_1page_architecture.md | 2 + .../portfolio/inferedge_pipeline_status.ko.md | 45 +++++++++++++++++ docs/portfolio/inferedge_pipeline_status.md | 2 + .../inferedge_portfolio_submission.ko.md | 48 +++++++++++++++++++ .../inferedge_portfolio_submission.md | 2 + .../inferedge_resume_interview_summary.ko.md | 34 +++++++++++++ .../inferedge_resume_interview_summary.md | 2 + ...st_runtime_intelligence_bundle_manifest.py | 36 ++++++++++++-- 10 files changed, 215 insertions(+), 7 deletions(-) create mode 100644 docs/portfolio/inferedge_1page_architecture.ko.md create mode 100644 docs/portfolio/inferedge_pipeline_status.ko.md create mode 100644 docs/portfolio/inferedge_portfolio_submission.ko.md create mode 100644 docs/portfolio/inferedge_resume_interview_summary.ko.md diff --git a/README.md b/README.md index c2fc52c..73b1c52 100644 --- a/README.md +++ b/README.md @@ -134,10 +134,10 @@ Portfolio entry points: | Document | Korean label | Use it for | |---|---|---| -| [Portfolio submission](docs/portfolio/inferedge_portfolio_submission.md) | [한국어: 포트폴리오 제출 문서](docs/portfolio/inferedge_portfolio_submission.md) | submission-ready project narrative | -| [Resume/interview summary](docs/portfolio/inferedge_resume_interview_summary.md) | [한국어: 이력서/면접 요약](docs/portfolio/inferedge_resume_interview_summary.md) | short role-specific explanation | -| [1-page architecture summary](docs/portfolio/inferedge_1page_architecture.md) | [한국어: 1페이지 아키텍처 요약](docs/portfolio/inferedge_1page_architecture.md) | ecosystem diagram and role split | -| [Pipeline status](docs/portfolio/inferedge_pipeline_status.md) | [한국어: 파이프라인 상태](docs/portfolio/inferedge_pipeline_status.md) | current implementation status | +| [Portfolio submission](docs/portfolio/inferedge_portfolio_submission.md) | [한국어: 포트폴리오 제출 문서](docs/portfolio/inferedge_portfolio_submission.ko.md) | submission-ready project narrative | +| [Resume/interview summary](docs/portfolio/inferedge_resume_interview_summary.md) | [한국어: 이력서/면접 요약](docs/portfolio/inferedge_resume_interview_summary.ko.md) | short role-specific explanation | +| [1-page architecture summary](docs/portfolio/inferedge_1page_architecture.md) | [한국어: 1페이지 아키텍처 요약](docs/portfolio/inferedge_1page_architecture.ko.md) | ecosystem diagram and role split | +| [Pipeline status](docs/portfolio/inferedge_pipeline_status.md) | [한국어: 파이프라인 상태](docs/portfolio/inferedge_pipeline_status.ko.md) | current implementation status | Interview one-liner: **InferEdge is an end-to-end inference validation pipeline that converts, runs, compares, diagnoses, and decides whether an edge AI model candidate is ready to deploy.** diff --git a/docs/portfolio/inferedge_1page_architecture.ko.md b/docs/portfolio/inferedge_1page_architecture.ko.md new file mode 100644 index 0000000..220aeca --- /dev/null +++ b/docs/portfolio/inferedge_1page_architecture.ko.md @@ -0,0 +1,43 @@ +# InferEdge 1-Page Architecture Summary 한국어 Quick Guide + +언어: [English](inferedge_1page_architecture.md) | 한국어 + +이 문서는 한국어 리뷰어가 아키텍처 경계를 빠르게 파악하기 위한 요약본이다. 대표/canonical 문서는 [InferEdge 1-Page Architecture Summary](inferedge_1page_architecture.md)이다. + +## 한 줄 정의 + +InferEdge는 Edge AI 모델의 artifact provenance, real runtime execution, comparability-first evidence, report, optional diagnosis, Lab-owned deployment decision을 하나로 연결하는 local-first inference validation pipeline이다. + +## 구조 + +```text +ONNX model +-> Forge +-> Runtime +-> EdgeEnv +-> Lab +-> optional AIGuard +-> optional Orchestrator operation context +``` + +## 책임 경계 + +| Component | 책임 | 소유하지 않는 것 | +|---|---|---| +| Forge | build artifact/provenance | runtime scheduling | +| Runtime | inference execution/result export | anomaly detector, deployment decision | +| EdgeEnv | registry/comparability/regression evidence | Lab decision, public leaderboard | +| Lab | report/API/job/deployment decision | production SaaS infrastructure | +| AIGuard | deterministic diagnosis evidence | final decision owner | +| Orchestrator | queue/deadline/fallback operation context | Kubernetes/cloud orchestration | + +## Runtime Intelligence 흐름 + +Runtime Intelligence는 새 repo나 monitoring SaaS가 아니다. Orchestrator operation feed, EdgeEnv telemetry/regression context, AIGuard deterministic warning evidence를 Lab report에 보존해 deployment risk를 더 쉽게 검토하게 만드는 local-first evidence extension이다. + +## Reviewer focus + +- Lab-owned deployment decision이 최종 판단 owner로 유지되는가. +- EdgeEnv comparability-first 정책이 regression 계산 전에 보존되는가. +- AIGuard/Orchestrator evidence가 supplemental context로 표시되는가. +- production SaaS, cloud control plane, production remote execution으로 과장되지 않는가. diff --git a/docs/portfolio/inferedge_1page_architecture.md b/docs/portfolio/inferedge_1page_architecture.md index 59f8e4e..8cd9bcf 100644 --- a/docs/portfolio/inferedge_1page_architecture.md +++ b/docs/portfolio/inferedge_1page_architecture.md @@ -1,5 +1,7 @@ # InferEdge 1-Page Architecture Summary +Language: English | [한국어](inferedge_1page_architecture.ko.md) + ## One-line Pitch InferEdge is an end-to-end Edge AI inference validation pipeline that builds deployment artifacts, runs edge inference, compares results, diagnoses provenance issues, and produces deployment decisions. diff --git a/docs/portfolio/inferedge_pipeline_status.ko.md b/docs/portfolio/inferedge_pipeline_status.ko.md new file mode 100644 index 0000000..fe2a095 --- /dev/null +++ b/docs/portfolio/inferedge_pipeline_status.ko.md @@ -0,0 +1,45 @@ +# InferEdge Pipeline Status 한국어 Quick Guide + +언어: [English](inferedge_pipeline_status.md) | 한국어 + +이 문서는 한국어 리뷰어가 현재 구현 상태를 빠르게 확인하기 위한 요약본이다. 대표/canonical 문서는 [InferEdge Pipeline Status](inferedge_pipeline_status.md)이다. + +## 현재 완료된 것 + +- Lab compare/report/API/job workflow와 Lab-owned deployment decision. +- Local Studio demo evidence replay. +- Runtime result JSON ingestion과 worker request/response contract. +- Jetson TensorRT FP16 25W/15W fixture evidence. +- EdgeEnv runtime telemetry/regression context ingestion. +- AIGuard deterministic runtime warning evidence preservation. +- Orchestrator queue/deadline/fallback context를 supplemental operation evidence로 표시. + +## 현재 evidence snapshot + +| Evidence | 값 | +|---|---| +| ONNX Runtime CPU FP32 demo | `45.4299 ms` mean, `49.2128 ms` p99, `22.0119 FPS` | +| Jetson TensorRT FP16 25W demo | `10.066401 ms` mean, `15.548438 ms` p99, `99.340373 FPS` | +| Demo speedup | 약 `4.51x` | +| Jetson EdgeEnv preservation smoke | `device_local_starter`, `run-20260529-034704-fbf753f0`, `runtime_operation_summary` | + +## 아직 구현하지 않았거나 명시적으로 제외한 것 + +- production worker daemon +- persistent DB/queue +- file upload +- production frontend beyond Local Studio +- auth/billing +- cloud control plane +- production remote execution +- production observability platform + +## 안전한 표현 + +현재 상태는 production SaaS 완성이 아니라, portfolio-grade local-first validation workflow와 Runtime Intelligence evidence chain이 테스트/문서/fixture로 연결된 상태다. + +Lab은 최종 decision owner다. EdgeEnv는 registry/comparability/regression evidence owner다. AIGuard는 deterministic evidence provider다. Orchestrator는 operation context provider다. + +## Jetson 필요 여부 + +이 문서 확인과 README 링크 검증에는 Jetson 기기가 필요하지 않다. 새로운 live device-local smoke나 sustained replay evidence를 추가할 때는 Jetson 기기가 필요하다. diff --git a/docs/portfolio/inferedge_pipeline_status.md b/docs/portfolio/inferedge_pipeline_status.md index a2f3f47..d8ef8cb 100644 --- a/docs/portfolio/inferedge_pipeline_status.md +++ b/docs/portfolio/inferedge_pipeline_status.md @@ -1,5 +1,7 @@ # InferEdge Pipeline Status +Language: English | [한국어](inferedge_pipeline_status.ko.md) + ## Purpose This document summarizes the current portfolio status of the InferEdge multi-repository project. diff --git a/docs/portfolio/inferedge_portfolio_submission.ko.md b/docs/portfolio/inferedge_portfolio_submission.ko.md new file mode 100644 index 0000000..ff62507 --- /dev/null +++ b/docs/portfolio/inferedge_portfolio_submission.ko.md @@ -0,0 +1,48 @@ +# InferEdge Portfolio Submission 한국어 Quick Guide + +언어: [English](inferedge_portfolio_submission.md) | 한국어 + +이 문서는 한국어 리뷰어가 빠르게 맥락을 잡기 위한 요약본이다. 대표/canonical 문서는 [InferEdge Portfolio Submission](inferedge_portfolio_submission.md)이다. + +## 핵심 메시지 + +InferEdgeLab은 Runtime 결과를 단순히 보여주는 도구가 아니라, runtime evidence를 비교하고 report/API/job result/deployment decision으로 정리하는 Lab-owned deployment decision layer다. + +InferEdge 전체 흐름은 다음처럼 읽으면 된다. + +```text +Forge build provenance +-> Runtime real execution evidence +-> EdgeEnv registry / comparability / regression context +-> Lab report / deployment decision +-> optional AIGuard deterministic evidence +-> optional Orchestrator operation context +``` + +## 한눈에 보는 역할 분리 + +| Layer | Owner | Reviewer가 확인할 것 | +|---|---|---| +| Forge | build/provenance | artifact가 어떤 model/config에서 만들어졌는가 | +| Runtime | execution/result export | 실제 runtime evidence가 Lab-compatible JSON으로 남았는가 | +| EdgeEnv | registry/comparability | 비교 가능한 조건인지 먼저 판정했는가 | +| Lab | report/deployment decision | 최종 deploy/review/blocked 판단을 Lab이 소유하는가 | +| AIGuard | deterministic diagnosis | runtime/output warning을 근거 기반으로 설명하는가 | +| Orchestrator | operation context | queue/deadline/fallback context가 supplemental evidence로 보존되는가 | + +## 강한 evidence + +- Local Studio demo pair: ONNX Runtime CPU FP32 `45.4299 ms` mean / `49.2128 ms` p99 / `22.0119 FPS`. +- Jetson TensorRT FP16 25W fixture: `10.066401 ms` mean / `15.548438 ms` p99 / `99.340373 FPS`. +- 같은 demo pair 기준 TensorRT Jetson FP16은 ONNX Runtime CPU 대비 약 `4.51x` 빠르다. +- Jetson EdgeEnv preservation smoke는 `device_local_starter`, live `tegrastats`, `runtime_operation_summary`, EdgeEnv run evidence, AIGuard warning, Lab deployment risk report까지 이어지는 local-first artifact chain을 보여준다. + +## 경계 + +이 문서는 production SaaS, production observability platform, cloud control plane, production remote execution, public leaderboard 완성을 주장하지 않는다. Runtime Intelligence는 local-first artifact/evidence chain이며, Lab-owned deployment decision을 더 설명 가능하게 만드는 확장이다. + +## 읽는 순서 + +1. README의 Portfolio entry points table을 먼저 본다. +2. 영어 canonical 문서에서 상세 evidence와 최신 수치를 확인한다. +3. 이 한국어 quick guide로 면접/리뷰 설명의 한글 표현을 정리한다. diff --git a/docs/portfolio/inferedge_portfolio_submission.md b/docs/portfolio/inferedge_portfolio_submission.md index 1bf8616..15af1ae 100644 --- a/docs/portfolio/inferedge_portfolio_submission.md +++ b/docs/portfolio/inferedge_portfolio_submission.md @@ -1,5 +1,7 @@ # InferEdge Portfolio Submission +Language: English | [한국어](inferedge_portfolio_submission.ko.md) + ## 1. Project Summary InferEdge는 edge AI 모델을 변환, 실행, 비교, 진단하고 최종 배포 가능 여부를 판단하는 end-to-end inference validation pipeline이다. diff --git a/docs/portfolio/inferedge_resume_interview_summary.ko.md b/docs/portfolio/inferedge_resume_interview_summary.ko.md new file mode 100644 index 0000000..ee936c2 --- /dev/null +++ b/docs/portfolio/inferedge_resume_interview_summary.ko.md @@ -0,0 +1,34 @@ +# InferEdge Resume and Interview Summary 한국어 Quick Guide + +언어: [English](inferedge_resume_interview_summary.md) | 한국어 + +이 문서는 한국어 이력서/면접 설명을 빠르게 잡기 위한 요약본이다. 대표/canonical 문서는 [InferEdge Resume and Interview Summary](inferedge_resume_interview_summary.md)이다. + +## 45초 설명 + +InferEdge는 edge AI 모델을 build provenance, real runtime execution, 비교/report, optional deterministic diagnosis, Lab-owned deployment decision까지 연결하는 inference validation pipeline입니다. 저는 Lab에서 Runtime result를 compare/report/API/job workflow/deployment decision으로 묶고, EdgeEnv의 registry/comparability/regression evidence와 AIGuard의 deterministic warning evidence를 Lab report에 보존하는 흐름을 구현했습니다. Jetson TensorRT FP16 25W fixture는 `10.066401 ms` mean, `15.548438 ms` p99, `99.340373 FPS`를 기록했고, Local Studio demo pair에서는 ONNX Runtime CPU 대비 약 `4.51x` speedup을 보여줍니다. + +## 역할별 강조점 + +| 지원 역할 | 강조할 메시지 | +|---|---| +| AI Inference Engineer | TensorRT/ONNX Runtime evidence, latency/p99/FPS, provenance-aware compare key | +| Embedded / Edge Engineer | Jetson evidence, power-mode context, device-local preservation smoke | +| Backend / AI Platform | API/job/report contract, Lab-owned decision, artifact bundle traceability | + +## 면접에서 강하게 말할 것 + +- 단순 benchmark가 아니라 artifact provenance -> runtime evidence -> deployment decision까지 이어지는 validation pipeline이다. +- EdgeEnv는 비교 가능성 판단과 regression evidence를 담당하고, Lab deployment decision을 대체하지 않는다. +- AIGuard는 LLM 추측이 아니라 deterministic evidence provider다. +- Orchestrator operation context는 supplemental evidence이며 production scheduler 완성 주장이 아니다. + +## 말하지 않을 것 + +- production SaaS 완성 +- production observability platform +- cloud control plane +- production remote execution +- AIGuard가 최종 배포 판단을 자동으로 내린다는 표현 + +정확한 표현은 "portfolio-grade local-first validation and runtime intelligence evidence pipeline"이다. diff --git a/docs/portfolio/inferedge_resume_interview_summary.md b/docs/portfolio/inferedge_resume_interview_summary.md index 936029f..7e184bc 100644 --- a/docs/portfolio/inferedge_resume_interview_summary.md +++ b/docs/portfolio/inferedge_resume_interview_summary.md @@ -1,5 +1,7 @@ # InferEdge Resume and Interview Summary +Language: English | [한국어](inferedge_resume_interview_summary.ko.md) + ## Final Resume Bullets - Built InferEdge, an end-to-end Edge AI inference validation pipeline that connects Forge build provenance, C++ Runtime execution, Lab comparison/report/API/job workflows, optional AIGuard diagnosis evidence, and Lab-owned deployment decisions. diff --git a/tests/test_runtime_intelligence_bundle_manifest.py b/tests/test_runtime_intelligence_bundle_manifest.py index 0e7ea74..a6120cd 100644 --- a/tests/test_runtime_intelligence_bundle_manifest.py +++ b/tests/test_runtime_intelligence_bundle_manifest.py @@ -214,32 +214,62 @@ def test_readme_internal_links_include_matching_korean_labels(): "Portfolio submission", "포트폴리오 제출 문서", "docs/portfolio/inferedge_portfolio_submission.md", + "docs/portfolio/inferedge_portfolio_submission.ko.md", ), ( "Resume/interview summary", "이력서/면접 요약", "docs/portfolio/inferedge_resume_interview_summary.md", + "docs/portfolio/inferedge_resume_interview_summary.ko.md", ), ( "1-page architecture summary", "1페이지 아키텍처 요약", "docs/portfolio/inferedge_1page_architecture.md", + "docs/portfolio/inferedge_1page_architecture.ko.md", ), ( "Pipeline status", "파이프라인 상태", "docs/portfolio/inferedge_pipeline_status.md", + "docs/portfolio/inferedge_pipeline_status.ko.md", ), ( "docs/portfolio/edgeenv_runtime_regression_lab_handoff.md", "EdgeEnv 런타임 회귀 Lab handoff 문서", "docs/portfolio/edgeenv_runtime_regression_lab_handoff.md", + "docs/portfolio/edgeenv_runtime_regression_lab_handoff.md", + ), + ] + + for english_label, korean_label, english_target, korean_target in link_pairs: + assert f"[{english_label}]({english_target})" in readme + assert f"[한국어: {korean_label}]({korean_target})" in readme + + +def test_portfolio_entry_korean_guides_preserve_language_links_and_boundaries(): + guide_pairs = [ + ("inferedge_portfolio_submission.md", "inferedge_portfolio_submission.ko.md"), + ( + "inferedge_resume_interview_summary.md", + "inferedge_resume_interview_summary.ko.md", ), + ("inferedge_1page_architecture.md", "inferedge_1page_architecture.ko.md"), + ("inferedge_pipeline_status.md", "inferedge_pipeline_status.ko.md"), ] - for english_label, korean_label, target in link_pairs: - assert f"[{english_label}]({target})" in readme - assert f"[한국어: {korean_label}]({target})" in readme + for english_name, korean_name in guide_pairs: + english_doc = REPO_ROOT / "docs" / "portfolio" / english_name + korean_doc = REPO_ROOT / "docs" / "portfolio" / korean_name + english_text = english_doc.read_text(encoding="utf-8") + korean_text = korean_doc.read_text(encoding="utf-8") + + assert f"Language: English | [한국어]({korean_name})" in english_text + assert f"언어: [English]({english_name}) | 한국어" in korean_text + assert "대표/canonical 문서" in korean_text + assert "Lab-owned deployment decision" in korean_text + assert "production SaaS" in korean_text + assert "cloud control plane" in korean_text def test_runtime_intelligence_bundle_manifest_gate_validates_edgeenv_handoff(