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docs: align Rust ONNX service deployment docs

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- Update docker-compose.yml MODEL_PATH from best_model.keras to model.onnx
- Fix Machine_Learning/README.md TOC and section numbering (remove duplicate section 9, add Validasi Parity ONNX as section 9)
- Clarify parity validation as manual/recommended, not mandatory CI
- Update CLAUDE.md to document Rust/Axum/ONNX Runtime ML service and ONNX export workflow
- Simplify root README.md ML service section with port clarification (8001 local, 8000 container)
- Remove stale endpoint examples from root README (documented in apps/ml-service/README.md)
- Ensure no FastAPI/Uvicorn references in deployment documentation

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
This commit is contained in:
Asep Haryana Saputra
2026-05-23 10:58:08 +00:00
co-authored by Claude Opus 4.7
parent abff278f49
commit f2e4c338bb
4 changed files with 29 additions and 26 deletions
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README.md
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@@ -166,23 +166,7 @@ Jika model berada di lokasi lain, gunakan environment variable `MODEL_PATH`:
MODEL_PATH=/path/to/model.onnx cargo run
```
## Endpoint Penting
### ML Service
| Method | Endpoint | Fungsi |
|---|---|---|
| GET | `/health` | Mengecek status service dan status model |
| GET | `/metadata` | Melihat metadata service, label, input size, dan path model |
| POST | `/predict` | Mengunggah gambar daun jagung untuk klasifikasi |
Contoh verifikasi lokal:
```bash
curl http://localhost:8001/health
curl http://localhost:8001/metadata
curl -X POST http://localhost:8001/predict -F "file=@/path/to/corn-leaf.jpg"
```
Service mendengarkan di `http://localhost:8001` secara default untuk development lokal (lihat `apps/ml-service/.env.example`). Dalam Docker container, service mendengarkan di port `8000`.
## Docker Deployment