docs: document Rust ONNX ML service
Update documentation to reflect migration from FastAPI/Uvicorn to Rust/Axum with ONNX Runtime: - Root README: Update ML service description, tech stack, prerequisites, and run instructions to use Rust/Cargo instead of Python/Uvicorn - Root README: Update model path references from best_model.keras to model.onnx - Root README: Add model.onnx to artifact lists and generated files - Root README: Update troubleshooting section with Rust-specific guidance - Machine_Learning/README: Add table of contents entry for ONNX conversion - Machine_Learning/README: Add Tahap 5 section documenting ONNX conversion with convert_onnx.py and validate_onnx_parity.py - Machine_Learning/README: Update output table to include model.onnx with Rust ONNX Runtime usage - apps/ml-service/README: Create comprehensive documentation for Rust Axum ONNX service including setup, endpoints, environment variables, testing, and troubleshooting Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
This commit is contained in:
Asep Haryana Saputra
co-authored by
Claude Opus 4.7
Claude Opus 4.7
parent
b1e99643f6
commit
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@@ -6,7 +6,7 @@ ZeaVis Edu adalah aplikasi edukasi untuk membantu mengenali penyakit daun jagung
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- Aplikasi web untuk pengalaman pengguna dan interaksi edukatif.
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- API backend untuk status layanan, integrasi data, dan komunikasi dengan layanan ML.
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- ML service berbasis FastAPI untuk inferensi penyakit daun jagung dari gambar.
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- ML service berbasis Rust/Axum dengan ONNX Runtime untuk inferensi penyakit daun jagung dari gambar.
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- Pipeline machine learning untuk preprocessing dataset, training di Google Colab, dan ekspor model produksi.
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- Dukungan Docker untuk deployment web, API, dan ML service.
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- Workspace monorepo berbasis Bun dan Moon untuk menjalankan task development, typecheck, dan build secara terpusat.
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@@ -28,7 +28,7 @@ Model klasifikasi menargetkan empat label berbahasa Indonesia:
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.
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├── apps/
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│ ├── api/ # Backend Elysia/Bun
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│ ├── ml-service/ # Layanan inferensi FastAPI + TensorFlow
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│ ├── ml-service/ # Layanan inferensi Rust/Axum + ONNX Runtime
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│ └── web/ # Frontend React + Vite
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├── Machine_Learning/ # Pipeline dataset, training, dan ekspor model
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├── packages/
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@@ -59,11 +59,12 @@ Model klasifikasi menargetkan empat label berbahasa Indonesia:
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### Machine Learning
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- Python
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- Python (preprocessing, training, export)
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- TensorFlow/Keras
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- EfficientNetV2B0
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- FastAPI
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- Uvicorn
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- Rust
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- Axum
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- ONNX Runtime
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- TFLite
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- TensorFlow.js
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@@ -82,10 +83,10 @@ Untuk menjalankan seluruh project secara lokal, siapkan:
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- Bun
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- Python 3.9–3.11 untuk pipeline ML
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- Python 3.10+ untuk `apps/ml-service`
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- Rust dan Cargo untuk `apps/ml-service`
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- Docker dan Docker Compose jika ingin menjalankan/deploy via container
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- PostgreSQL jika fitur backend yang membutuhkan database digunakan
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- File model `Machine_Learning/best_model/best_model.keras` untuk inferensi ML lokal
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- File model `Machine_Learning/model/model.onnx` untuk inferensi ML lokal
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## Instalasi Root Workspace
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@@ -150,19 +151,20 @@ bun run typecheck
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```bash
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cd apps/ml-service
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python -m venv .venv
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source .venv/bin/activate
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pip install -r requirements.txt
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uvicorn main:app --host 0.0.0.0 --port 8001
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cargo run
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```
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Default path model adalah:
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```text
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../../Machine_Learning/best_model/best_model.keras
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../../Machine_Learning/model/model.onnx
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```
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Jika model berada di lokasi lain, gunakan environment variable `MODEL_PATH`.
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Jika model berada di lokasi lain, gunakan environment variable `MODEL_PATH`:
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```bash
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MODEL_PATH=/path/to/model.onnx cargo run
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```
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## Endpoint Penting
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@@ -248,6 +250,7 @@ Output utama pipeline ML:
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| `Machine_Learning/best_model/best_model.keras` | Model Keras hasil training |
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| `Machine_Learning/model/saved_model/` | TensorFlow SavedModel |
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| `Machine_Learning/model/model.tflite` | Model untuk mobile/TFLite |
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| `Machine_Learning/model/model.onnx` | Model untuk Rust ONNX Runtime |
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| `Machine_Learning/model/tfjs_model/` | Model untuk TensorFlow.js |
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## Artifact Lokal dan Generated Files
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@@ -262,6 +265,7 @@ Beberapa file tidak tersedia di fresh clone karena berukuran besar, dihasilkan l
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- `Machine_Learning/best_model/best_model.keras`
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- `Machine_Learning/model/saved_model/`
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- `Machine_Learning/model/model.tflite`
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- `Machine_Learning/model/model.onnx`
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- `Machine_Learning/model/tfjs_model/`
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## Environment Variable Penting
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@@ -272,7 +276,7 @@ Beberapa file tidak tersedia di fresh clone karena berukuran besar, dihasilkan l
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| `API_PORT` | API | Port backend produksi |
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| `WEB_APP_URL` | API | URL frontend untuk konfigurasi CORS/integrasi |
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| `ML_SERVICE_URL` | API | URL layanan ML |
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| `MODEL_PATH` | ML Service | Lokasi file model Keras |
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| `MODEL_PATH` | ML Service | Lokasi file model ONNX, default `../../Machine_Learning/model/model.onnx` |
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| `MODEL_INPUT_SIZE` | ML Service | Ukuran input model, default produksi `224` |
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## Troubleshooting
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@@ -294,13 +298,13 @@ Pastikan `DATABASE_URL` tersedia di `.env` root dan PostgreSQL dapat diakses ole
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Pastikan file model tersedia di path default:
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```text
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Machine_Learning/best_model/best_model.keras
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Machine_Learning/model/model.onnx
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```
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Atau set path khusus:
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```bash
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MODEL_PATH=/path/to/best_model.keras uvicorn main:app --host 0.0.0.0 --port 8001
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MODEL_PATH=/path/to/model.onnx cargo run
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```
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### Docker Compose gagal karena network tidak ditemukan
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