zeavis-edu/apps/ml-service

ML Inference Service — ZeaVis Edu

Layanan inferensi machine learning berbasis Rust/Axum + ONNX Runtime untuk klasifikasi penyakit daun jagung.

← Kembali ke README utama

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Daftar Isi

1. Fitur
2. Prasyarat & Instalasi
3. Menjalankan Service
4. Environment Variables
5. Endpoint API
6. Verifikasi & Testing
7. Docker Deployment
8. Troubleshooting

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1. Fitur

• Framework: Axum (async Rust web framework)
• Runtime Inferensi: ONNX Runtime untuk kompatibilitas lintas platform
• Model: EfficientNetV2B0 dalam format ONNX
• Endpoint: Health check, metadata, dan prediksi gambar
• Multipart Upload: Dukungan upload gambar langsung via HTTP POST

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2. Prasyarat & Instalasi

• Rust 1.70+ dan Cargo
• Model ONNX di ../../Machine_Learning/model/model.onnx (atau path custom via MODEL_PATH)

Dependensi Rust sudah terdaftar di Cargo.toml. Cargo akan mengunduh dan mengkompilasi otomatis saat pertama kali build.

cargo build

Output build lokal berada di target/ dan direktori tersebut diabaikan oleh Git.

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3. Menjalankan Service

Semua perintah di bawah dijalankan dari direktori apps/ml-service.

Opsi 1: Default (Port 4012)

cargo run

Service akan mencari model di path default:

../../Machine_Learning/model/model.onnx

Opsi 2: Local Development dengan .env.example (Port 4012)

source .env.example
cargo run

Opsi 3: Custom Model Path & Port

ML_SERVICE_PORT=9000 MODEL_PATH=/path/to/model.onnx cargo run

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4. Environment Variables

Variable	Default	Keterangan
ML_SERVICE_HOST	0.0.0.0	Bind address
ML_SERVICE_PORT	4012	Bind port
MODEL_PATH	../../Machine_Learning/model/model.onnx	Path ke file model ONNX
MODEL_INPUT_SIZE	224	Ukuran input gambar (224×224 untuk EfficientNetV2B0)
RUST_LOG	info	Level logging (debug, info, warn, error)

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5. Endpoint API

Health Check

curl http://localhost:4012/health

{
  "status": "ok",
  "model_loaded": true
}

Metadata

curl http://localhost:4012/metadata

{
  "service_name": "zeavis-ml-service",
  "service_version": "0.1.0",
  "model_path": "../../Machine_Learning/model/model.onnx",
  "model_loaded": true,
  "input_size": 224,
  "labels": ["Bercak Daun", "Daun Sehat", "Karat Daun", "Hawar Daun"]
}

Prediksi

Upload gambar daun jagung untuk klasifikasi:

curl -X POST http://localhost:4012/predict \
  -F "file=@/path/to/corn-leaf.jpg"

{
  "label": "Daun Sehat",
  "confidence": 0.95,
  "probabilities": {
    "Bercak Daun": 0.02,
    "Daun Sehat": 0.95,
    "Karat Daun": 0.01,
    "Hawar Daun": 0.02
  }
}

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6. Verifikasi & Testing

Build Produksi

cargo build --release
# Binary di target/release/zeavis-ml-service

Menjalankan Tests

cargo test

Verifikasi Manual (default port 4012)

# 1. Start service
cargo run

# 2. Health check
curl http://localhost:4012/health

# 3. Metadata
curl http://localhost:4012/metadata

# 4. Prediksi
curl -X POST http://localhost:4012/predict \
  -F "file=@../../Machine_Learning/dataset/Daun\ Sehat/sample.jpg"

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7. Docker Deployment

Service dapat di-deploy via Docker. Build dari root repository karena Dockerfile menyalin source service dan artifact ONNX dari beberapa direktori repo.

docker build -f apps/ml-service/Dockerfile -t zeavis-ml-service .
docker run -p 4012:4012 zeavis-ml-service

Pastikan Machine_Learning/model/model.onnx sudah dibuat sebelum build image.

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8. Troubleshooting

Model tidak ditemukan

Error: Failed to load model: No such file or directory

Solusi:

ls -la ../../Machine_Learning/model/model.onnx
# Atau set path custom:
MODEL_PATH=/absolute/path/to/model.onnx cargo run

Port sudah digunakan

Error: Address already in use

Solusi:

ML_SERVICE_PORT=9000 cargo run
# Cek port yang digunakan:
lsof -i :4012

ONNX Runtime tidak kompatibel

Error: ONNX Runtime initialization failed

Solusi: Pastikan binary ONNX Runtime kompatibel dengan sistem operasi. Jika masalah persisten:

cargo clean
cargo build

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← Kembali ke README utama • Pipeline ML → • Infra →