app/training/train.py

import os
from ultralytics import YOLO
import torch
import mlflow



device= 0 if torch.cuda.is_available() else "cpu"
if device==0:
    print("GPU")
else:
    print("CPU")


def train():
    # Project root
    ROOT_DIR = os.path.abspath(os.path.join(os.path.dirname(__file__), "../../"))
    data_path = os.path.join(ROOT_DIR, "data/raw/data.yaml")


    # Output directory (YOLO saves here)
    project_name= "experiments"
    run_name= "yolov8s_768_v2_run"

    output_dir= os.path.join(ROOT_DIR, project_name, run_name)


    # MLflow Setup
    mlflow.set_tracking_uri("sqlite:///mlflow.db")
    mlflow.set_experiment("license-plate-detection")


    # Training Config
    params= {
        "model": "yolov8s",
        "epochs": 40,
        "imgsz": 768,
        "batch": 6,
        "optimizer": "auto",
        "mosaic": 0.3,
        "device": device,
    }


    # Start MLflow run
    with mlflow.start_run(run_name=run_name):

        # log parameters
        mlflow.log_params(params)


        # load model
        model = YOLO("yolov8s.pt")

        # train
        results= model.train(
            data=data_path,
            epochs=params["epochs"],
            imgsz=params["imgsz"],
            device=params["device"],
            batch=params["batch"],
            cache=False,
            workers=0,
            patience=10,
            mosaic=params["mosaic"],
            project=project_name,
            name=run_name
        )

        # log metrics
        metrics = results.results_dict

        mlflow.log_metric("mAP50", metrics.get("metrics/mAP50(B)", 0))
        mlflow.log_metric("mAP50-95", metrics.get("metrics/mAP50-95(B)", 0))
        mlflow.log_metric("precision", metrics.get("metrics/precision(B)", 0))
        mlflow.log_metric("recall", metrics.get("metrics/recall(B)", 0))


        # log artifacts
        # -------------

        # 1. Best model
        best_model_path= os.path.join(output_dir, "weights/best.pt")
        if os.path.exists(best_model_path):
            mlflow.log_artifact(best_model_path, artifact_path="model")

        
        # 2. Training results csv
        results_csv= os.path.join(output_dir, "results.csv")
        if os.path.exists(results_csv):
            mlflow.log_artifact(results_csv, artifact_path="metrics")


        # 3. labels plot / confusion matrix (if generated)
        labels_img= os.path.join(output_dir, "labels.jpg")
        if os.path.exists(labels_img):
            mlflow.log_artifact(labels_img, artifact_path="plots")


        print("Training + MLflow logging completed")


if __name__ == "__main__":
    train()