# AIEM

AI Edge Management

**TODO**: introduce segmentation env variable 

AIEM repo can be seen as the core shared across all the projects that require an AI model to be trained or to run an inference server. It talks to the rest of the project-specific repos by means of, e.g., a GitHub Actions workflow. It contains Dockerfiles for different architectures and for different purposes. For example: training a YoloV8 model in an x86 architecture (*Dockerfile.x86.yolov8_trainer*). 

## Structure

The structure of the project:

```bash
.
├── docker
│   ├── Dockerfile.x86.yolov8_trainer
│   └── scripts
│       └── docker_build.sh
├── README.md
├── runner
│   └── README.md
└── trainer
    ├── README.md
    ├── train_yolov8.py
    └── utils
        ├── cvat_dataset.py
        ├── download_cvatdata.py
        ├── merge_cocos.py
        ├── path_utils.py
        ├── unzip_datasets.py
        └── yolo_labels.py
```

- **Download data** (*trainer/utils/download_cvatdata.py*). Main script to download the dataset into the docker container. It reads from project-specific YAML file with the tasks to download from CVAT, preprocess the data and get the workspace ready for the model be able to be trained.