Demo Project

This is a sample project to demonstrate how to setup your own ML service and publish it as a Docker container.
It is intended to be used as a possible template for the participants of the Hackfestival 2024.

This service simulates the steps of training your ML Model and then serving the stored, trained model as a REST API. First in train_model.py random data is generated, then a simple local outlier probability model is trained on this data.
The trained model is then saved to disk as a pickle and served as a REST API in ml_service.py.

A docker image is created, which only contains the trained model, the requirements.txt file and the ml_service.py file.
The send_request.py file is only used for local testing and is not part of the docker image.
The train_model.py file is only used to train the model localy on your machine and is also not part of the docker image.

How to run this project (Optional)

This project is only intended to give you some inspiration. You don't need to run it yourself. However, if you want to run it, follow these steps:

1. Clone the repository:

   git clone https://github.com/IroNEDR/hackathon-demo.git

2. Navigate to the project directory:

   cd hackathon-demo

3. Create a virtual environment and activate it:

   python3 -m venv env
   source env/bin/activate # On Mac or Linux
   # On Windows:
   .\env\Scripts\activate

4. Install the required packages:

   pip install -r requirements.txt

5. Train the model:

   python train_model.py

6. Run the ML service:

   python ml_service.py

7. Open a new terminal and send a request to the service:

   python send_request.py

How to create your own Python project

When you create your own Python project follow these steps:

1. Ensure that you have python and python-venv installed in your machine and if not install it following the instructions for your OS.

2. Create a directory for your project and navigate to it:

   mkdir your_project
   cd your_project

3. Create a virtual environment. You can choose your name, but I recommend using the name env:

   python3 -m venv env

4. Activate the virtual environment:

   source env/bin/activate # On Mac or Linux
   .\env\Scripts\activate # On Windows

5. Install the packages from the requirements.txt file using pip:

   pip install <package_name>

6. Create a requirements.txt that specifies all the external packages needed to run your app:

   pip freeze > requirements.txt

   Your teammates can then install all the dependencies by running:

   pip install -r requirements.txt

7. Create a .gitignore file in your project directory and add this content to it.

8. Create a README.md file in your project directory and add a description of your project.

9. Create a Dockerfile in your project and set it up similar to the one in this project.

10. Create a new repository on GitHub and push your project to it:

    git init
    git add .
    git commit -m "Initial commit"
    git branch -M main
    git remote add origin <your_git_repository_url>
    git push -u origin main

11. Build and publish your docker image so that you can later use it in SAP AI Core.

    docker login
    # After you have logged in, build and push your docker image
    docker build -t <your_docker_image_name> .
    docker tag <your_docker_image_name> <your_docker_image_name>:latest
    docker push <your_docker_image_name>:latest

12. Follow the instructions in the SAP AI CORE Hackathon Repository if you want to run your docker image in SAP AI Core.