Inference Server Example

This project demonstrates a simple inference server using Flask, Docker, and a pre-trained machine learning model based on the Iris dataset. The server provides an API to predict the category of an Iris plant based on its features.

Table of Contents

• Requirements
• Installation
• Model Training
• Usage
• API Documentation
• Logs
• Troubleshooting

Requirements

To run this project, you need to have the following installed:

• Docker
• Docker Compose

If Docker is not installed, follow these steps:

Docker Installation (Ubuntu 22.04 LTS)

1. Update your package list:

   sudo apt-get update

2. Install required dependencies:

   sudo apt-get install ca-certificates curl gnupg lsb-release

3. Add Docker’s official GPG key:

   sudo mkdir -p /etc/apt/keyrings
   curl -fsSL https://download.docker.com/linux/ubuntu/gpg | sudo gpg --dearmor -o /etc/apt/keyrings/docker.gpg

4. Set up the Docker stable repository:

   echo \
   "deb [arch=$(dpkg --print-architecture) signed-by=/etc/apt/keyrings/docker.gpg] https://download.docker.com/linux/ubuntu \
   $(lsb_release -cs) stable" | sudo tee /etc/apt/sources.list.d/docker.list > /dev/null

5. Install Docker:

   sudo apt-get update
   sudo apt-get install docker-ce docker-ce-cli containerd.io docker-buildx-plugin docker-compose-plugin

6. Start Docker:

   sudo systemctl start docker
   sudo systemctl enable docker

Installation

1. Clone the repository:

   git clone https://github.com/your-username/inference-server-example.git
   cd inference-server-example

2. Install required Python packages (if you are running locally):

   pip install -r requirements.txt

Model Training

Before train the model, you need to create data directory and insert iris_with_category_index.csv file.

Before running the server, you need to train the model and save the trained model as a .pkl file. Run the following command to execute train.py and generate the model file:

python train.py

This will generate a file named iris_model.pkl model directory in the project.

Usage

Once the model is trained and the server is running, you can interact with the API.

Docker Compose

If you want to run the server inside Docker, use the following command to build and run the Docker container:

docker-compose up --build

The API will be accessible at http://localhost:5000.

Example Request

To get a prediction for the Iris dataset, send a GET request:

curl http://localhost:5000/predict/1

Example Response

{
  "data_id": 1,
  "class": 0
}

If you request an invalid data_id, the server will return a 404 error:

curl http://localhost:5000/predict/9999

Response:

{
  "error": "Data not found"
}

API Documentation

1. Prediction Endpoint

• URL: /predict/<data_id>
• Method: GET
• Parameters:
  • data_id (integer): The ID of the data entry from the Iris dataset to predict.
• Response:
  • Success: 200 OK with a JSON body containing:
    • data_id: The ID of the input data.
    • class: The predicted category (classification).
  • Failure: 404 Not Found if the data_id is invalid or does not exist.

Example:

GET /predict/1

{
  "data_id": 1,
  "class": 0
}

Logs

Logs are stored in the logs directory and can be accessed both from inside and outside the Docker container.

• The main log file is located at logs/app.log.

• You can inspect logs using:

  cat logs/app.log

Troubleshooting

• If the container fails to start, check the logs using:

  docker-compose logs

• If you need to stop the container:

  docker-compose down

If you encounter any issues or need further help, please open an issue in the repository.