Readme.md

YoloV8

1. Check Install Ultralytics Docker tab

   docker pull ultralytics/ultralytics:latest
   docker run -it --ipc=host --gpus all ultralytics/ultralytics:latest
   

2. Create server.py script and place endpoints of the API
   • /: displays welcome message
   • /version: retrieves Pytorch version and Cuda devices
   • /predict: predicts an image passed by {"image"} field on body, encoded on base64
3. Create Dockerfile and configure the imagen

   FROM ultralytics/ultralytics:latest
   
   # Aditional dependencies if exists
   # RUN apt-get update && apt-get install -y <packagename>
   
   # Copy all files on these folder to /app folder on container
   COPY . /app
   # Change the workdir to /app
   WORKDIR /app
   
   # Install dependencies, flask in this case
   RUN pip install -r requirements.txt
   
   # Expose Flask port 5000
   EXPOSE 5000
   
   # cmd to run server
   CMD ["python", "server.py"]
   

4. Build the image from the Dockerfile

   docker build -t image-name .
   

   • -t specifies image label
   • . indicates place of the Dockerfile
5. Run the container associated to the image

   docker run -p 5000:5000 --gpus all -it image-name
   

   • -p mapping port 5000 of host machine to container port 5000
   • --gpus map gpu to the image
   • -it specifies name of the image
6. Run the client.py file to test an image processing:

   import cv2
   import requests
   import base64
   import json, time
   
   # Read img
   image = cv2.imread("test.jpg")
   
   # Convert to base64
   _, buffer = cv2.imencode('.jpg', image)
   image_base64 = base64.b64encode(buffer).decode('utf-8')
   payload = {
       'image': image_base64
   }
   
   # Send POST request to endpoint
   url = 'http://localhost:5000/predict'
   headers = {'Content-Type': 'application/json'}
   response = requests.post(url, data=json.dumps(payload), headers=headers)
   # Show response
   print(response.json())
   

Docker Compose

1. Install cmd:
   • sudo curl -L "https://github.com/docker/compose/releases/download/1.29.2/docker-compose-$(uname -s)-$(uname -m)" -o /usr/local/bin/docker-compose
   • sudo chmod +x /usr/local/bin/docker-compose
   • docker-compose --version
2. Create docker-compose.yml:

   version: '3'
   services:
       app:
           build:
               context: .
               dockerfile: dockerfile
           ports:
               - 5000:5000
           deploy:
               resources:
                   reservations:
                   devices:
                   - capabilities: [gpu]
                       driver: nvidia
   

   • context maps to current directory
   • dockerfile maps to specified dockerfile
   • ports attach host port to container port
   • runtime: nvidia specifies Nvidia Container Toolkit GPU support
3. Run the compose by docker-compose up
4. Finish and delete containers by docker-compose down