Creating a Custom Image and Using It to Create an AI Application

Building an Image Locally

This section uses a Linux x86_x64 host as an example. You can purchase an ECS of the same specifications or use an existing local host to create a custom image.

For details about how to purchase an ECS, see Purchasing and Logging In to a Linux ECS. When creating the ECS, select an Ubuntu 18.04 public image.

Figure 1 Creating an ECS using an x86 public image

1. After logging in to the host, install Docker. For details, see Docker official documents. Alternatively, run the following commands to install Docker:

   curl -fsSL get.docker.com -o get-docker.sh
   sh get-docker.sh

2. Obtain the base image. Ubuntu 18.04 is used in this example.

   docker pull ubuntu:18.04

3. Create the self-define-images folder, and edit Dockerfile and test_app.py in the folder for the custom image. In the sample code, the application code runs on the Flask framework.
   The file structure is as follows:

   self-define-images/
       --Dockerfile
       --test_app.py

   • Dockerfile

     From ubuntu:18.04
     # Configure the HUAWEI CLOUD source and install Python, Python3-PIP, and Flask.
     RUN cp -a /etc/apt/sources.list /etc/apt/sources.list.bak && \
       sed -i "s@http://.*security.ubuntu.com@http://repo.huaweicloud.com@g" /etc/apt/sources.list && \
       sed -i "s@http://.*archive.ubuntu.com@http://repo.huaweicloud.com@g" /etc/apt/sources.list && \
       apt-get update && \
       apt-get install -y python3 python3-pip && \
       pip3 install  --trusted-host https://repo.huaweicloud.com -i https://repo.huaweicloud.com/repository/pypi/simple  Flask
     
     # Copy the application code to the image.
     COPY test_app.py /opt/test_app.py
     
     # Specify the boot command of the image.
     CMD python3  /opt/test_app.py

   • test_app.py

     from flask import Flask, request
     import json 
     app = Flask(__name__)
     
     @app.route('/greet', methods=['POST'])
     def say_hello_func():
         print("----------- in hello func ----------")
         data = json.loads(request.get_data(as_text=True))
         print(data)
         username = data['name']
         rsp_msg = 'Hello, {}!'.format(username)
         return json.dumps({"response":rsp_msg}, indent=4)
     
     @app.route('/goodbye', methods=['GET'])
     def say_goodbye_func():
         print("----------- in goodbye func ----------")
         return '\nGoodbye!\n'
     
     
     @app.route('/', methods=['POST'])
     def default_func():
         print("----------- in default func ----------")
         data = json.loads(request.get_data(as_text=True))
         return '\n called default func !\n {} \n'.format(str(data))
     
     # host must be "0.0.0.0", port must be 8080
     if __name__ == '__main__':
         app.run(host="0.0.0.0", port=8080)

4. Switch to the self-define-images folder and run the following command to create custom image test:v1:

   docker build -t test:v1 .

5. Run docker images to view the custom image you have created.

Verifying the Image Locally and Uploading It to SWR

1. Run the following command in the local environment to start the custom image:

   docker run -it -p 8080:8080 test:v1

   Figure 2 Starting a custom image
   
2. Open another terminal and run the following commands to test the functions of the three APIs of the custom image:

   curl -X POST -H "Content-Type: application/json" --data '{"name":"Tom"}'  127.0.0.1:8080/
   curl -X POST -H "Content-Type: application/json" --data '{"name":"Tom"}' 127.0.0.1:8080/greet
   curl -X GET 127.0.0.1:8080/goodbye

   If information similar to the following is displayed, the function verification is successful.

   Figure 3 Testing API functions
   

3. Upload the custom image to SWR. For details, see How Can I Upload Images to SWR?
4. View the uploaded image on the My Images > Private Images page of the SWR console.
   Figure 4 Uploaded images
   

Using the Custom Image to Create an AI Application

Deploying the AI Application as a Real-Time Service

1. Deploy the AI application as a real-time service. For details, see Deploying as a Real-Time Service.
2. View the details about the real-time service.
   Figure 6 Usage Guides
   
3. Access the real-time service on the Prediction tab page.
   Figure 7 Accessing a real-time service