Custom build the Seldon server

How to custom build the Seldon server

Introduction

We need to set up the container environment to deploy our registered model. We’ll customize a pre-packaged model image for this step to suit our needs. This will demonstrate how to modify, build, and deploy a custom image using PrimeHub Deployments.

Requirements

To follow the instructions in this section you should have:

• A docker account

• Familiarity with the command line

• Python version 3 or above

• An x86/64 CPU (Apple M1 currently not supported)

We will be using the screw model prepackage server as a template.

Step-by-step Method

On your local computer, run the following commands to clone the model server repository:

• Check the deployment/ project:

$ git clone https://github.com/InfuseAI/primehub-screw-detection.git
$ cd ~/primehub-screw-detection/deployment/

In a text editor, open the following file ./tensorflow2/Model.py and modify the prediction logic.

After editing and saving Model.py, build the pre-packaged model image with the following command.

$ make build

Check that the image is listed by running:

$ docker images

The output should look similar to:

REPOSITORY                            TAG                               IMAGE ID       CREATED        SIZE
infuseaidev/tensorflow2-prepackaged   screw-classification-v0.0.1       689530dd1ef9   3 minutes ago  1.67GB

Tag and Push to Docker

Tag the image into your Docker registry with the screw-classification tag, replacing with your Docker username.

$ make push

If you’re not logged into docker yet, log in now:

$ docker login

You can see your image in DockerHub web UI.