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.
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.
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
$ make push
If you’re not logged into docker yet, log in now:
$ docker login
You can see your image in DockerHub web UI.