How to build, publish and deploy a service¶

This guide will help you in the steps to build, publish and deploy a service for the Core engine.

Follow the official tutorials¶

If you have not already, we highly recommend you to follow the official tutorials to learn how to create a new service:

Build, publish and deploy a service¶

Info

If you want to deploy the service, you will need a container registry and a Kubernetes cluster.

If you are part of the Swiss AI Center GitHub organization, most of the process is automated and you can use GitHub container registry and Kubernetes cluster.

If you are not part of the Swiss AI Center GitHub organization, you can use a container registry and a Kubernetes cluster of your choice such as GitHub container registry and your own Kubernetes cluster. You will need the Kubeconfig file of your Kubernetes cluster.

You have two ways to build, publish and deploy your service:

Using a CI/CD pipeline (recommended)Build, publish and deploy manually

Tip

This can only work if you commit and push your code to a GitHub repository.

Store the secrets in GitHub Actions

Note

If your repository is part of the Swiss AI Center GitHub organization, most of the following secrets are already set for you.

Access the GitHub repository settings. Under Secrets and variables > Actions, open the Secrets tab.

Add secrets using the New repository secret button.

The following secrets are required to build, publish and deploy the service:

Secret name	Description	Type	Default value¹
`DEV_KUBE_CONFIG`	The content of the Kubeconfig file.	string	`*****`

All DEV_* secrets can be duplicated to PROD_* secrets to deploy the service on the production environment.

Store the variables in GitHub Actions

Note

If your repository is part of the Swiss AI Center GitHub organization, most of the following variables are already set for you.

Access the GitHub repository settings. Under Secrets and variables > Actions, open the Variables tab.

Add variables using the New repository variable button.

The following variables are required to build, publish and deploy the service:

Variable name	Description	Type	Default value¹
`RUN_CICD`	Whether to run the CI/CD pipeline.	boolean	`true`
`DEPLOY_DEV`	Whether to deploy the service on the development environment.	boolean	`false`
`DEPLOY_PROD`	Whether to deploy the service on the production environment.	boolean	`false`
`SERVICE_NAME`	The name of the service.	string
`MODEL_PATH`	The path to the model binary file, e.g. `./model-creation/model/my-model.h5`	string
`DEV_SERVICE_URL`	The URL of the service of the development environment.	string
`DEV_CORE_ENGINE_URLS`	The URL(s) of the Core engine(s) of the development environment.	string	`'["https://backend-core-engine-swiss-ai-center.kube.isc.heia-fr.ch"]'`
`DEV_ENGINE_ANNOUNCE_RETRIES`	The number of retries to announce the service to the Core engine(s) of the development environment.	string	`'5'`
`DEV_ENGINE_ANNOUNCE_RETRY_DELAY`	The delay between each retry to announce the service to the Core engine(s) of the development environment.	string	`'3'`
`DEV_LOG_LEVEL`	The log level of the service of the development environment.	string	`info`
`DEV_MAX_TASKS`	The maximum number of tasks the service of the development environment can accept in its queue.	string	`'50'`
`DEV_NAMESPACE`	The namespace of the service of the development environment.	string	`swiss-ai-center-dev`

All DEV_* variables can be duplicated to PROD_* variables to deploy the service on the production environment.

Run the GitHub Actions workflow

Note

Your code must be pushed to a GitHub repository to trigger the GitHub Actions workflow.

In the GitHub repository, open the Actions tab.

If a workflow is already running, you can click on it to see its progress.

If no workflow is running, you can click on the github_workflow on the left side of the Actions tab.

Click on the Run workflow button. Select the main branch and click on the Run workflow button.

The workflow should start and your service should be built, published and deployed automatically:

The model-serving directory is reviewed with the help of Flake8
The model is trained in the model-creation directory
The model is uploaded to GitHub artifacts using the MODEL_PATH variable
The service is tested in the model-serving directory with the help of pytest
The service is released to the GitHub container registry
The service is deployed on the development/production environment

The service should be deployed on the development/production environment.

Build the Docker image

Info

If you want to publish the Docker image to a container registry, you will need to tag the Docker image with the URL of the container registry.

See the official GitHub container registry documentation or the official GitLab container registry documentation for more information.

Build the Docker image using the following command:

# Build the Docker image
docker build -t <image-name>:<image-tag> .

Run the Docker container

Run the Docker container using the following command:

# Run the Docker container
docker run -p 9090:80 <image-name>:<image-tag>

Access the Docker container

Access the Docker container at http://localhost:9090. You should see the FastAPI documentation of the service.

Publish the Docker image

Login to the container registry using the following command:

# Login to the container registry
docker login <container-registry-url>

Publish the Docker image using the following command:

# Publish the Docker image
docker push <image-name>:<image-tag>

Update the Kubernetes files

Edit the following Kubernetes files to configure your service:

kubernetes/config-map.yml
kubernetes/ingress.yml
kubernetes/service.yml
kubernetes/stateful.yml

Deploy the service on Kubernetes

Using the Kubeconfig file of your Kubernetes cluster, deploy the service on Kubernetes using the following command:

# Deploy the service on Kubernetes
kubectl --kubeconfig ~/path/to/your/kubeconfig --namespace namespace-of-your-kubernetes-cluster apply \
    -f kubernetes/config-map.yml \
    -f kubernetes/ingress.yml \
    -f kubernetes/service.yml \
    -f kubernetes/stateful.yml

The service should be deployed on Kubernetes.

If your repository is part of the Swiss AI Center GitHub organization. ↩↩