1. Continuous Deployment of AI Models with GKE and CI/CD


    Continuous deployment of AI models involves setting up an automated process where the model training, testing, evaluation, and deployment occur seamlessly as new code is integrated. This automation is typically achieved through CI/CD pipelines and a Kubernetes environment such as Google Kubernetes Engine (GKE) for serving the model.

    We will create a Pulumi program in Python that sets up such a CI/CD pipeline and deploys a trained AI model on GKE. We'll use Google Cloud Platform (GCP) resources for this.

    Here's what our Pulumi program will do:

    1. Set up a GKE cluster where we will deploy our models.
    2. Create a registry where we will store our Docker container images.
    3. Define and launch a Kubernetes Deployment, which will use the images from the registry to run containers inside our GKE cluster.
    4. Optionally, we'll set up a CI/CD process to automate the release and deployment of new model versions.

    Below is the Python code:

    import pulumi import pulumi_gcp as gcp # Create a GKE cluster where the AI models will be deployed. ai_model_cluster = gcp.container.Cluster("ai-model-cluster", initial_node_count=2, ) # Information required to connect to the GKE cluster kubeconfig = pulumi.Output.all(ai_model_cluster.name, ai_model_cluster.endpoint, ai_model_cluster.master_auth).apply( lambda args: """ apiVersion: v1 clusters: - cluster: certificate-authority-data: {cacert} server: https://{endpoint} name: kubernetes contexts: - context: cluster: kubernetes user: kubernetes-admin name: kubernetes-admin@kubernetes current-context: kubernetes-admin@kubernetes kind: Config preferences: {{}} users: - name: kubernetes-admin user: client-certificate-data: {clientcert} client-key-data: {clientkey} """.format(cacert=args[2]['cluster_ca_certificate'], endpoint=args[1], clientcert=args[2]['client_certificate'], clientkey=args[2]['client_key'])) # Create a Google Container Registry to store Docker images. image_registry = gcp.container.Registry("ai-model-registry", project=ai_model_cluster.project, # Infer the project from our cluster. ) # Define a Kubernetes Deployment to run the AI model. ai_model_deployment = gcp.container.NodePool("ai-model-deployment", cluster=ai_model_cluster.name, initial_node_count=1, location=ai_model_cluster.location, # Run in the same location as the cluster. ) # Export the Kubeconfig so we can easily access our cluster. pulumi.export('kubeconfig', kubeconfig) # CI/CD process setup would follow. # It would include setting up a source repository, a build process that creates Docker images, # and a deployment process that updates the running services in GKE whenever a new image is available. # However, setting up a full CI/CD pipeline goes beyond the scope of this example and is unique # to each user's preferences for CI/CD tools and processes.
    • We create a Cluster which is the GKE cluster where our AI model will actually run.
    • We output the necessary kubeconfig which can be used to interact with the cluster via kubectl or other Kubernetes tools.
    • We initiate a Google Container Registry, Registry, which we'll use as a place to store and serve our AI model's container images.
    • We set up a NodePool, which is essentially a set of worker machines for our cluster. These are VMs that will run our containerized applications.
    • Finally, we export kubeconfig. This is not a necessary step, but it's convenient for accessing our cluster outside of Pulumi.

    This isn't a full CI/CD pipeline, as that requires additional setup, including source control management, build and testing automation, and deployment orchestration. Tools like Jenkins, CircleCI, GitLab CI, or GitHub Actions could all be used in conjunction with Pulumi to create a complete workflow. The pipeline configuration would depend on the chosen toolset.

    For a complete CI/CD setup, you will need to further define build and release pipelines, which could be outside the scope of Pulumi and more in the realm of specific CI/CD tools. You would typically use Pulumi to set up the base infrastructure, and then configure your CI/CD tool to use this infrastructure to deploy and manage your applications.