1. CI/CD Pipelines for Machine Learning on Kubernetes


    Creating a CI/CD (Continuous Integration/Continuous Deployment) pipeline for Machine Learning on Kubernetes can be a complex process. It involves several components that work together to train models, manage the infrastructure, and deploy the resulting models into production.

    Using Pulumi, you can define these infrastructure components programmatically, which adds the benefit of version control and better collaboration among team members.

    Let’s walk through the main components you would typically need and how we can use Pulumi to deploy them:

    1. Version Control for Code: A source code repository such as GitHub, GitLab, or Azure Repos to store your Machine Learning project code.
    2. Build Server/Service: A build service like Azure DevOps Pipelines, Jenkins, or GitHub Actions to automate the training and testing of your models.
    3. Docker Registry: A place to store your containerized applications, e.g., Docker Hub, Amazon ECR, or Azure Container Registry.
    4. Kubernetes Cluster: A Kubernetes cluster to deploy your applications. You can use managed Kubernetes services like AKS (Azure Kubernetes Service), EKS (Amazon Elastic Kubernetes Service), or GKE (Google Kubernetes Engine).
    5. Machine Learning Training and Deployment: This could be using Azure Machine Learning, Amazon SageMaker, Kubeflow, or a custom solution on Kubernetes.

    For this example, we'll focus on setting up the Azure Machine Learning workspace and Kubernetes cluster using the Azure cloud provider with Pulumi to host and manage our machine learning workloads.

    Azure Machine Learning provides a cloud-based environment to prepare data, train, test, deploy, manage, and track machine learning models. Kubernetes will serve as the deployment platform for serving the models.

    Below is a Pulumi program in Python that sets up an Azure Machine Learning workspace and a Kubernetes cluster with Azure Kubernetes Service (AKS):

    import pulumi from pulumi_azure_native import resources, containerservice, machinelearningservices # Create an Azure Resource Group resource_group = resources.ResourceGroup("mlResourceGroup") # Create an Azure Machine Learning Workspace workspace = machinelearningservices.Workspace( "mlWorkspace", resource_group_name=resource_group.name, location=resource_group.location, sku=machinelearningservices.SkuArgs(name="Standard"), identity=machinelearningservices.IdentityArgs( type="SystemAssigned", ), ) # Create an Azure Kubernetes Service cluster aks_cluster = containerservice.ManagedCluster( "aksCluster", resource_group_name=resource_group.name, location=resource_group.location, agent_pool_profiles=[{ "count": 3, "max_pods": 110, "mode": "System", "name": "agentpool", "os_type": "Linux", "vm_size": "Standard_DS2_v2", }], dns_prefix=resource_group.name, enable_rgbac=True, ) # Export the kubeconfig kubeconfig = pulumi.Output.all(resource_group.name, aks_cluster.name).apply( lambda args: containerservice.list_managed_cluster_user_credentials( resource_group_name=args[0], resource_name=args[1], ).kubeconfigs[0].value.apply( lambda enc: enc.decode('utf-8') ) ) pulumi.export("kubeconfig", kubeconfig) # Export the Machine Learning Workspace URL pulumi.export("mlWorkspaceUrl", workspace.workspace_url)

    The code above performs the following actions:

    • Resource Creation: An Azure Resource Group is created to host our resources. It then creates an Azure Machine Learning workspace and an AKS Kubernetes cluster.
    • Configuration: The AKS cluster is configured with a System-assigned Managed Identity, enabling Azure services to authenticate and serve resources securely based on the Azure AD identity of the service.
    • Output: The kubeconfig is exported so that we can interact with our Kubernetes cluster using kubectl and other tools. The workspace URL for the Machine Learning workspace is also exported.

    It's important to have the necessary Azure credentials configured in your environment for Pulumi to create resources in your Azure subscription.

    Once you run the Pulumi program, it will output the kubeconfig which you can use to interact with your Kubernetes cluster and the URL to your Machine Learning workspace. You can then deploy containers that serve your machine learning models on the AKS cluster and manage your ML lifecycle in the Azure ML workspace.