1. GitOps for Kubernetes-Based Machine Learning Pipelines


    GitOps is a set of practices that leverages Git workflows to manage and automate the deployment of infrastructure and applications. For Kubernetes-based machine learning (ML) pipelines, GitOps can be particularly beneficial as it allows developers to use the same tools and processes for source code management for their infrastructure as code (IaC) and application deployments.

    In this context, a GitOps workflow involves several key components:

    • A Git repository that holds your Kubernetes manifests, ML pipeline definitions, and possibly the ML code itself.
    • An agent or operator inside the Kubernetes cluster that monitors the Git repository and applies changes to the cluster based on the manifests it finds.

    For this kind of workflow, Pulumi does not directly provide a GitOps tool but can be used to set up the underlying infrastructure required for GitOps. For instance, Pulumi can help create the Kubernetes clusters and any cloud resources you need, and you can use other tools like Flux or Argo CD to implement the GitOps operations within the cluster.

    Let's assume you want to set up a GitOps workflow for a Kubernetes-based ML pipeline on a cloud provider like AWS, Azure, or GCP. Here is a Pulumi program that demonstrates how you could configure the necessary infrastructure. For the purpose of this example, we'll use AWS and EKS (Amazon Elastic Kubernetes Service), but similar concepts would apply for Azure AKS or Google GKE.

    The following program will perform these tasks:

    • Create a new VPC for our Kubernetes cluster.
    • Provision an EKS cluster in this VPC.
    • Configure kubectl using the output from the EKS cluster.

    Once Pulumi is done provisioning the infrastructure, you would typically proceed to set up your GitOps tooling, such as Flux or Argo CD, which is beyond the scope of the Pulumi resources but could form part of your cloud setup scripts or be manually set up by your operations team.

    import pulumi import pulumi_aws as aws import pulumi_eks as eks # Create a VPC for our cluster. vpc = aws.ec2.Vpc("vpc", cidr_block="") # Create Internet Gateway for the VPC igw = aws.ec2.InternetGateway("igw", vpc_id=vpc.id) # Create a Subnet for the EKS cluster. This simplifies VPC configuration. subnet = aws.ec2.Subnet("subnet", vpc_id=vpc.id, cidr_block="", map_public_ip_on_launch=True) # Create an EKS cluster. cluster = eks.Cluster("cluster", role_arn=eks_role.arn, vpc_config=eks.ClusterVpcConfigArgs( public_subnet_ids=[subnet.id] )) # Export the cluster's kubeconfig. pulumi.export('kubeconfig', cluster.kubeconfig) # Assuming you have your YAML manifests for Kubernetes in a directory, # you could use 'kubectl' from within your CI/CD pipeline to apply the manifests. # ``` # kubectl apply -f ./k8s-manifests/ # ``` # This is where you'd incorporate your ML pipeline manifests, which might consist of # - Namespaces # - Deployments/Sts/DS for your ML workloads # - Services/Ingresses to expose the ML services # - PersistentVolumeClaims for stateful workloads # - ConfigMaps/Secrets for configuration & sensitive data # - RBAC configurations # - CustomResourceDefinitions and operators for extra functionality

    In this script, the EKS cluster is set up with a node group within a new VPC and a subnet. You would typically pass the kubeconfig output to your CI/CD system, which uses it to run kubectl commands against your cluster to apply your ML manifests.

    Make sure to replace the role_arn with the ARN of an IAM role that has permissions to create an EKS cluster, and adjust subnet configurations as necessary for your use case.

    After deploying your infrastructure with Pulumi, you would continue by setting up a GitOps tool. If you choose Flux for GitOps, you would install the Flux operator in your Kubernetes cluster and configure it to track your Git repository. Once Flux is in place, changes pushed to the relevant paths in your repository get automatically synced to the cluster.

    Remember that you will need to handle your deployment files and container images for your machine learning workloads separately, using the CI/CD pipeline of your choice to build, test, and push images to a container registry, as well as to generate and apply deployment manifests to your Kubernetes cluster.