1. ML Experiment Tracking with Kubernetes and Unleash


    To build and manage an ML experiment tracking system on Kubernetes and integrate Unleash for feature flag management, you will first need to deploy a Kubernetes cluster, set up your machine learning tracking tool such as MLflow, and then integrate Unleash. We will follow these steps using Pulumi:

    1. Provision Kubernetes Cluster: You will use Pulumi to create a Kubernetes cluster on a cloud provider such as AWS, Azure, or GCP.
    2. Deploy MLflow: MLflow is an open-source platform to manage the ML lifecycle, including experimentation, reproducibility, and deployment. You will deploy MLflow to the Kubernetes cluster.
    3. Deploy Unleash: Unleash is an open-source feature management platform. It allows you to roll out new features in a controlled manner. You will deploy Unleash on the same Kubernetes cluster.
    4. Configure Ingress: To expose both MLflow and Unleash, you will configure Kubernetes Ingress or use a service mesh to manage traffic to the services.

    For the sake of conciseness, this explanation will focus on a high-level example using Pulumi with AWS as the cloud provider. I'm not including specific details such as security configurations, persistent storage setup, or scaling strategies that you would want to consider for a production environment.

    import pulumi from pulumi_aws import eks, iam # First, we will create a Kubernetes cluster on AWS using Amazon EKS. # We create an IAM role for the cluster and node group. eks_role = iam.Role('eksRole', assume_role_policy="""{ "Version": "2012-10-17", "Statement": [ { "Effect": "Allow", "Principal": { "Service": "eks.amazonaws.com" }, "Action": "sts:AssumeRole" } ] }""") # Then, we instantiate the cluster using the eks package, attaching the IAM role. cluster = eks.Cluster('mlCluster', role_arn=eks_role.arn, vpc_id='vpc-12345', # Replace with your VPC ID public_subnets=['subnet-12345', 'subnet-67890']) # Replace with your subnet IDs # We'll create an EKS cluster and configure kubectl to connect to the new cluster. kubeconfig = pulumi.Output.all(cluster.endpoint, cluster.certificate_authority, cluster.name).apply( lambda args: f""" apiVersion: v1 clusters: - cluster: server: {args[0]} certificate-authority-data: {args[1]['data']} name: kubernetes contexts: - context: cluster: kubernetes user: aws name: aws current-context: aws kind: Config preferences: {{}} users: - name: aws user: exec: apiVersion: client.authentication.k8s.io/v1alpha1 command: aws args: - "eks" - "get-token" - "--cluster-name" - "{args[2]}" """ ) # Now, we will install MLflow onto our cluster. For simplicity, we use a Helm chart. mlflow_chart = pulumi_kubernetes.helm.v3.Chart( 'mlflow', pulumi_kubernetes.helm.v3.ChartOpts( chart='mlflow', version='0.1.0', fetch_opts=pulumi_kubernetes.helm.v3.FetchOpts( repo='http://your-helm-chart-repo', # Replace with the Helm repo URL ), ), opts=pulumi.ResourceOptions(provider=cluster.provider), ) # Next, deploy Unleash using a similar Helm chart method. unleash_chart = pulumi_kubernetes.helm.v3.Chart( 'unleash', pulumi_kubernetes.helm.v3.ChartOpts( chart='unleash', version='1.0.0', fetch_opts=pulumi_kubernetes.helm.v3.FetchOpts( repo='http://your-helm-chart-repo', # Replace with the Helm repo URL ), ), opts=pulumi.ResourceOptions(provider=cluster.provider), ) # Expose the MLflow and Unleash services through Ingress or a Kubernetes Service. # The exact details are beyond the scope of this example and will depend on how you manage Ingress. # Finally, export the kubeconfig so that you can interact with your cluster using kubectl. pulumi.export('kubeconfig', kubeconfig)

    In this program:

    • We create an EKS cluster with required IAM roles.
    • We deploy both MLflow and Unleash using Helm charts.
    • We consider network access, potentially through Ingress (not fully defined for brevity).

    You need to replace placeholders like 'vpc-12345', 'subnet-12345' with your actual VPC and subnet IDs, and specify the correct Helm chart repositories. Also, handle the Ingress or Kubernetes Service exposure based on your preference or environment.

    Pulumi stack exports (like the kubeconfig in this case) can provide necessary information to connect to and interact with the created cluster, which you would use with kubectl or other tools to manage your services. This is a foundational step, and you would typically add more robust configuration and security details for a complete deployment.