1. Kubernetes-Based Machine Learning Experimentation Platforms


    In order to set up a Kubernetes-based machine learning experimentation platform, you would typically define a Kubernetes cluster and install a machine learning workload on it. This often involves leveraging Kubernetes Operators or Helm Charts which package machine learning platforms like Kubeflow, which facilitates the deployment and management of ML workflows.

    Below is a Pulumi program in Python that creates a basic Kubernetes cluster using Amazon EKS (Elastic Kubernetes Service), and then deploys Kubeflow onto it.

    The program consists of several main steps:

    1. Create an EKS Cluster: Set up the EKS cluster, which will be the underlying infrastructure for our ML experimentation platform.
    2. Install Kubeflow: While the Pulumi program won't directly deploy Kubeflow (as its installation can be complex and might require customization), I'll show you where in the program the Kubeflow deployment command would typically be run.

    Before Kubeflow can be deployed, kubectl must be configured to communicate with the Kubernetes cluster. This is usually done outside of the Pulumi program or as a separate Pulumi component.

    For this example, we assume the necessary Pulumi packages are installed and configured.

    import pulumi import pulumi_eks as eks # Step 1: Create an EKS Cluster. # This creates an EKS cluster with the default configurations which include # default node group on AWS managed nodes, etc. # Define the name of the EKS cluster. cluster_name = 'eks-cluster' # Create an EKS cluster. cluster = eks.Cluster(cluster_name) # Export the kubeconfig to access the cluster. pulumi.export('kubeconfig', cluster.kubeconfig) # The output of the cluster, cluster.kubeconfig, will provide you with the # kubeconfig string. You will use this kubeconfig to configure kubectl for # Kubeflow installation. # Here is where you'd add the custom logic or script execution to deploy Kubeflow. # This usually involves calling `kubectl apply -f` on Kubeflow's YAML files, # which you would have to download and customize according to your requirements. # Step 2: Install Kubeflow. This step is provided as guidance and should be executed # with the appropriate Kubeflow version and configuration suited for your specific # use case. # # Example shell command after setting up the kubeconfig (outside of this Pulumi program): # `kubectl apply -k github.com/kubeflow/manifests//kfdef/kfctl_aws.v1.2.0.yaml` # After installing Kubeflow, you would apply your machine learning workloads using # the Kubeflow Pipelines, Katib for hyperparameter tuning, and other Kubeflow components.

    To guide you further on what to expect after setting up the cluster:

    • Kubeflow Installation: You would typically follow Kubeflow's AWS installation guide to set up Kubeflow on your EKS cluster. You need to ensure that kubectl is configured with the kubeconfig you obtain from the Pulumi program above.

    • Applying ML Workloads: Once Kubeflow is installed, you can start running machine learning experiments by defining pipelines in Kubeflow Pipelines, running hyperparameter tuning jobs with Katib, and leveraging other Kubeflow components such as Serving to serve your trained models.

    This program must be run with Pulumi's CLI tools after setting up the appropriate AWS credentials. The EKS cluster creation is a sensitive operation - it can incur costs and will create substantial resources in your AWS account, so it should be run with careful consideration and monitoring. After running the program, you can manage the resources via Pulumi CLI, including updating and deleting resources as required.

    Remember, the installation of Kubeflow is typically done using kubectl commands and often requires additional configuration and resource adjustments based on workload requirements. It is recommended to familiarize yourself with Kubernetes, AWS EKS, and Kubeflow before attempting to deploy a production-grade machine learning experimentation platform.