1. Multi-Tenant Machine Learning Platforms on EKS


    To create a multi-tenant machine learning platform on Amazon EKS (Elastic Kubernetes Service), you'll need to set up an EKS cluster and configure it to host different machine learning workloads securely and efficiently. Multi-tenancy in this context means that multiple teams or users can use the cluster simultaneously, with proper isolation and resource management.

    Here's how you might build this with Pulumi using Python:

    1. EKS Cluster: Using the eks module, you can create an EKS cluster, which abstracts away much of the complexity and provides sensible defaults. For a machine learning workload, you'll likely want to customize node groups with specific types of instances suitable for compute-heavy tasks.

    2. Node Groups: To run machine learning workloads, you might want to use instances with GPU support, for which you can enable GPU nodes in the EKS cluster.

    3. Kubernetes Namespaces: For multi-tenancy, each tenant could be given their own namespace. This provides a level of isolation between the different workloads.

    4. IAM Roles and Policies: You'll need to set up IAM roles and policies for EKS so that your cluster has the right permissions within your AWS account.

    5. VPC CNI and Networking: Proper networking for the cluster is necessary to ensure isolation and security. The AWS VPC CNI plugin allows Kubernetes pods to have the same IP address inside the pod as they do on the VPC network.

    6. Security Groups: These define the network access rules for your EKS cluster.

    7. Storage: Persistent and ephemeral storage can be configured for stateful workloads like machine learning models that require dataset storage.

    8. Monitoring and Logging: AWS provides CloudWatch Logs and Metrics, which you can integrate with EKS for monitoring your cluster's performance and the status of your machine learning jobs.

    Let's translate the above into a Pulumi program. The following Python code snippet demonstrates how to create an EKS cluster using Pulumi's eks module, set up GPU-enabled node groups, and configure basic networking and IAM roles for a machine learning platform.

    import pulumi import pulumi_eks as eks # Create an EKS cluster with default settings. # For a production system, you would likely need to configure additional parameters here. cluster = eks.Cluster('ml-cluster') # Create a GPU-enabled node group for machine learning workloads. # This would likely require more configuration based on the specific needs of your workload. gpu_node_group = eks.NodeGroup('gpu-node-group', cluster=cluster.core, # Reference the EKS cluster created earlier instance_type='p2.xlarge', # Example GPU instance type desired_capacity=2, # Number of nodes you want in the node group min_size=1, max_size=4) # Set up IAM roles for the EKS cluster. # The eks.Cluster method creates a default role with the required permissions, but # you may need to create your own roles or modify permissions for advanced use cases. # Networking and VPC configuration is also handled with sensible defaults by the eks.Cluster class, # but can be customized if needed for advanced networking configurations. # Export the cluster's kubeconfig and the name of the GPU node group. # The kubeconfig will allow you to interact with your cluster via kubectl or other Kubernetes tooling. # The node group name can be used for monitoring and scaling purposes. pulumi.export('kubeconfig', cluster.kubeconfig) pulumi.export('gpu_node_group', gpu_node_group)

    In this program:

    • We initialize an EKS cluster using the eks.Cluster class. This sets up the control plane, default node group, and other necessary components. You can find more information about the eks.Cluster class here.

    • We define an EKS node group with GPU support specifying instance_type to a GPU instance like p2.xlarge, suitable for machine learning workloads. Refer to the NodeGroup documentation for more detail.

    • IAM roles and policies are crucial for providing the EKS cluster with the necessary AWS permissions. By default, the eks.Cluster will create a role with the minimum required permissions. You can customize this for granular access.

    • Networking and VPC settings have defaults managed by the eks.Cluster. You'd want to tailor this based on your organizational policies for network isolation and compliance. The VPC CNI plugin, security groups, and other network settings will need thorough review and configuration for a production setup, especially in a multi-tenant environment.

    • Finally, we output the kubeconfig for the new cluster and the GPU node group's name so you can interact with your cluster and begin deploying machine learning workloads.

    This example sets up a basic EKS cluster suitable for machine learning jobs. For a real-world multi-tenant machine learning platform, you would build upon this foundation with additional security, isolation, monitoring, and multi-tenancy policies to ensure a robust, secure environment for all users and workloads.