1. Secured Cluster Communications for EKS AI Workloads


    Creating secure cluster communications for EKS (Elastic Kubernetes Service) AI workloads in AWS involves several steps. The goal is to ensure the communication between different services within the cluster is encrypted and follows best practices in terms of security. We’ll use Pulumi's Python language SDK to provision the necessary resources securely.

    Firstly, we’ll create an Amazon EKS cluster, which provides the Kubernetes control plane. Then, we’ll configure the cluster to use AWS Identity and Access Management (IAM) roles for service accounts. This will give fine-grained permission control to the Kubernetes pods running the AI workloads.

    After setting up the cluster, we’ll touch on how to use a Kubernetes Network Policy to secure pod-to-pod communications within the cluster. To further bolster security, we can integrate AWS services like App Mesh or configure mutual TLS (mTLS) for pod communications, but these are advanced topics we won't cover in detail here.

    Here's what each part of the code does:

    1. Cluster Creation: We'll use the eks.Cluster class from the Pulumi EKS Python SDK to create a new EKS cluster. The class takes various parameters such as the desired Kubernetes version and the number of nodes.

    2. IAM Roles for Service Accounts (IRSA): We'll create an IAM role and attach policies that grant the necessary permissions for our AI workloads. With IRSA, pods that require AWS service access can assume an IAM role that grants those permissions.

    3. Kubernetes Network Policies: Although not created by default within the code below, network policies are essential for securing communication within the cluster. They allow us to control traffic flow at the IP address or port level.

    Below is the program that creates a secure EKS cluster suitable for running AI workloads. The code sets up the cluster with public access disabled for the Kubernetes API server (to reduce surface area for potential attacks), and by default, no additional configurations are added for network policies or mTLS.

    For AI workloads, one might want to adjust the instanceType and desiredCapacity based on the computing needs of the workload.

    import pulumi import pulumi_eks as eks # Create a new EKS cluster with the desired configuration cluster = eks.Cluster('ai-workload-cluster', desired_capacity=2, # Define the desired number of worker nodes min_size=1, # Define the minimum number of worker nodes (can scale down to this size) max_size=3, # Define the maximum number of worker nodes (can scale up to this size) instance_type="m5.large", # Choose an instance type for your worker nodes version='1.21', # Specify the version of Kubernetes ) # Once your cluster is created, you can use `kubeconfig` to interact with it using kubectl kubeconfig = cluster.kubeconfig.apply(lambda c: c.raw) # This output provides the kubeconfig as a sensitive output to mask it in the Pulumi Console pulumi.export('kubeconfig', pulumi.Output.secret(kubeconfig))

    Keep in mind this is a starting point. To ramp up the security for AI workloads, you may need additional configurations like:

    • Encrypted storage using Kubernetes Secrets or integrations with systems like AWS KMS.
    • Fine-tuning RBAC (Role-Based Access Control) policies for different user roles interacting with the EKS cluster.
    • Making use of service meshes, like AWS App Mesh, which can secure and control the communication between your microservices.

    Remember to operate within the security best practices advised by AWS and Kubernetes when running sensitive workloads on EKS.

    Please ensure you have the Pulumi CLI installed and AWS credentials configured, as this script will communicate with your AWS account to create resources. Once you're ready, run the script using the pulumi up command. This will start the provisioning process in your AWS account. If you're using Pulumi for the first time, it'll prompt you to create a new stack, which is a logical grouping of resources.