1. Yandex Managed Service for Kubernetes with AI Workloads.


    To deploy AI workloads on Yandex Managed Service for Kubernetes (Yandex Kubernetes Engine), we'll need to create a Kubernetes cluster, configure its nodes to fit the requirements of AI workloads (such as allocating sufficient CPU, memory, and potentially GPU resources), and then deploy the applications.

    To get started, we will create a program using Pulumi's Python SDK that sets up a Yandex Kubernetes Cluster. This cluster will then be the environment where you can deploy your AI workloads.

    The following resources and components will be used:

    • yandex.KubernetesCluster: This resource will be used to create and manage a Kubernetes cluster within Yandex Cloud.
    • Nodes configuration: While defining the cluster, we can specify the types of machines (nodes) we want, which can be tailored to the demands of AI workloads. For example, we can choose machines with GPUs that are often required for intensive AI computations.

    Let's create a Pulumi program to set up a Kubernetes cluster in Yandex Cloud. This program will detail all essential steps, and I will explain each one through comments in the code. Please note that you'll need to have Pulumi installed along with the Yandex Cloud provider. Also, the configurations such as the folder ID, subnet ID, and service account ID must be set up beforehand according to your Yandex Cloud account details.

    Here is the Pulumi program to define a managed Kubernetes cluster in Yandex Cloud suitable for AI workloads:

    import pulumi import pulumi_yandex as yandex # Replace these variables with your own values folder_id = "your-folder-id" # Yandex Cloud folder ID where the Kubernetes cluster will be deployed subnet_id = "your-subnet-id" # ID of the subnet where the Kubernetes cluster should operate service_account_id = "your-service-account-id" # ID of the service account for the Kubernetes cluster zone = "your-zone-id" # Desired availability zone for the Kubernetes cluster # Create a managed Kubernetes cluster in Yandex Cloud k8s_cluster = yandex.KubernetesCluster("ai-k8s-cluster", name="ai-k8s-cluster", folder_id=folder_id, network_id=subnet_id, service_account_id=service_account_id, master=yandex.KubernetesClusterMasterArgs( # Master configuration, e.g. zonal for a single-zone setup or regional for multiple availability zones zonal=yandex.KubernetesClusterMasterZonalArgs( zone=zone, subnet_id=subnet_id, ), # You can set other parameters for the master, like version or public IP here ), # Node group configuration to determine the size and type of worker nodes node_group=yandex.KubernetesClusterNodeGroupArgs( # Name and count configuration for the node group name="ai-node-group", count=2, # Specify the resources per node, adjust these according to the requirements of your AI workloads resources=yandex.KubernetesClusterNodeGroupResourcesArgs( memory=8, cores=2, ), # You can set other parameters like disk size and type, preemptible node usage, labels, and taints here ) ) # Export the cluster name and ID as stack outputs pulumi.export("cluster_name", k8s_cluster.name) pulumi.export("cluster_id", k8s_cluster.id)

    In this program, we set up a modest Kubernetes cluster with a single master node and a node group consisting of two worker nodes, each with a specific amount of memory and CPU cores. This is a starting point, and based on your AI workload requirements, you might need to increase the allocated resources or add specific configurations like GPU support.

    To deploy AI workloads, you would typically containerize your AI applications, push the container images to a registry, and then create Kubernetes deployments that reference those images. This is done using Kubernetes manifests or a continuous deployment tool that interacts with your Kubernetes cluster.