Yandex Managed Service for Kubernetes with AI Workloads.

Question

Pulumi · Accepted Answer

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:

```python
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.