1. Kubernetes GPU Nodes for Training Deep Learning Models


    To create a Kubernetes cluster with GPU nodes suitable for training deep learning models, you would typically need to follow these steps using Pulumi:

    1. Create a Managed Kubernetes Cluster resource depending on the cloud provider (AWS EKS, GCP GKE, Azure AKS, etc.).
    2. Define a node pool with GPU-enabled instances as part of the Kubernetes cluster.
    3. Configure the necessary Kubernetes resources, such as namespaces, limits, or quotas, for working with GPUs.
    4. Apply any required cloud-specific configurations, such as IAM roles or service accounts, that allow the instances to work with GPUs.

    For illustration purposes, let's consider creating a GPU node pool in Google Kubernetes Engine (GKE) with Pulumi in Python. Google Cloud Platform offers the possibility to create nodes with attached GPUs that can be used for computation-intensive tasks like training deep learning models.

    Below is the Python program that demonstrates how to create a GKE cluster and add a node pool with GPUs using Pulumi. In this example, we will:

    • Use gcp.container.Cluster to create a new GKE cluster.
    • Use gcp.container.NodePool to create a new node pool with specific GPU machine types (e.g., n1-standard-8 with nvidia-tesla-k80 GPUs).
    • Configure the node pool with necessary Kubernetes labels and taints to ensure that only GPU-specific workloads are scheduled on these nodes.

    Here is the Pulumi program to perform these tasks:

    import pulumi import pulumi_gcp as gcp # Specify the desired settings for the GKE cluster. cluster = gcp.container.Cluster("gpu-cluster", initial_node_count=1, min_master_version="latest", node_version="latest", location="us-west1-a") # Create a GKE node pool with GPU-enabled nodes for deep learning workloads. gpu_node_pool = gcp.container.NodePool("gpu-node-pool", cluster=cluster.name, location=cluster.location, initial_node_count=1, autoscaling={ "min_node_count": 1, "max_node_count": 5, }, management={ "auto_repair": True, }, node_config={ "machine_type": "n1-standard-8", # Example machine type, adjust as necessary. "oauth_scopes": [ "https://www.googleapis.com/auth/logging.write", "https://www.googleapis.com/auth/monitoring", ], "accelerators": [{ "accelerator_count": 1, "accelerator_type": "nvidia-tesla-k80", # Specify the desired GPU type. }], "tags": ["deep-learning", "gpu"], # These tags help identify the GPU nodes. "labels": {"usage": "gpu"}, # These labels can be used to schedule GPU workloads. "taints": [{ "key": "nvidia.com/gpu", "value": "present", "effect": "NoSchedule", # This taint prevents non-GPU workloads from being scheduled on GPU nodes. }], }, depends_on=[cluster]) # Export the cluster name and the GPU node pool name for easy access later. pulumi.export("cluster_name", cluster.name) pulumi.export("gpu_node_pool_name", gpu_node_pool.name)

    This program does the following:

    • Defines a GKE cluster resource, setting up the initial number of nodes, the Kubernetes versions, and the location for the cluster.
    • Specifies a node pool with GPU acceleration, setting the cluster to which the node pool belongs, the initial number of nodes, and autoscaling parameters.
    • Configures the node pool with machine types, OAuth scopes for access to logging and monitoring, the type and count of GPUs per node.
    • Adds metadata such as tags and labels for identifying and scheduling nodes, as well as taints to ensure proper workload scheduling.
    • The depends_on parameter ensures the node pool is created only after the cluster is successfully provisioned.
    • Exports the cluster and node pool names so that they can be accessed outside of Pulumi for management and configuration purposes.

    Please note that the specific values such as n1-standard-8 for machine types and nvidia-tesla-k80 for accelerator types should be chosen based on your workload requirements and the availability in your chosen region. Additionally, some settings such as IAM permissions or enabling the GPU hardware drivers may need to be configured separately depending on your cloud provider's requirements or for specific Kubernetes configurations.