1. GPU Utilization Tracking in Kubernetes for Deep Learning


    To track GPU utilization in a Kubernetes cluster, specifically for deep learning workloads, you can use multiple Kubernetes resources and Pulumi to manage these resources and automate tasks. Kubernetes does not natively support GPU resources, so you typically need to set up your cluster with specific configurations that allow Kubernetes to schedule GPU workloads and track their utilization.

    Here are the steps we will take to accomplish GPU utilization tracking in Kubernetes for deep learning:

    1. Set up a Kubernetes cluster with GPU nodes: Make sure your cluster has nodes with GPU capabilities. This often involves choosing the right machine types and installing the necessary drivers and Kubernetes device plugins.

    2. Define ResourceQuotas: This is a Kubernetes resource that helps manage GPU resource utilization by setting constraints on the GPU resources a namespace can request and use.

    3. Use Metrics Server & Custom Metrics: Deploy the Kubernetes Metrics Server to expose resource utilization metrics. For GPU metrics, you can use a custom metrics server like Nvidia's DCGM Exporter, which provides GPU usage metrics that you can scrape using Prometheus.

    4. Horizontal Pod Autoscaling (HPA): With custom metrics available from a GPU metrics server, you can set up HPA to automatically scale the number of pods in a deployment based on these custom GPU usage metrics.

    Now, let's write a Pulumi program to manage a ResourceQuota in Python. This will assume that the cluster is already set up with GPU nodes and that you have installed a tool to collect GPU metrics. We will not handle that setup in this program, but it's important to note that it's a prerequisite for the following code to be meaningful.

    import pulumi import pulumi_kubernetes as kubernetes # Define a GPU resource quota in the Kubernetes cluster to manage and monitor resource utilization. # This assumes you have a namespace 'gpu-workloads' which will run your deep learning pods. gpu_resource_quota = kubernetes.core.v1.ResourceQuota( "gpu-resource-quota", metadata=kubernetes.meta.v1.ObjectMetaArgs(name="gpu-resource-quota", namespace="gpu-workloads"), spec=kubernetes.core.v1.ResourceQuotaSpecArgs( # Here you can define quotas for various resources, including custom GPU resources. # For instance, "nvidia.com/gpu" is often used as a resource name for NVIDIA GPUs. hard={ "nvidia.com/gpu": "4", # Limits the total number of GPUs allowed in the namespace to 4. "requests.memory": "64Gi", # Limits the total memory requests across all pods in the namespace. "limits.memory": "128Gi", # Limits the total memory limits across all pods in the namespace. }, ), ) # Export the resource quota's name for easy access if needed. pulumi.export("gpu_resource_quota", gpu_resource_quota.metadata["name"])

    This Pulumi program creates a ResourceQuota that limits the number of GPUs and memory that can be used by pods in the gpu-workloads namespace. Adjust the limits as per your requirements.

    It's important to note that while ResourceQuota is essential, monitoring actual GPU utilization in real-time and using it for autoscaling would require additional resources such as the Kubernetes Metrics Server and custom metrics adapters like Nvidia's GPU metrics. These would be set up outside of this Pulumi program and can be managed by Pulumi if desired.

    To integrate real-time GPU metrics into your Kubernetes workload management, you would typically use the HorizontalPodAutoscaler resource from Kubernetes, configuring it to scale based on your custom GPU metrics. However, configuring HPA with custom metrics in Pulumi is complex and depends heavily on your specific metrics stack, so it's not included here.

    Before running this program with Pulumi, ensure you've set up your Pulumi CLI and the Kubernetes provider. When you're ready, run pulumi up in your CLI to deploy this to your cluster.