GPU Allocation Scheduling for Deep Learning Tasks

Question

Pulumi · Accepted Answer

To allocate GPU resources and schedule deep learning tasks, you would typically use Kubernetes as the orchestrator to manage your workloads on a cluster where GPU resources are available. Pulumi's Kubernetes provider allows you to define, deploy, and manage Kubernetes resources using infrastructure as code.

Here, I will provide you with a Pulumi program that creates a Kubernetes Deployment for a deep learning task, allocating GPU resources to the pods. We'll use the Nvidia Device Plugin for Kubernetes to expose GPU resources to the Kubernetes scheduler.

First, you'll need to have a Kubernetes cluster with GPU-equipped nodes and the Nvidia Device Plugin installed. We won't cover setting up the cluster and installation in this example, but once you have that ready, you can define your workload as follows:

1. **Kubernetes Namespace**: We'll create a separate namespace for our deep learning tasks to organize our resources better.
2. **Kubernetes Deployment**: This will define our deep learning workload. It will include specification for the container image, the number of replicas, and the GPU resources each pod should request.
3. **Kubernetes Service**: To expose our deep learning task, if it provides any network services, we'll define a Kubernetes service.

Below is the Pulumi program that represents the described resources in Python:

```python
import pulumi
from pulumi_kubernetes.core.v1 import Namespace
from pulumi_kubernetes.apps.v1 import Deployment
from pulumi_kubernetes.core.v1 import Service

# Create a Kubernetes Namespace for the deep learning tasks
deep_learning_ns = Namespace("deep-learning-ns")

# Define the GPU resource name as per Kubernetes Extended Resources and Nvidia Device Plugin
gpu_resource_name = "nvidia.com/gpu"

# Define a Kubernetes Deployment for the deep learning task
deep_learning_deployment = Deployment(
    "deep-learning-deployment",
    metadata={
        "namespace": deep_learning_ns.metadata["name"],
    },
    spec={
        "selector": {
            "matchLabels": {
                "app": "deep-learning"
            }
        },
        "replicas": 1,  # Define the number of replicas/pods
        "template": {
            "metadata": {
                "labels": {
                    "app": "deep-learning"
                }
            },
            "spec": {
                "containers": [{
                    "name": "deep-learning-container",
                    "image": "tensorflow/tensorflow:latest-gpu",  # An example image with GPU support
                    "resources": {
                        "limits": {
                            gpu_resource_name: "1"  # Request 1 GPU for this container
                        }
                    }
                }]
            }
        }
    })

# Optional: Define a Service if your application needs to expose a network service
deep_learning_service = Service(
    "deep-learning-service",
    metadata={
        "namespace": deep_learning_ns.metadata["name"],
    },
    spec={
        "selector": {
            "app": "deep-learning"
        },
        "ports": [{
            "protocol": "TCP",
            "port": 80,
            "targetPort": 8080,
        }]
    })

# Export the namespace name and service name
pulumi.export("namespace_name", deep_learning_ns.metadata["name"])
pulumi.export("service_name", deep_learning_service.metadata["name"])
```

In this program:

- We've declared a **Namespace** object for our deep learning tasks, which helps in separating these resources within the cluster for better management.
- We created a **Deployment** object named `deep-learning-deployment`. Inside the deployment spec, we've specified that we want one replica of the pod. The pod's container uses a TensorFlow image with GPU support. In the resources section, we've requested one GPU using the resource name provided by the Nvidia Device Plugin.
- The **Service** object named `deep-learning-service` is an optional component that exposes your deep learning application on a network. This is useful if your tasks need to be accessible via a network service. We map port 80 on the service to port 8080 on the pod(s).

This basic configuration illustrates how to schedule a containerized deep learning task with GPU allocation in Kubernetes using Pulumi. You can customize the container properties, such as the image, commands, and number of GPUs, according to your specific needs.

Please note that Pulumi programs are run from your local machine or a CI/CD system, which must be set up with the necessary access to the Kubernetes cluster. Ensure you have `kubectl` configured with access to your Kubernetes cluster and the appropriate context selected.

This simplified example doesn't go into the specifics of managing a GPU-enabled Kubernetes cluster, setting up node affinity/anti-affinity, persistent storage, or complex networking policies, which might be necessary for production-grade machine learning infrastructure. Such configurations can be added and managed in a similar declarative manner with Pulumi.