Distributed Training Workloads on Kubernetes Clusters

Question

Pulumi · Accepted Answer

To accomplish distributed training workloads on Kubernetes clusters using Pulumi and Python, we will follow several steps:

1. **Create the Kubernetes cluster**: The core of our setup, where we will schedule our distributed training jobs.
2. **Define the distributed training job**: Outline the containers, configuration, and resources needed for the distributed training workload.
3. **Deploy the job on the cluster**: Apply the defined configurations to the cluster to start the training.

In this guide, I'll demonstrate how to achieve this on AWS using Amazon EKS (Elastic Kubernetes Service), which is a fully managed Kubernetes service. We will deploy a Kubernetes cluster in AWS and then deploy a sample distributed training job using Kubernetes resources such as `Deployment`, `StatefulSet`, `Job`, or custom resources if working with a framework like Kubeflow.

Here is the program that sets up a Kubernetes cluster and deploys a mock distributed training job. In a real-world scenario, you would need to replace the placeholder job configuration with your actual training script and dependencies.

```python
import pulumi
import pulumi_aws as aws
import pulumi_kubernetes as k8s

# Create an EKS cluster with default settings.
# We will have different options to customize the EKS cluster if needed, such as the version, 
# size and type of instances for the worker nodes, and the desired capacity.
cluster = aws.eks.Cluster("eks-cluster")

# Create a Kubernetes Provider pointing to the created cluster.
# The provider is used for deploying resources to the cluster.
k8s_provider = k8s.Provider("k8s-provider", kubeconfig=cluster.kubeconfig.apply(lambda c: c))

# A distributed training job typically consists of multiple pods running the same workload in parallel.
# Kubernetes Jobs are suitable for this purpose. The number of completions can be used to control the level of parallelism.
# Here is an example of a Kubernetes Job that would be used for a distributed training scenario.
training_job = k8s.batch.v1.Job(
    "training-job",
    spec=k8s.batch.v1.JobSpecArgs(
        completions=3,  # Assuming we want three parallel workers for training.
        parallelism=3,  # The job will run with up to 3 Pods running concurrently.
        template=k8s.core.v1.PodTemplateSpecArgs(
            spec=k8s.core.v1.PodSpecArgs(
                containers=[k8s.core.v1.ContainerArgs(
                    name="training-container",
                    image="YOUR_TRAINING_IMAGE",  # Use your custom image with training logic here.
                    command=["python", "train.py"]  # Replace this command with the one your container requires.
                )],
                restart_policy="Never",
            ),
        ),
    ),
    opts=pulumi.ResourceOptions(provider=k8s_provider)
)

# Export the cluster's kubeconfig.
pulumi.export("kubeconfig", cluster.kubeconfig)
```

### Explanation:

- **EKS Cluster**: We use the `aws.eks.Cluster` class to create a new managed Kubernetes cluster. AWS EKS offloads a lot of the heavy lifting when it comes to managing Kubernetes, such as the control plane.

- **Kubernetes Provider**: The `pulumi_kubernetes.Provider` class connects to the created EKS cluster to manage Kubernetes resources. We use the cluster's kubeconfig as the credentials to access the cluster.

- **Kubernetes Job**: The `pulumi_kubernetes.batch.v1.Job` class represents a batch job in Kubernetes. Jobs are ideal for batch or background tasks that run to completion, which is often the case with distributed training tasks. The `completions` attribute specifies the desired number of successfully finished pods, and `parallelism` controls the maximum number of pods that can run in parallel during the job execution.

- **Container Image**: Within the job specification, you define the container image that holds your training code (`YOUR_TRAINING_IMAGE`). This should be replaced with the image URL of your training application.

- **Training Script**: Replace the `command` with the command that needs to be run within the container to start the distributed training. In this example, it's a Python script named `train.py`.

- **Resource Options**: Usage of `ResourceOptions` in pulumi enables specifying additional options; here, we're tying the Kubernetes resources to a specific provider that manages the resources in the designated EKS cluster.

Please note that this is a simplified example. Depending on the training job's requirements, you might need to configure additional parameters, such as resource requests/limits for CPU and memory, volume mounts for data access, and configuring environment variables.

Remember to replace `YOUR_TRAINING_IMAGE` with the actual image URL and adjust the `command` according to what your container requires to start the training process. Additionally, make sure your AWS credentials are configured properly for Pulumi to interact with AWS services.