Kubernetes-Distributed Training of Machine Learning Models

Question

Pulumi · Accepted Answer

To achieve Kubernetes-distributed training of machine learning models using Pulumi, we will walk through the necessary components and the steps involved in setting up such an environment on a cloud provider. Our approach will utilize Google Cloud Platform (GCP) to create a Kubernetes cluster using GKE (Google Kubernetes Engine), and then we will deploy a machine learning training job to this cluster.

Below is a comprehensive guide combined with a Pulumi program written in Python to perform these steps.

Pre-requisite

Before we dive into the Pulumi program, ensure that you've installed the Pulumi CLI and set up the GCP configuration.

Components used:

GKE (Google Kubernetes Engine): Google's managed Kubernetes service, which simplifies the creation and management of a Kubernetes cluster.
Kubernetes Jobs: Kubernetes jobs manage the batch processing, which is a perfect fit for ML model training that can be executed as a batch workload.
Machine Learning Training Job: This will be a Docker container with all the necessary dependencies and your machine learning code that can be scaled and distributed across the Kubernetes cluster.

Steps in the Pulumi program:

Import Dependencies: Import the necessary Pulumi packages for GCP and Kubernetes.
GKE Cluster Creation: Create a GKE cluster using the pulumi_gcp.container.Cluster class.
Kubernetes Provider: After the GKE cluster setup, initiate a Kubernetes provider associated with the created GKE cluster to deploy Kubernetes resources.
Deployment of ML Training Job: Define a Kubernetes Job resource using pulumi_kubernetes.batch.v1.Job that encapsulates the container running your ML training code.
Exposing the Job Results: An explanation will be given for retrieving the logs or output from the ML training job, which is an essential part of a machine learning workflow.

The following Pulumi program does all this:

import pulumi
from pulumi_gcp import container
import pulumi_kubernetes as k8s

# Configurations to possibly allow user customization - training image, cluster settings, job settings, etc.
training_container_image = "gcr.io/<your-gcp-project-id>/ml-training-job:latest"  # Replace with your container image URL

# Create a GKE cluster
gke_cluster = container.Cluster("ml-gke-cluster",
                                initial_node_count=3,
                                node_config={
                                    "machineType": "n1-standard-1",  # Adjust based on computational needs
                                    "oauthScopes": [
                                        "https://www.googleapis.com/auth/compute",
                                        "https://www.googleapis.com/auth/devstorage.read_only",
                                        "https://www.googleapis.com/auth/logging.write",
                                        "https://www.googleapis.com/auth/monitoring",
                                    ],
                                })

# Create a Kubernetes provider instance with the created GKE cluster credentials
k8s_provider = k8s.Provider("k8s-provider", kubeconfig=gke_cluster.kubeconfig)

# Define the Kubernetes Job to perform distributed machine learning training
job_labels = {"job": "ml-training"}
ml_training_job = k8s.batch.v1.Job("ml-training-job",
                                   spec=k8s.batch.v1.JobSpecArgs(
                                       template=k8s.core.v1.PodTemplateSpecArgs(
                                           metadata=k8s.meta.v1.ObjectMetaArgs(labels=job_labels),
                                           spec=k8s.core.v1.PodSpecArgs(
                                               containers=[k8s.core.v1.ContainerArgs(
                                                   name="ml-training-container",
                                                   image=training_container_image,  # The image containing ML training code
                                               )],
                                               restart_policy="Never",  # Since training jobs are not long-running services
                                           )
                                       ),
                                       backoff_limit=2,  # Specify the number of retries for a failed job
                                   ),
                                   opts=pulumi.ResourceOptions(provider=k8s_provider))

# Output the GKE cluster name and Kubernetes Job name
pulumi.export("gke_cluster_name", gke_cluster.name)
pulumi.export("ml_training_job_name", ml_training_job.metadata["name"])

In the above program:

The GKE cluster is created with a specified number of nodes. Depending on your ML training requirements, you may adjust initial_node_count and machineType.
The Kubernetes provider is set up with the GKE cluster information to communicate with it.
A Kubernetes Job is defined with a container that should have your ML code and dependencies baked in. You'll need to replace <your-gcp-project-id> with your actual GCP project ID and <your-container-image> with the URL of the container image that contains your machine learning code.
Upon deployment, this job will trigger the ML model training on the Kubernetes cluster.

To see the output of your machine learning model training, you can use kubectl to access the logs of the terminated pods (since restart_policy is set to "Never").

Retrieve logs command example:

kubectl logs <job-pod-name>

Remember to replace <job-pod-name> with the actual name of the Pod created for the job, which you can find by listing the pods related to the job using kubectl get pods --selector=job=ml-training.