Train a model with Ray and PyTorch on Google Kubernetes Engine (GKE)

This guide demonstrates how to train a model on Google Kubernetes Engine (GKE) using Ray, PyTorch, and the Ray Operator add-on.

About Ray

Ray is an open-source scalable compute framework for AI/ML applications. Ray Train is a component within Ray designed for distributed model training and fine-tuning. You can use the Ray Train API to scale training across multiple machines and to integrate with machine learning libraries such as PyTorch.

You can deploy Ray training jobs using the RayCluster or RayJob resource. You should use a RayJob resource when deploying Ray jobs in production for the following reasons

  • The RayJob resource creates an ephemeral Ray cluster that can be automatically deleted when a job completes.
  • The RayJob resource supports retry policies for resilient job execution.
  • You can manage Ray jobs using familiar Kubernetes API patterns.

Objectives

This guide is intended for Generative AI customers, new or existing users of GKE, ML Engineers, MLOps (DevOps) engineers, or platform administrators who are interested in using Kubernetes container orchestration capabilities for serving models using Ray.

  • Create a GKE cluster.
  • Create a Ray cluster using the RayCluster custom resource.
  • Train a model using a Ray job.
  • Deploy a Ray job using the RayJob custom resource.

Costs

In this document, you use the following billable components of Google Cloud:

To generate a cost estimate based on your projected usage, use the pricing calculator. New Google Cloud users might be eligible for a free trial.

When you finish the tasks that are described in this document, you can avoid continued billing by deleting the resources that you created. For more information, see Clean up.

Before you begin

Cloud Shell is preinstalled with the software you need for this tutorial, including kubectl, and the gcloud CLI. If you don't use Cloud Shell, you must install the gcloud CLI.

  1. Sign in to your Google Cloud account. If you're new to Google Cloud, create an account to evaluate how our products perform in real-world scenarios. New customers also get $300 in free credits to run, test, and deploy workloads.
  2. Install the Google Cloud CLI.

  3. To initialize the gcloud CLI, run the following command: 

    gcloud init

  4. Create or select a Google Cloud project.

    • Create a Google Cloud project:

      gcloud projects create PROJECT_ID

      Replace PROJECT_ID with a name for the Google Cloud project you are creating.

    • Select the Google Cloud project that you created:

      gcloud config set project PROJECT_ID

      Replace PROJECT_ID with your Google Cloud project name.

  5. Make sure that billing is enabled for your Google Cloud project.

  6. Enable the GKE API: 

    gcloud services enable container.googleapis.com
    7.
  7. Install the Google Cloud CLI.

  8. To initialize the gcloud CLI, run the following command: 

    gcloud init

  9. Create or select a Google Cloud project.

    • Create a Google Cloud project:

      gcloud projects create PROJECT_ID

      Replace PROJECT_ID with a name for the Google Cloud project you are creating.

    • Select the Google Cloud project that you created:

      gcloud config set project PROJECT_ID

      Replace PROJECT_ID with your Google Cloud project name.

  10. Make sure that billing is enabled for your Google Cloud project.

  11. Enable the GKE API: 

    gcloud services enable container.googleapis.com
    12.

  12. Grant roles to your user account. Run the following command once for each of the following IAM roles: roles/container.clusterAdmin, roles/container.admin 

    gcloud projects add-iam-policy-binding PROJECT_ID --member="USER_IDENTIFIER" --role=ROLE
    • Replace PROJECT_ID with your project ID.

    • Replace USER_IDENTIFIER with the identifier for your user account. For example, user:[email protected].

    • Replace ROLE with each individual role.
  13. Install Ray.

Prepare your environment

To prepare your environment, follow these steps:

  1. Launch a Cloud Shell session from the Google Cloud console, by clicking Cloud Shell activation icon Activate Cloud Shell in the Google Cloud console. This launches a session in the bottom pane of the Google Cloud console.

  2. Set environment variables:

    export PROJECT_ID=PROJECT_ID
export CLUSTER_NAME=ray-cluster
export COMPUTE_REGION=us-central1
export COMPUTE_ZONE=us-central1-c
export CLUSTER_VERSION=CLUSTER_VERSION
export TUTORIAL_HOME=`pwd`

    Replace the following:

    • PROJECT_ID: your Google Cloud project ID.
    • CLUSTER_VERSION: the GKE version to use. Must be 1.30.1 or later.

  3. Clone the GitHub repository:

    git clone https://github.com/GoogleCloudPlatform/kubernetes-engine-samples

  4. Change to the working directory:

    cd kubernetes-engine-samples/ai-ml/gke-ray/raytrain/pytorch-mnist

Create a GKE cluster

Create an Autopilot or Standard GKE cluster:

Autopilot

Create an Autopilot cluster:

gcloud container clusters create-auto ${CLUSTER_NAME}  \
    --enable-ray-operator \
    --cluster-version=${CLUSTER_VERSION} \
    --location=${COMPUTE_REGION}

Standard

Create a Standard cluster:

gcloud container clusters create ${CLUSTER_NAME} \
    --addons=RayOperator \
    --cluster-version=${CLUSTER_VERSION}  \
    --machine-type=e2-standard-8 \
    --location=${COMPUTE_ZONE} \
    --num-nodes=4

Deploy a RayCluster resource

Deploy a RayCluster resource to your cluster:

  1. Review the following manifest:

    apiVersion: ray.io/v1
kind: RayCluster
metadata:
  name: pytorch-mnist-cluster
spec:
  rayVersion: '2.9.0'
  headGroupSpec:
    rayStartParams:
      dashboard-host: '0.0.0.0'
    template:
      metadata:
      spec:
        containers:
        - name: ray-head
          image: rayproject/ray:2.9.0
          ports:
          - containerPort: 6379
            name: gcs
          - containerPort: 8265
            name: dashboard
          - containerPort: 10001
            name: client
          resources:
            limits:
              cpu: "2"
              memory: "4Gi"
            requests:
              cpu: "2"
              memory: "4Gi"
  workerGroupSpecs:
  - replicas: 4
    minReplicas: 1
    maxReplicas: 5
    groupName: worker-group
    rayStartParams: {}
    template:
      spec:
        containers:
        - name: ray-worker
          image: rayproject/ray:2.9.0
          resources:
            limits:
              cpu: 2
              memory: "8Gi"
            requests:
              cpu: 2
              memory: "8Gi"

    This manifest describes a RayCluster custom resource.

  2. Apply the manifest to your GKE cluster:

    kubectl apply -f ray-cluster.yaml

  3. Verify the RayCluster resource is ready:

    kubectl get raycluster

    The output is similar to the following:

    NAME                    DESIRED WORKERS   AVAILABLE WORKERS   CPUS   MEMORY   GPUS   STATUS   AGE
pytorch-mnist-cluster   2                 2                   6      20Gi     0      ready    63s

    In this output, ready in the STATUS column indicates the RayCluster resource is ready.

Connect to the RayCluster resource

Connect to the RayCluster resource to submit a Ray job.

  1. Verify that GKE created the RayCluster Service:

    kubectl get svc pytorch-mnist-cluster-head-svc

    The output is similar to the following:

    NAME                             TYPE        CLUSTER-IP       EXTERNAL-IP   PORT(S)                                AGE
pytorch-mnist-cluster-head-svc   ClusterIP   34.118.238.247   <none>        10001/TCP,8265/TCP,6379/TCP,8080/TCP   109s

  2. Establish a port-forwarding session to the Ray head:

    kubectl port-forward svc/pytorch-mnist-cluster-head-svc 8265:8265 2>&1 >/dev/null &

  3. Verify the Ray client can connect to the Ray cluster using localhost:

    ray list nodes --address http://localhost:8265

    The output is similar to the following:

    Stats:
------------------------------
Total: 3

Table:
------------------------------
    NODE_ID                                                   NODE_IP     IS_HEAD_NODE    STATE    NODE_NAME    RESOURCES_TOTAL                 LABELS
0  1d07447d7d124db641052a3443ed882f913510dbe866719ac36667d2  10.28.1.21  False           ALIVE    10.28.1.21   CPU: 2.0                        ray.io/node_id: 1d07447d7d124db641052a3443ed882f913510dbe866719ac36667d2
# Several lines of output omitted

Train a model

Train a PyTorch model using the Fashion MNIST dataset:

  1. Submit a Ray job and wait for the job to complete:

    ray job submit --submission-id pytorch-mnist-job --working-dir . --runtime-env-json='{"pip": ["torch", "torchvision"]}' --address http://localhost:8265 -- python train.py

    The output is similar to the following:

    Job submission server address: http://localhost:8265

--------------------------------------------
Job 'pytorch-mnist-job' submitted successfully
--------------------------------------------

Next steps
  Query the logs of the job:
    ray job logs pytorch-mnist-job
  Query the status of the job:
    ray job status pytorch-mnist-job
  Request the job to be stopped:
    ray job stop pytorch-mnist-job

Handling connection for 8265
Tailing logs until the job exits (disable with --no-wait):
...
...

  2. Verify the Job status:

    ray job status pytorch-mnist

    The output is similar to the following:

    Job submission server address: http://localhost:8265
Status for job 'pytorch-mnist-job': RUNNING
Status message: Job is currently running.

    Wait for the Status for job to be COMPLETE. This can take 15 minutes or longer.

  3. View Ray job logs:

    ray job logs pytorch-mnist

    The output is similar to the following:

    Training started with configuration:
╭─────────────────────────────────────────────────╮
│ Training config                                  │
├──────────────────────────────────────────────────┤
│ train_loop_config/batch_size_per_worker       8  │
│ train_loop_config/epochs                     10  │
│ train_loop_config/lr                      0.001  │
╰─────────────────────────────────────────────────╯

# Several lines omitted

Training finished iteration 10 at 2024-06-19 08:29:36. Total running time: 9min 18s
╭───────────────────────────────╮
│ Training result                │
├────────────────────────────────┤
│ checkpoint_dir_name            │
│ time_this_iter_s      25.7394  │
│ time_total_s          351.233  │
│ training_iteration         10  │
│ accuracy               0.8656  │
│ loss                  0.37827  │
╰───────────────────────────────╯

# Several lines omitted
-------------------------------
Job 'pytorch-mnist' succeeded
-------------------------------

Deploy a RayJob

The RayJob custom resource manages the lifecycle of a RayCluster resource during the execution of a single Ray job.

  1. Review the following manifest:

    apiVersion: ray.io/v1
kind: RayJob
metadata:
  name: pytorch-mnist-job
spec:
  shutdownAfterJobFinishes: true
  entrypoint: python ai-ml/gke-ray/raytrain/pytorch-mnist/train.py
  runtimeEnvYAML: |
    pip:
      - torch
      - torchvision
    working_dir: "https://github.com/GoogleCloudPlatform/kubernetes-engine-samples/archive/main.zip"
    env_vars:
      NUM_WORKERS: "4"
      CPUS_PER_WORKER: "2"
  rayClusterSpec:
    rayVersion: '2.9.0'
    headGroupSpec:
      rayStartParams: {}
      template:
        spec:
          containers:
            - name: ray-head
              image: rayproject/ray:2.9.0
              ports:
                - containerPort: 6379
                  name: gcs-server
                - containerPort: 8265
                  name: dashboard
                - containerPort: 10001
                  name: client
              resources:
                limits:
                  cpu: "2"
                  memory: "4Gi"
                requests:
                  cpu: "2"
                  memory: "4Gi"
    workerGroupSpecs:
      - replicas: 4
        minReplicas: 1
        maxReplicas: 5
        groupName: small-group
        rayStartParams: {}
        template:
          spec:
            containers:
              - name: ray-worker
                image: rayproject/ray:2.9.0
                resources:
                  limits:
                    cpu: "2"
                    memory: "8Gi"
                  requests:
                    cpu: "2"
                    memory: "8Gi"

    This manifest describes a RayJob custom resource.

  2. Apply the manifest to your GKE cluster:

    kubectl apply -f ray-job.yaml

  3. Verify the RayJob resource is running:

    kubectl get rayjob

    The output is similar to the following:

    NAME                JOB STATUS   DEPLOYMENT STATUS   START TIME             END TIME   AGE
pytorch-mnist-job   RUNNING      Running             2024-06-19T15:43:32Z              2m29s

    In this output, the DEPLOYMENT STATUS column indicates the RayJob resource is Running.

  4. View the RayJob resource status:

    kubectl logs -f -l job-name=pytorch-mnist-job

    The output is similar to the following:

    Training started with configuration:
╭─────────────────────────────────────────────────╮
│ Training config                                  │
├──────────────────────────────────────────────────┤
│ train_loop_config/batch_size_per_worker       8  │
│ train_loop_config/epochs                     10  │
│ train_loop_config/lr                      0.001  │
╰─────────────────────────────────────────────────╯

# Several lines omitted

Training finished iteration 10 at 2024-06-19 08:29:36. Total running time: 9min 18s
╭───────────────────────────────╮
│ Training result                │
├────────────────────────────────┤
│ checkpoint_dir_name            │
│ time_this_iter_s      25.7394  │
│ time_total_s          351.233  │
│ training_iteration         10  │
│ accuracy               0.8656  │
│ loss                  0.37827  │
╰───────────────────────────────╯

# Several lines omitted
-------------------------------
Job 'pytorch-mnist' succeeded
-------------------------------

  5. Verify the Ray job is complete:

    kubectl get rayjob

    The output is similar to the following:

    NAME                JOB STATUS   DEPLOYMENT STATUS   START TIME             END TIME               AGE
pytorch-mnist-job   SUCCEEDED    Complete            2024-06-19T15:43:32Z   2024-06-19T15:51:12Z   9m6s

    In this output, the DEPLOYMENT STATUS column indicates the RayJob resource is Complete.

Clean up

Delete the project

    Delete a Google Cloud project:

    gcloud projects delete PROJECT_ID

Delete individual resources

If you used an existing project and you don't want to delete it, you can delete the individual resources. To delete the cluster, type:

gcloud container clusters delete ${CLUSTER_NAME}

What's next

  • Explore reference architectures, diagrams, and best practices about Google Cloud. Take a look at our Cloud Architecture Center.