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Running TPU training workloads on GKE

This reference guide compiles best practices, prescriptive guidance, and code samples for running large-scale machine learning training workloads with TPU v4, TPU v5p, and TPU v5e on Google Kubernetes Engine (GKE).

The guide covers two main topics:

  • Configuring a GKE based environment for large scale training on Cloud TPUs
    • This section describes how to configure a GKE cluster to optimize it for running large-scale machine learning training workloads on Cloud TPUs.
  • Defining, Submitting, and Monitoring Training Jobs
    • This section provides guidance on how to define, submit, and manage training jobs using the Kubernetes JobSet and Kueue APIs.

Architecture of the training environment

The diagram below depicts a high-level architecture of the training environment.

arch

The foundation of the environment is a regional, VPC-native GKE cluster. The cluster has two types of node pools:

This cluster topology supports running both single-slice and multislice TPU training jobs.

Following are the components supporting the environment:

Training workload processing

The following diagram illustrates the process of submitting and processing training workloads in the training environment.

training workloads

In this guide we advocate using the Kubernetes JobSet API as the preferred method of coordinating large-scale distributed machine learning training workloads on Kubernetes. When combined with the Kubernetes Kueue job queuing API, it provides flexible and comprehensive training job orchestration.

The training environment's Kueue configuration consists of a single ClusterQueue and multiple LocalQueues. This topology provides basic multi-tenancy and supports managing and prioritizing jobs submitted by multiple teams.

All training workloads are represented as JobSet resources. A JobSet resource may contain multiple job types, such as a core distributed training job and an auxiliary job that manages TensorBoard logs and other artifacts generated by the training job.

JobSet workloads are submitted to a namespaced LocalQueue that points to a ClusterQueue. As illustrated in the diagram, in our reference implementation, there is a single cluster queue.

Kueue monitors when resources (such as TPU slices) required by a workload (JobSet) are available, and then decides when to admit the workload and how to allocate the workload's components to the cluster's node pools.

For example, a training workload can contain two types of jobs:

  • A multislice distributed training job
  • A job that uploads TensorBoard logs generated by the training job to Vertex AI TensorBoard

When all the resources required by this workload become available, the training job's workers are started on the requested number of TPU slices. The TensorBoard uploader is started on one of the nodes in the CPU node pool.

If the compute resources required by other submitted workloads are not available, these workloads are queued and scheduled for admission based on the priorities that have been defined in the Kueue configuration.

To submit a JobSet-defined workload, you need to create a YAML JobSet resource definition. There are a few different ways to do this. In this guide, we demonstrate two approaches:

  • Using Kustomize, which helps you create YAML JobSet resource definitions directly.
  • Using xpk, which provides an easy-to-use Python-based CLI.

Setup

The deployment process is automated using Cloud Build, Terraform, and Kustomize. The Cloud Build configuration file defines two deployment stages:

In the first stage a Terraform configuration is applied, which:

  • Creates a network, a subnet, and IP ranges for GKE pods and services.
  • Creates a VPC-native cluster.
  • Creates a node pool with nodes equipped with CPUs only.
  • Creates a specified number of TPU node pools.
  • Creates an IAM service account for Workload Identity and an IAM service account to be used as a custom node pool service account.
  • Configures the cluster for Workload Identity.
  • Creates a Google Cloud Storage bucket.
  • Creates a Vertex TensorBoard instance
  • Creates an Artifact Registry

In the second stage, the JobSet and Kueue custom resources are installed and Kueue is configured as described in the previous section.

Warning

Your project must have sufficient quota to provision TPU resources. Else, you can request for a higher quota limit.

Configure pre-requisites

Before submitting the Cloud Build build, you need to:

  • Create a new Google Cloud project or select an existing one.
  • Enable the necessary services.
  • Configure an automation service account and an automation Google Cloud storage bucket.

The following services are required by the base environment:

  • cloudbuild.googleapis.com
  • artifactregistry.googleapis.com
  • cloudkms.googleapis.com
  • cloudresourcemanager.googleapis.com
  • container.googleapis.com
  • compute.googleapis.com
  • container.googleapis.com
  • iam.googleapis.com
  • iamcredentials.googleapis.com
  • serviceusage.googleapis.com
  • stackdriver.googleapis.com
  • storage-component.googleapis.com
  • storage.googleapis.com
  • sts.googleapis.com
  • aiplatform.googleapis.com

You also need a GCS bucket that will be used for managing Terraform state and other Terraform artifacts and a service account that will be impersonated by Terraform when provisioning the environment. The service account should have the following project level roles:

  • iam.securityAdmin
  • iam.serviceAccountAdmin
  • compute.networkAdmin
  • container.admin
  • iam.serviceAccountUser
  • storage.admin
  • artifactregistry.admin
  • aiplatform.user
  • serviceusage.serviceUsageConsumer

If you lack administrative-level permissions to enable GCP services or to create and configure service accounts in your project, your project administrator must perform these tasks. However, if you are a project owner, you can enable the services and create and configure the automation service account as part of the Configure automation settings step.

Configure automation settings

During this step, Terraform is configured to utilize the specified automation bucket and service account. Optionally, if configured, it can also enable the necessary services and create both the automation service account and the automation bucket.

  1. Clone this repo
  2. Change the current folder to environment/0-bootstrap
  3. Copy the terraform.tfvars.tmpl file to terraform.tfvars
  4. Modify the terraform.tfvars file to reflect your environment
  • project_id - your project ID
  • deletion_protection - Set to true to protect you cluster and GCS buckets from accidental deletion by Terraform apply/destroy commands. Unless this field is set to false, a terraform destroy or terraform apply that would delete the cluster or non-empty GCS buckets will fail.
  • create_automation_bucket - set to true if you want to create a new automation bucket; set to false if you want to use an existing bucket
  • automation_bucket - the name and location of a bucket you want to use for automation. If you use an existing bucket the location field will be ignored
  • create_automation_sa - set to true if you want to create a new automation service account; set to false if you want to use an existing service account
  • automation_sa_name - the name of an automation service account to be used by Terraform for impersonation
  • enable_apis - set to true if you want to enable the services listed in the services variable
  • services - the list of services to enable in your project
  • roles - the list of roles to assign to an automation services account. These roles will only be assigned to a newly created account. If you are using an existing account, this list will be ignored.
  1. Execute the terraform init command
  2. Execute the terraform apply command

The Terraform configuration generates prepopulated template files for configuring the Terraform backend and providers, which can be utilized in the following setup stages. These template files are stored in the gs://<YOUR-AUTOMATION-BUCKET/providers and gs://<YOUR-AUTOMATION-BUCKET/tfvars folders.

Grant Cloud Build impersonating rights

To be able to impersonate the automation service account, the Cloud Build service account needs to have the iam.serviceAccountTokenCreator rights on the automation service account.

AUTOMATION_SERVICE_ACCOUNT=<AUTOMATTION_SERVICE_ACOUNT_EMAIL>
CLOUD_BUILD_SERVICE_ACCOUNT=<PROJECT_NUMBER>@cloudbuild.gserviceaccount.com

gcloud iam service-accounts add-iam-policy-binding $AUTOMATION_SERVICE_ACCOUNT --member="serviceAccount:$CLOUD_BUILD_SERVICE_ACCOUNT" --role='roles/iam.serviceAccountTokenCreator'

Replace <PROJECT_NUMBER> with your project number. Replace <AUTOMATION_SERVICE_ACCOUNT_EMAIL> with the email of your automation service account. If you created the automation service account using the bootstrap Terraform you can retrieve its email by executing the terraform output automation_sa command from the environment\0-bootstrap folder.

Deploy

Clone the GitHub repo.

If you haven't already run the bootstrap stage, please clone this repository now.

git clone https://github.com/GoogleCloudPlatform/applied-ai-engineering-samples.git

Change the current directory, to ai-infrastructure/tpu-training-on-gke/environment.

Configure build parameters

To configure the Terraform steps in the build, copy the terraform.tfvars.tmpl template file in the 1-base-infrastructure folder to terraform.tfvars. Make modifications to the terraform.tfvars file to align it with your specific environment. At the very least, you should set the following variables:

  • project_id - your project ID
  • region - your region for a VPC and a GKE cluster
  • prefix - the prefix that will be added to the default names of resources provisioned by the configuration
  • tensorboard_config.region - the region of a TensorBoard instance
  • create_artifact_registry - set to true to create a new artifact registry
  • cpu_node_pools - The terraform.tfvars.tmpl template provides an example configuration for a single autoscaling node pool.
  • tpu_node_pools - The template shows an example configuration for two TPU node pools: one with a single v5e-4 pod slice and the other with a single v5e-16 pod slice. Modify the tpu_node_pools variable to provision different TPU node pool configurations, as described below.

If you wish to modify other default settings, such as the default name suffixes for a cluster or GCS bucket names, you can override the defaults specified in the variables.tf file within your terraform.tfvars file.

When configuring TPU node pools, ensure that you set the TPU type to one of the following values:

TPU types
TPU type name Slice type Slice topology TPU VM type Number of VMs in a slice Number of chips in a VM
v5litepod-4 tpu-v5-lite-podslice 2x2 ct5lp-hightpu-4t 1 4
v5litepod-16 tpu-v5-lite-podslice 4x4 ct5lp-hightpu-4t 4 4
v5litepod-32 tpu-v5-lite-podslice 4x8 ct5lp-hightpu-4t 8 4
v5litepod-64 tpu-v5-lite-podslice 8x8 ct5lp-hightpu-4t 16 4
v5litepod-128 tpu-v5-lite-podslice 8x16 ct5lp-hightpu-4t 32 4
v5litepod-256 tpu-v5-lite-podslice 16x16 ct5lp-hightpu-4t 64 4
v4-8 tpu-v4-podslice 2x2x1 ct4p-hightpu-4t 1 4
v4-16 tpu-v4-podslice 2x2x2 ct4p-hightpu-4t 2 4
v4-32 tpu-v4-podslice 2x2x4 ct4p-hightpu-4t 4 4
v4-64 tpu-v4-podslice 2x4x4 ct4p-hightpu-4t 8 4
v4-128 tpu-v4-podslice 4x4x4 ct4p-hightpu-4t 16 4
v4-256 tpu-v4-podslice 4x4x8 ct4p-hightpu-4t 32 4
v4-512 tpu-v4-podslice 4x8x8 ct4p-hightpu-4t 64 4
v4-1024 tpu-v4-podslice 8x8x8 ct4p-hightpu-4t 128 4
v4-1536 tpu-v4-podslice 8x8x12 ct4p-hightpu-4t 192 4
v4-2048 tpu-v4-podslice 8x8x16 ct4p-hightpu-4t 256 4
v4-4096 tpu-v4-podslice 8x16x16 ct4p-hightpu-4t 512 4
v5p-8 tpu-v5p-slice 2x2x1 ct5p-hightpu-4t 1 4
v5p-16 tpu-v5p-slice 2x2x2 ct5p-hightpu-4t 2 4
v5p-32 tpu-v5p-slice 2x2x4 ct5p-hightpu-4t 4 4
v5p-64 tpu-v5p-slice 2x4x4 ct5p-hightpu-4t 8 4
v5p-128 tpu-v5p-slice 4x4x4 ct5p-hightpu-4t 16 4
v5p-256 tpu-v5p-slice 4x4x8 ct5p-hightpu-4t 32 4
v5p-384 tpu-v5p-slice 4x4x12 ct5p-hightpu-4t 48 4
v5p-512 tpu-v5p-slice 4x8x8 ct5p-hightpu-4t 64 4
v5p-640 tpu-v5p-slice 4x4x20 ct5p-hightpu-4t 80 4
v5p-768 tpu-v5p-slice 4x8x12 ct5p-hightpu-4t 96 4
v5p-896 tpu-v5p-slice 4x4x28 ct5p-hightpu-4t 112 4
v5p-1024 tpu-v5p-slice 8x8x8 ct5p-hightpu-4t 128 4
v5p-1152 tpu-v5p-slice 4x12x12 ct5p-hightpu-4t 144 4
v5p-1280 tpu-v5p-slice 4x8x20 ct5p-hightpu-4t 160 4
v5p-1408 tpu-v5p-slice 4x4x44 ct5p-hightpu-4t 176 4
v5p-1536 tpu-v5p-slice 8x8x12 ct5p-hightpu-4t 192 4
v5p-1664 tpu-v5p-slice 4x4x52 ct5p-hightpu-4t 208 4
v5p-1792 tpu-v5p-slice 4x8x28 ct5p-hightpu-4t 224 4
v5p-1920 tpu-v5p-slice 4x12x20 ct5p-hightpu-4t 240 4
v5p-2048 tpu-v5p-slice 8x8x16 ct5p-hightpu-4t 256 4
v5p-2176 tpu-v5p-slice 4x4x68 ct5p-hightpu-4t 272 4
v5p-2304 tpu-v5p-slice 8x12x12 ct5p-hightpu-4t 288 4
v5p-2432 tpu-v5p-slice 4x4x76 ct5p-hightpu-4t 304 4
v5p-2560 tpu-v5p-slice 8x8x20 ct5p-hightpu-4t 320 4
v5p-2688 tpu-v5p-slice 4x12x28 ct5p-hightpu-4t 336 4
v5p-2816 tpu-v5p-slice 4x8x44 ct5p-hightpu-4t 352 4
v5p-2944 tpu-v5p-slice 4x4x92 ct5p-hightpu-4t 368 4
v5p-3072 tpu-v5p-slice 4x12x16 ct5p-hightpu-4t 384 4
v5p-3200 tpu-v5p-slice 4x20x20 ct5p-hightpu-4t 400 4
v5p-3328 tpu-v5p-slice 4x8x52 ct5p-hightpu-4t 416 4
v5p-3456 tpu-v5p-slice 12x12x12 ct5p-hightpu-4t 432 4
v5p-3584 tpu-v5p-slice 8x8x28 ct5p-hightpu-4t 448 4
v5p-3712 tpu-v5p-slice 4x4x116 ct5p-hightpu-4t 464 4
v5p-3840 tpu-v5p-slice 8x12x20 ct5p-hightpu-4t 480 4
v5p-3968 tpu-v5p-slice 4x4x124 ct5p-hightpu-4t 496 4
v5p-4096 tpu-v5p-slice 8x16x16 ct5p-hightpu-4t 512 4
v5p-4224 tpu-v5p-slice 4x12x44 ct5p-hightpu-4t 528 4
v5p-4352 tpu-v5p-slice 4x8x68 ct5p-hightpu-4t 544 4
v5p-4480 tpu-v5p-slice 4x20x28 ct5p-hightpu-4t 560 4
v5p-4608 tpu-v5p-slice 12x12x16 ct5p-hightpu-4t 576 4
v5p-4736 tpu-v5p-slice 4x4x148 ct5p-hightpu-4t 592 4
v5p-4864 tpu-v5p-slice 4x8x76 ct5p-hightpu-4t 608 4
v5p-4992 tpu-v5p-slice 4x12x52 ct5p-hightpu-4t 624 4
v5p-5120 tpu-v5p-slice 8x16x20 ct5p-hightpu-4t 640 4
v5p-5248 tpu-v5p-slice 4x4x164 ct5p-hightpu-4t 656 4
v5p-5376 tpu-v5p-slice 8x12x28 ct5p-hightpu-4t 672 4
v5p-5504 tpu-v5p-slice 4x4x172 ct5p-hightpu-4t 688 4
v5p-5632 tpu-v5p-slice 8x8x44 ct5p-hightpu-4t 704 4
v5p-5760 tpu-v5p-slice 12x12x20 ct5p-hightpu-4t 720 4
v5p-5888 tpu-v5p-slice 4x8x92 ct5p-hightpu-4t 736 4
v5p-6016 tpu-v5p-slice 4x4x188 ct5p-hightpu-4t 752 4
v5p-6144 tpu-v5p-slice 12x16x16 ct5p-hightpu-4t 768 4
v5p-6272 tpu-v5p-slice 4x28x28 ct5p-hightpu-4t 784 4
v5p-6400 tpu-v5p-slice 8x20x20 ct5p-hightpu-4t 800 4
v5p-6528 tpu-v5p-slice 4x12x68 ct5p-hightpu-4t 816 4
v5p-6656 tpu-v5p-slice 8x8x52 ct5p-hightpu-4t 832 4
v5p-6784 tpu-v5p-slice 4x4x212 ct5p-hightpu-4t 848 4
v5p-6912 tpu-v5p-slice 12x12x24 ct5p-hightpu-4t 864 4
v5p-7040 tpu-v5p-slice 4x20x44 ct5p-hightpu-4t 880 4
v5p-7168 tpu-v5p-slice 8x16x28 ct5p-hightpu-4t 896 4
v5p-7296 tpu-v5p-slice 4x12x76 ct5p-hightpu-4t 912 4
v5p-7424 tpu-v5p-slice 4x8x116 ct5p-hightpu-4t 928 4
v5p-7552 tpu-v5p-slice 4x4x236 ct5p-hightpu-4t 944 4
v5p-7680 tpu-v5p-slice 12x16x20 ct5p-hightpu-4t 960 4
v5p-7808 tpu-v5p-slice 4x4x244 ct5p-hightpu-4t 976 4
v5p-7936 tpu-v5p-slice 4x8x124 ct5p-hightpu-4t 992 4
v5p-8064 tpu-v5p-slice 12x12x28 ct5p-hightpu-4t 1008 4
v5p-8192 tpu-v5p-slice 16x16x16 ct5p-hightpu-4t 1024 4
v5p-8320 tpu-v5p-slice 4x20x52 ct5p-hightpu-4t 1040 4
v5p-8448 tpu-v5p-slice 8x12x44 ct5p-hightpu-4t 1056 4
v5p-8704 tpu-v5p-slice 8x8x68 ct5p-hightpu-4t 1088 4
v5p-8832 tpu-v5p-slice 4x12x92 ct5p-hightpu-4t 1104 4
v5p-8960 tpu-v5p-slice 8x20x28 ct5p-hightpu-4t 1120 4
v5p-9216 tpu-v5p-slice 12x16x24 ct5p-hightpu-4t 1152 4
v5p-9472 tpu-v5p-slice 4x8x148 ct5p-hightpu-4t 1184 4
v5p-9600 tpu-v5p-slice 12x20x20 ct5p-hightpu-4t 1200 4
v5p-9728 tpu-v5p-slice 8x8x76 ct5p-hightpu-4t 1216 4
v5p-9856 tpu-v5p-slice 4x28x44 ct5p-hightpu-4t 1232 4
v5p-9984 tpu-v5p-slice 8x12x52 ct5p-hightpu-4t 1248 4
v5p-10240 tpu-v5p-slice 16x16x20 ct5p-hightpu-4t 1280 4
v5p-10368 tpu-v5p-slice 12x12x36 ct5p-hightpu-4t 1296 4
v5p-10496 tpu-v5p-slice 4x8x164 ct5p-hightpu-4t 1312 4
v5p-10752 tpu-v5p-slice 12x16x28 ct5p-hightpu-4t 1344 4
v5p-10880 tpu-v5p-slice 4x20x68 ct5p-hightpu-4t 1360 4
v5p-11008 tpu-v5p-slice 4x8x172 ct5p-hightpu-4t 1376 4
v5p-11136 tpu-v5p-slice 4x12x116 ct5p-hightpu-4t 1392 4
v5p-11264 tpu-v5p-slice 8x16x44 ct5p-hightpu-4t 1408 4
v5p-11520 tpu-v5p-slice 12x20x24 ct5p-hightpu-4t 1440 4
v5p-11648 tpu-v5p-slice 4x28x52 ct5p-hightpu-4t 1456 4
v5p-11776 tpu-v5p-slice 8x8x92 ct5p-hightpu-4t 1472 4
v5p-11904 tpu-v5p-slice 4x12x124 ct5p-hightpu-4t 1488 4
v5p-12032 tpu-v5p-slice 4x8x188 ct5p-hightpu-4t 1504 4
v5p-12160 tpu-v5p-slice 4x20x76 ct5p-hightpu-4t 1520 4
v5p-12288 tpu-v5p-slice 16x16x24 ct5p-hightpu-4t 1536 4
v5p-13824 tpu-v5p-slice 12x24x24 ct5p-hightpu-4t 1728 4
v5p-17920 tpu-v5p-slice 16x20x28 ct5p-hightpu-4t 2240 4
Modify Workload Identity and Kueue configurations

By default the following names and identifiers are used when configuring Workload Identity Federation and Kueue

  • The IAM service account for WID - <prefix>-wid-sa
  • The Kubernetes service account - wid-ksa
  • The Cluster Queue name - cluster-queue
  • The Local Queue name - tpu-training-jobs
  • The Namespace for WID Kubernetes accoutn and Local Queue - tpu-training

If you want to change these defaults, create a terraform.tfvars file in the 2-gke-config and override the default values from the environment/2-gke-config/variables.tf file.

Submit the build

To initiate the build, execute the following command:

export PROJECT_ID=<PROJECT_ID>
export AUTOMATION_BUCKET=<YOUR_AUTOMATION_BUCKET>
export AUTOMATION_ACCOUNT=<YOUR_AUTOMATION_ACCOUNT>
export ENV_NAME=<ENV_STATE_FOLDER> 
export JOBSET_API_VERSION=v0.3.0
export KUEUE_API_VERSION=v0.5.3 

gcloud builds submit \
  --project $PROJECT_ID \
  --config cloudbuild.provision.yaml \
  --substitutions _JOBSET_API_VERSION=$JOBSET_API_VERSION,_KUEUE_API_VERSION=$KUEUE_API_VERSION,_AUTOMATION_BUCKET=$AUTOMATION_BUCKET,_ENV_NAME=$ENV_NAME,_AUTOMATION_ACCOUNT=$AUTOMATION_ACCOUNT \
  --timeout "2h" \
  --machine-type=e2-highcpu-32

Replace the following values:

  • <PROJECT_ID> with your project ID
  • <YOUR_AUTOMATION_BUCKET> with your automation bucket
  • <YOUR_AUTOMATION_ACCOUNT> with you automation service account
  • <ENV_STATE_FOLDER> with the name of the folder within your automation bucket where Terraform state and other artifacts will be managed

The examples in this repo have been tested with v0.4.0 version of the JobSet API and v0.5.3 version of the Kueue API.

To track the progress of the build, you can either follow the link displayed in Cloud Shell or visit the Cloud Build page on the Google Cloud Console.

Training workloads examples

The examples folder contains code samples that demonstrate how to configure, submit and manage a number of different training workloads.

Refer to the README in the examples folder for detailed instructions.

Cleanup Environment

To destroy the environment and clean up all the provisioned resources:

export PROJECT_ID=<PROJECT_ID>
export AUTOMATION_BUCKET=<YOUR_AUTOMATION_BUCKET>
export ENV_NAME=<TF_STATE_FOLDER>

gcloud builds submit \
  --project $PROJECT_ID \
  --config cloudbuild.destroy.yaml \
  --substitutions _AUTOMATION_BUCKET=$AUTOMATION_BUCKET,_ENV_NAME=$ENV_NAME \
  --timeout "2h" \
  --machine-type=e2-highcpu-32