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Publish G4 GKE base blueprints
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‎examples/README.md‎

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* [gke-tpu-v6](#gke-tpu-v6--) ![community-badge] ![experimental-badge]
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* [xpk-n2-filestore](#xpk-n2-filestore--) ![community-badge] ![experimental-badge]
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* [gke-h4d](#gke-h4d-) ![core-badge]
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* [gke-g4](#gke-g4-) ![core-badge]
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* [Blueprint Schema](#blueprint-schema)
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* [Writing an HPC Blueprint](#writing-an-hpc-blueprint)
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* [Blueprint Boilerplate](#blueprint-boilerplate)
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[gke-h4d]: ../examples/gke-h4d
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### [gke-g4] ![core-badge]
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This blueprint uses GKE to provision a Kubernetes cluster and a G4 node pool, along with networks and service accounts. Information about G4 machines can be found [here](https://cloud.google.com/blog/products/compute/introducing-g4-vm-with-nvidia-rtx-pro-6000). The deployment instructions can be found in the [README](/examples/gke-g4/README.md).
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[gke-g4]: ../examples/gke-g4
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## Blueprint Schema
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Similar documentation can be found on

‎examples/gke-g4/README.md‎

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# GKE G4 Blueprint
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This blueprint uses GKE to provision a Kubernetes cluster and a G4 node pool, along with networks and service accounts. More information about G4 machines can be found here:
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* [Blog post](https://cloud.google.com/blog/products/compute/introducing-g4-vm-with-nvidia-rtx-pro-6000)
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* [Documentation](https://cloud.google.com/compute/docs/gpus#rtx-6000-gpus)
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> **_NOTE:_** The required GKE version for G4 support is >= 1.32.4-gke.1698000.
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## Steps to deploy the G4 blueprint
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1. Install Cluster Toolkit
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1. Install [dependencies](https://cloud.google.com/cluster-toolkit/docs/setup/install-dependencies).
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1. Set up [Cluster Toolkit](https://cloud.google.com/cluster-toolkit/docs/setup/configure-environment).
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1. Switch to the Cluster Toolkit directory
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```sh
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cd cluster-toolkit
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```
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1. Get the IP address for your host machine
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```sh
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curl ifconfig.me
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```
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1. Update the vars block of the `gke-g4-deployment.yaml` file.
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1. `project_id`: ID of the project where you are deploying the cluster.
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1. `deployment_name`: Name of the deployment.
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1. `region`: Compute region used for the deployment.
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1. `zone`: Compute zone used for the deployment.
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1. `machine_type`: The VM shape. See allowed values at https://cloud.google.com/compute/docs/gpus#rtx-6000-gpus.
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1. `num_gpus`: Number of GPUS in the VM. Can be found at https://cloud.google.com/compute/docs/gpus#rtx-6000-gpus.
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1. `static_node_count`: Number of nodes to create.
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1. `authorized_cidr`: update the IP address in `<your-ip-address>/32`.
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1. Build the Cluster Toolkit binary
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```sh
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make
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```
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1. Provision the GKE cluster
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```sh
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./gcluster deploy -d examples/gke-g4/gke-g4-deployment.yaml examples/gke-g4/gke-g4.yaml
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```
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These four options are displayed:
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```sh
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(D)isplay full proposed changes,
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(A)pply proposed changes,
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(S)top and exit,
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(C)ontinue without applying
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```
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Type `a` and hit enter to create the cluster.
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## Clean Up
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To destroy all resources associated with creating the GKE cluster, run the following command:
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```sh
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./gcluster destroy CLUSTER-NAME
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```
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Replace `CLUSTER-NAME` with the `deployment_name` used in the blueprint vars block.

‎examples/gke-g4/gke-g4-deployment.yaml‎

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# Copyright 2025 "Google LLC"
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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terraform_backend_defaults:
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type: gcs
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configuration:
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# The GCS bucket used for storing terraform state
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bucket:
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vars:
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# Your GCP Project ID
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project_id:
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# This should be unique across all of your Cluster Toolkit Deployments.
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deployment_name:
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# The GCP Region used for this deployment.
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region:
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# The GCP Zone used for this deployment.
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zone:
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# The VM shape. See allowed values here: https://cloud.google.com/compute/docs/gpus#rtx-6000-gpus
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machine_type:
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# Number of GPUS in the VM. Can be found here: https://cloud.google.com/compute/docs/gpus#rtx-6000-gpus
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num_gpus:
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# The number of nodes to be created
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static_node_count:
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# Cidr block containing the IP of the machine calling terraform.
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# The following line must be updated for this example to work.
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authorized_cidr:

‎examples/gke-g4/gke-g4.yaml‎

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# Copyright 2025 "Google LLC"
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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blueprint_name: gke-g4
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vars:
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# The following variables should be over-written in the deployment.yaml file.
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# Your GCP Project ID
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project_id:
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# This should be unique across all of your Cluster Toolkit Deployments.
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deployment_name: gke-g4
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# The GCP Region used for this deployment.
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region:
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# The GCP Zone used for this deployment.
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zone:
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# The VM shape. See allowed values here: https://cloud.google.com/compute/docs/gpus#rtx-6000-gpus
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machine_type:
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# Number of GPUS in the VM. Can be found here: https://cloud.google.com/compute/docs/gpus#rtx-6000-gpus
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num_gpus:
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# The number of nodes to be created.
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static_node_count:
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# Cidr block containing the IP of the machine calling terraform.
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# The following line must be updated for this example to work.
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authorized_cidr:
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system_node_pool_disk_size_gb: 100
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deployment_groups:
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- group: primary
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modules:
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- id: gke-g4-net-0
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source: modules/network/vpc
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settings:
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network_name: $(vars.deployment_name)-net-0
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subnetworks:
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- subnet_name: $(vars.deployment_name)-sub-0
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subnet_region: $(vars.region)
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subnet_ip: 192.168.0.0/18
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secondary_ranges_list:
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- subnetwork_name: $(vars.deployment_name)-sub-0
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ranges:
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- range_name: pods
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ip_cidr_range: 10.4.0.0/14
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- range_name: services
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ip_cidr_range: 10.0.32.0/20
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firewall_rules:
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- name: $(vars.deployment_name)-internal-0
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ranges: [192.168.0.0/16]
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allow:
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- protocol: tcp
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ports: ["0-65535"]
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- protocol: udp
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ports: ["0-65535"]
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- protocol: icmp
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- id: gke-g4-net-1
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source: modules/network/vpc
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settings:
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network_name: $(vars.deployment_name)-net-1
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subnetworks:
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- subnet_name: $(vars.deployment_name)-sub-1
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subnet_region: $(vars.region)
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subnet_ip: 192.168.64.0/18
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firewall_rules:
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- name: $(vars.deployment_name)-internal-1
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ranges: [192.168.0.0/16]
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allow:
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- protocol: tcp
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ports: ["0-65535"]
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- protocol: udp
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ports: ["0-65535"]
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- protocol: icmp
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- id: node_pool_service_account
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source: community/modules/project/service-account
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settings:
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name: gke-np-sa
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project_roles:
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- logging.logWriter
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- monitoring.metricWriter
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- monitoring.viewer
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- stackdriver.resourceMetadata.writer
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- storage.objectViewer
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- artifactregistry.reader
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- id: workload_service_account
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source: community/modules/project/service-account
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settings:
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name: gke-wl-sa
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project_roles:
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- logging.logWriter
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- monitoring.metricWriter
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- monitoring.viewer
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- stackdriver.resourceMetadata.writer
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- storage.objectAdmin
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- artifactregistry.reader
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- id: g4-cluster
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source: modules/scheduler/gke-cluster
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use: [gke-g4-net-0, workload_service_account]
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settings:
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system_node_pool_machine_type: "e2-standard-16"
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system_node_pool_disk_size_gb: $(vars.system_node_pool_disk_size_gb)
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system_node_pool_taints: []
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enable_dcgm_monitoring: true
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enable_private_endpoint: false # Allows access from authorized public IPs
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configure_workload_identity_sa: true
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master_authorized_networks:
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- cidr_block: $(vars.authorized_cidr) # Allows your machine to run the kubectl command. Required for multi network setup.
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display_name: "kubectl-access-network"
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additional_networks:
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$(concat(
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[{
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network=gke-g4-net-1.network_name,
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subnetwork=gke-g4-net-1.subnetwork_name,
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subnetwork_project=vars.project_id,
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nic_type="GVNIC",
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queue_count=null,
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network_ip=null,
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stack_type=null,
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access_config=[{nat_ip=null, public_ptr_domain_name=null, network_tier=null}],
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ipv6_access_config=[],
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alias_ip_range=[]
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}]
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))
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# Cluster versions cannot be updated through the toolkit after creation
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# Please manage cluster version from the Google Cloud Console directly
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version_prefix: "1.32."
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release_channel: RAPID
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maintenance_exclusions:
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- name: no-minor-or-node-upgrades-indefinite
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start_time: "2025-08-01T00:00:00Z"
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end_time: "2026-04-10T00:00:00Z"
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exclusion_scope: NO_MINOR_OR_NODE_UPGRADES
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outputs: [instructions]
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- id: g4-pool
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source: modules/compute/gke-node-pool
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use: [g4-cluster, node_pool_service_account]
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settings:
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machine_type: $(vars.machine_type)
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auto_upgrade: true
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zones: [$(vars.zone)]
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static_node_count: $(vars.static_node_count)
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guest_accelerator:
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- type: nvidia-rtx-pro-6000
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count: $(vars.num_gpus)
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gpu_driver_installation_config:
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gpu_driver_version: LATEST # TODO: Move to a fixed GPU driver version.
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additional_networks:
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$(concat(
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[{
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network=gke-g4-net-1.network_name,
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subnetwork=gke-g4-net-1.subnetwork_name,
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subnetwork_project=vars.project_id,
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nic_type="GVNIC",
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queue_count=null,
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network_ip=null,
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stack_type=null,
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access_config=[{nat_ip=null, public_ptr_domain_name=null, network_tier=null}],
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ipv6_access_config=[],
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alias_ip_range=[]
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}]
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))
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outputs: [instructions]
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# Install Kueue and Jobset
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- id: workload-manager-install
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source: modules/management/kubectl-apply
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use: [g4-cluster]
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settings:
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kueue:
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install: true
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jobset:
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install: true
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- id: nvidia_smi_job_template
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source: modules/compute/gke-job-template
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use: [g4-pool]
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settings:
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image: nvidia/cuda:13.0.0-base-ubuntu24.04
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command:
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- nvidia-smi
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node_count: $(vars.static_node_count)
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name: run-nvidia-smi
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k8s_service_account_name: workload-identity-k8s-sa
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outputs: [instructions]

‎tools/validate_configs/validate_configs.sh‎

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fi
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}
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CONFIGS=$(find examples/ community/examples/ tools/validate_configs/test_configs/ docs/tutorials/ docs/videos/build-your-own-blueprint/ -name "*.yaml" -type f -not -path 'examples/machine-learning/a3-megagpu-8g/*' -not -path 'examples/machine-learning/a3-ultragpu-8g/*' -not -path 'examples/machine-learning/build-service-images/*' -not -path 'examples/gke-a3-ultragpu/*' -not -path 'examples/hypercompute_clusters/*' -not -path 'examples/gke-consumption-options/*' -not -path 'examples/gke-a4/*' -not -path 'examples/gke-a3-megagpu/*' -not -path 'examples/machine-learning/a4-highgpu-8g/*' -not -path 'examples/machine-learning/a4x-highgpu-4g/*' -not -path 'community/examples/gke-tpu-v6/*' -not -path 'community/examples/xpk-n2-filestore/*' -not -path 'examples/gke-a4x/*' -not -path 'examples/science/af3-slurm/*' -not -path 'examples/gke-h4d/*' -not -path 'community/examples/hpc-slinky/*')
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CONFIGS=$(find examples/ community/examples/ tools/validate_configs/test_configs/ docs/tutorials/ docs/videos/build-your-own-blueprint/ -name "*.yaml" -type f -not -path 'examples/machine-learning/a3-megagpu-8g/*' -not -path 'examples/machine-learning/a3-ultragpu-8g/*' -not -path 'examples/machine-learning/build-service-images/*' -not -path 'examples/gke-a3-ultragpu/*' -not -path 'examples/hypercompute_clusters/*' -not -path 'examples/gke-consumption-options/*' -not -path 'examples/gke-a4/*' -not -path 'examples/gke-a3-megagpu/*' -not -path 'examples/machine-learning/a4-highgpu-8g/*' -not -path 'examples/machine-learning/a4x-highgpu-4g/*' -not -path 'community/examples/gke-tpu-v6/*' -not -path 'community/examples/xpk-n2-filestore/*' -not -path 'examples/gke-a4x/*' -not -path 'examples/science/af3-slurm/*' -not -path 'examples/gke-h4d/*' -not -path 'community/examples/hpc-slinky/*' -not -path 'examples/gke-g4/*')
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# Exclude blueprints that use v5 modules.
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declare -A EXCLUDE_EXAMPLE
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EXCLUDE_EXAMPLE["tools/validate_configs/test_configs/two-clusters-sql.yaml"]=

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