Fine-tune and scale reinforcement learning with NVIDIA NeMo RL on GKE

Autopilot Standard

This tutorial shows you how to orchestrate a distributed training environment for reinforcement learning (RL) on Google Kubernetes Engine (GKE). You use Ray and the NVIDIA NeMo RL framework to set up a distributed training environment to fine-tune a model.

This tutorial focuses on the Group Relative Policy Optimization (GRPO) training pipeline on GKE with Ray and NeMo RL. GRPO is a reinforcement learning algorithm designed to improve a model's reasoning ability. This memory-efficient algorithm simplifies the RL process by eliminating the Critic, or value model , and using a relative group-based calculation instead.

Before you run this tutorial, we recommend that you complete the Fine-tune and scale reinforcement learning with verl on GKE tutorial. The following tutorial uses the same cluster setup and configuration as the fine-tuning and scaling RL with verl tutorial.

Background

The following sections provide a brief overview of the concepts used in this tutorial.

Reinforcement learning (RL)

RL teaches models through experience, exploration, and feedback rather than static imitation. Although pre-training teaches a model what to say, reinforcement learning from human feedback (RLHF) teaches it how to be helpful, safe, and logical. RL serves as the bridge between a base model and a fine-tuned model for a specialized use case.

For more information, see What is reinforcement learning?

Group Relative Policy Optimization (GRPO)

GRPO , an algorithm popularized by DeepSeek, offers a memory-efficient alternative to Proximal Policy Optimization (PPO) for LLM alignment by removing the Critic model. Instead of a Critic network, GRPO generates a group of responses for the same prompt and uses the average reward of that group as the baseline.

For more information, see GRPO .

NVIDIA NeMo RL

NeMo RL is NVIDIA's open-source post-training library designed for scalable RL. Part of the broader NeMo framework ecosystem, NeMo RL enables both small-scale experiments on a single GPU and multi-node deployments across thousands of GPUs.

For more information, see NVIDIA NeMo RL .

GSM8k dataset

In this tutorial, you use the GSM8k dataset, which contains 8,500 high-quality, linguistically diverse grade-school-math word problems problems.

By using GSM8k and GRPO, the model generates a group of n different responses for the same problem. GRPO compares these responses against the group average. The model is rewarded more for paths that are consistently correct and logically sound compared to the rest of the group. Over time, the model learns that articulating its steps clearly is the most reliable way to maximize reward, effectively reducing the reward for low-performance answers.

For more information, see GSM8k .

Objectives

This tutorial shows you how to set up RL on GKE with NeMo RL by completing the following steps:

Prepare your environment.
Set up a GKE cluster with B200 or H200 GPUs.
Configure KubeRay to manage a distributed Ray cluster.
Use Managed Lustre for high-performance storage.
Run a GRPO training job that uses NeMo RL.

Before you begin

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.

Install the Google Cloud CLI.

Note:If you installed the gcloud CLI previously, make sure you have the latest version by running gcloud components update .

If you're using an external identity provider (IdP), you must first sign in to the gcloud CLI with your federated identity .

To initialize the gcloud CLI, run the following command:

gcloud  
init

Create or select a Google Cloud project .

Roles required to select or create a project

Select a project : Selecting a project doesn't require a specific IAM role—you can select any project that you've been granted a role on.
Create a project : To create a project, you need the Project Creator role ( roles/resourcemanager.projectCreator ), which contains the resourcemanager.projects.create permission. Learn how to grant roles .

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.

Verify that billing is enabled for your Google Cloud project .

Enable the required APIs:

Roles required to enable APIs

To enable APIs, you need the Service Usage Admin IAM role ( roles/serviceusage.serviceUsageAdmin ), which contains the serviceusage.services.enable permission. Learn how to grant roles .

gcloud  
services  
 enable 
  
container.googleapis.com  
 storage.googleapis.com  
 compute.googleapis.com

Install the Google Cloud CLI.

Note:If you installed the gcloud CLI previously, make sure you have the latest version by running gcloud components update .

If you're using an external identity provider (IdP), you must first sign in to the gcloud CLI with your federated identity .

To initialize the gcloud CLI, run the following command:

gcloud  
init

Create or select a Google Cloud project .

Roles required to select or create a project

Select a project : Selecting a project doesn't require a specific IAM role—you can select any project that you've been granted a role on.
Create a project : To create a project, you need the Project Creator role ( roles/resourcemanager.projectCreator ), which contains the resourcemanager.projects.create permission. Learn how to grant roles .

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.

Verify that billing is enabled for your Google Cloud project .

Enable the required APIs:

Roles required to enable APIs

To enable APIs, you need the Service Usage Admin IAM role ( roles/serviceusage.serviceUsageAdmin ), which contains the serviceusage.services.enable permission. Learn how to grant roles .

gcloud  
services  
 enable 
  
container.googleapis.com  
 storage.googleapis.com  
 compute.googleapis.com

Grant roles to your user account. Run the following command once for each of the following IAM roles: roles/container.admin, roles/iam.serviceAccountAdmin, roles/storage.admin
```
gcloud  
projects  
add-iam-policy-binding  
 PROJECT_ID 
  
--member = 
 "user: USER_IDENTIFIER 
" 
  
--role = 
 ROLE 
```
Replace the following:
- PROJECT_ID : Your project ID.
- USER_IDENTIFIER : The identifier for your user account. For example, myemail@example.com .
- ROLE : The IAM role that you grant to your user account.

Create a Hugging Face account, if you don't already have one.
Ensure that you have a Hugging Face token .
Ensure your project has sufficient quota for B200 and H200 GPUs. To learn more, see Plan GPU quota and GPU quota .

Prepare your environment

In this tutorial, you use Cloud Shell .

Go to the Google Cloud console .
At the top of the Google Cloud console window, click the Activate Cloud Shellbutton.
Set the following environment variables:
```
  export 
  
 PROJECT_ID 
 = 
 $( 
gcloud  
config  
get  
project ) 
 export 
  
 PROJECT_NUMBER 
 = 
 $( 
gcloud  
projects  
describe  
 ${ 
 PROJECT_ID 
 } 
  
--format = 
 "value(projectNumber)" 
 ) 
 export 
  
 CONTROL_PLANE_LOCATION 
 = 
 CONTROL_PLANE_LOCATION 
 export 
  
 NODE_LOCATION 
 = 
 NODE_LOCATION 
 export 
  
 CLUSTER_NAME 
 = 
 CLUSTER_NAME 
 export 
  
 GPU_TYPE 
 = 
 GPU_TYPE 
 export 
  
 MACHINE_TYPE 
 = 
 MACHINE_TYPE 
 export 
  
 GKE_VERSION 
 = 
 GKE_VERSION 
 export 
  
 KSA_NAME 
 = 
generic-ksa export 
  
 NAMESPACE 
 = 
default export 
  
 GS_BUCKET 
 = 
 BUCKET_NAME 
- ${ 
 PROJECT_ID 
 } 
 export 
  
 HF_TOKEN 
 = 
 YOUR_HUGGING_FACE_TOKEN 
 
```
Replace the following values:
- CLUSTER_NAME : the name of your GKE cluster.
- CONTROL_PLANE_LOCATION : the Compute Engine region for the GKE cluster control plane.
- NODE_LOCATION : the location for your nodes. Select a zone where NVIDIA B200 or H200 GPUs are available .
- GPU_TYPE : the accelerator that you reserved in the Compute Engine capacity reservation. Must be one of the following values:
  - nvidia-b200 : NVIDIA B200 (180 GB)
  - nvidia-h200-141gb : NVIDIA H200 (141 GB)
- MACHINE_TYPE : the type of machine to use:
  - For NVIDIA B200 (180 GB) GPUs, use a4-highgpu-8g or later.
  - For NVIDIA H200 (141 GB) GPUs, use a3-ultragpu-8g or later.
- GKE_VERSION : the version of GKE to use:
  - For NVIDIA B200 (180 GB) GPUs, use 1.32.2-gke.1422000 or later.
  - For NVIDIA H200 (141 GB) GPUs, use 1.31.4-gke.1183000 or later.
- BUCKET_NAME : the base name for your Cloud Storage bucket.
- YOUR_HUGGING_FACE_TOKEN : your Hugging Face token.
Create the following environment variables for the network:
```
  export 
  
 GVNIC_NETWORK_PREFIX 
 = 
 "  GVNIC-NAME 
 
 " 
 
 export 
  
 RDMA_NETWORK_PREFIX 
 = 
 "  RDMA-NAME 
 
 " 
 
 
```
Replace the following values:
- GVNIC-NAME : the prefix for the gVNIC network name. You can use any prefix you want.
- RDMA-NAME : the prefix for the remote direct memory access (RDMA) network. You can use any prefix you want.

Set up infrastructure

In this section, you create VPC networks and a GKE cluster.

Create a VPC network

Create a VPC network for the gVNIC interface:

 gcloud  
compute  
networks  
create  
 ${ 
 GVNIC_NETWORK_PREFIX 
 } 
-net  
 \ 
  
--project = 
 ${ 
 PROJECT_ID 
 } 
  
 \ 
  
--subnet-mode = 
custom
gcloud  
compute  
networks  
subnets  
create  
 ${ 
 GVNIC_NETWORK_PREFIX 
 } 
-sub  
 \ 
  
--network = 
 ${ 
 GVNIC_NETWORK_PREFIX 
 } 
-net  
 \ 
  
--location = 
 ${ 
 CONTROL_PLANE_LOCATION 
 } 
  
 \ 
  
--range = 
 192 
.168.0.0/24
gcloud  
compute  
firewall-rules  
create  
 ${ 
 GVNIC_NETWORK_PREFIX 
 } 
-internal  
 \ 
  
--network = 
 ${ 
 GVNIC_NETWORK_PREFIX 
 } 
-net  
 \ 
  
--action = 
ALLOW  
 \ 
  
--rules = 
tcp:0-65535,udp:0-65535,icmp  
 \ 
  
--source-ranges = 
 192 
.168.0.0/16

Create a VPC network and subnets for RDMA that includes eight subnets for eight GPUs:

 gcloud  
compute  
networks  
create  
 ${ 
 RDMA_NETWORK_PREFIX 
 } 
-net  
 \ 
  
--network-profile = 
 ${ 
 CONTROL_PLANE_LOCATION 
 } 
-vpc-roce  
 \ 
  
--subnet-mode = 
custom for 
  
N  
 in 
  
 $( 
seq  
 0 
  
 7 
 ) 
 ; 
  
 do 
  
gcloud  
compute  
networks  
subnets  
create  
 ${ 
 RDMA_NETWORK_PREFIX 
 } 
-sub- $N 
  
 \ 
  
--network = 
 ${ 
 RDMA_NETWORK_PREFIX 
 } 
-net  
 \ 
  
--location = 
 ${ 
 CONTROL_PLANE_LOCATION 
 } 
  
 \ 
  
--range = 
 192 
.168. $(( 
 N 
 + 
 1 
 )) 
.0/24  
& done 
 wait

Create the GKE cluster

You can set NeMo RL in a GKE Autopilot or Standard cluster. We recommend that you use a Autopilot cluster for a fully managed Kubernetes experience. To choose the GKE mode of operation that's the best fit for your workloads, see About GKE modes of operation .

Autopilot

Create an Autopilot cluster:

 gcloud  
container  
clusters  
create-auto  
 ${ 
 CLUSTER_NAME 
 } 
  
 \ 
  
--location = 
 ${ 
 CONTROL_PLANE_LOCATION 
 } 
  
 \ 
  
--enable-multi-networking  
 \ 
  
--enable-ray-operator

Get credentials for your cluster:

 gcloud  
container  
clusters  
get-credentials  
 ${ 
 CLUSTER_NAME 
 } 
  
 \ 
  
--location = 
 ${ 
 CONTROL_PLANE_LOCATION 
 }

Install the NCCL RDMA installer for Autopilot:

 kubectl  
apply  
-f  
https://raw.githubusercontent.com/GoogleCloudPlatform/container-engine-accelerators/refs/heads/master/gpudirect-rdma/nccl-rdma-installer-autopilot.yaml

Standard

Create a Standard cluster:

 gcloud  
container  
clusters  
create  
 ${ 
 CLUSTER_NAME 
 } 
  
 \ 
  
--location = 
 ${ 
 CONTROL_PLANE_LOCATION 
 } 
  
 \ 
  
--enable-dataplane-v2  
 \ 
  
--enable-ip-alias  
 \ 
  
--enable-multi-networking  
 \ 
  
--addons = 
RayOperator  
 \ 
  
--num-nodes = 
 1

Get credentials for your cluster:

 gcloud  
container  
clusters  
get-credentials  
 ${ 
 CLUSTER_NAME 
 } 
  
 \ 
  
--location = 
 ${ 
 CONTROL_PLANE_LOCATION 
 }

Create the GPU node pool:

 gcloud  
container  
node-pools  
create  
gpu-pool  
 \ 
  
--cluster = 
 ${ 
 CLUSTER_NAME 
 } 
  
 \ 
  
--node-locations = 
 ${ 
 NODE_LOCATION 
 } 
  
 \ 
  
--machine-type = 
 ${ 
 MACHINE_TYPE 
 } 
  
 \ 
  
--accelerator = 
 type 
 = 
 ${ 
 GPU_TYPE 
 } 
,count = 
 8 
  
 \ 
  
--spot  
 \ 
  
--additional-node-network = 
 network 
 = 
 ${ 
 GVNIC_NETWORK_PREFIX 
 } 
-net,subnetwork = 
 ${ 
 GVNIC_NETWORK_PREFIX 
 } 
-sub  
 \ 
  
--additional-node-network = 
 network 
 = 
 ${ 
 RDMA_NETWORK_PREFIX 
 } 
-net,subnetwork = 
 ${ 
 RDMA_NETWORK_PREFIX 
 } 
-sub-0  
 \ 
  
--additional-node-network = 
 network 
 = 
 ${ 
 RDMA_NETWORK_PREFIX 
 } 
-net,subnetwork = 
 ${ 
 RDMA_NETWORK_PREFIX 
 } 
-sub-1  
 \ 
  
--additional-node-network = 
 network 
 = 
 ${ 
 RDMA_NETWORK_PREFIX 
 } 
-net,subnetwork = 
 ${ 
 RDMA_NETWORK_PREFIX 
 } 
-sub-2  
 \ 
  
--additional-node-network = 
 network 
 = 
 ${ 
 RDMA_NETWORK_PREFIX 
 } 
-net,subnetwork = 
 ${ 
 RDMA_NETWORK_PREFIX 
 } 
-sub-3  
 \ 
  
--additional-node-network = 
 network 
 = 
 ${ 
 RDMA_NETWORK_PREFIX 
 } 
-net,subnetwork = 
 ${ 
 RDMA_NETWORK_PREFIX 
 } 
-sub-4  
 \ 
  
--additional-node-network = 
 network 
 = 
 ${ 
 RDMA_NETWORK_PREFIX 
 } 
-net,subnetwork = 
 ${ 
 RDMA_NETWORK_PREFIX 
 } 
-sub-5  
 \ 
  
--additional-node-network = 
 network 
 = 
 ${ 
 RDMA_NETWORK_PREFIX 
 } 
-net,subnetwork = 
 ${ 
 RDMA_NETWORK_PREFIX 
 } 
-sub-6  
 \ 
  
--additional-node-network = 
 network 
 = 
 ${ 
 RDMA_NETWORK_PREFIX 
 } 
-net,subnetwork = 
 ${ 
 RDMA_NETWORK_PREFIX 
 } 
-sub-7

Install the NCCL RDMA installer:

 kubectl  
apply  
-f  
https://raw.githubusercontent.com/GoogleCloudPlatform/container-engine-accelerators/refs/heads/master/gpudirect-rdma/nccl-rdma-installer.yaml

Configure network mappings

Save the following manifest as network-mapping.yaml :

  # Copyright 2026 Google LLC. All rights reserved. 
 # 
 # Licensed under the Apache License, Version 2.0 (the "License"); 
 # you may not use this file except in compliance with the License. 
 # You may obtain a copy of the License at 
 # 
 #     http://www.apache.org/licenses/LICENSE-2.0 
 # 
 # Unless required by applicable law or agreed to in writing, software 
 # distributed under the License is distributed on an "AS IS" BASIS, 
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 
 # See the License for the specific language governing permissions and 
 # limitations under the License. 
 apiVersion 
 : 
  
 networking.gke.io/v1 
 kind 
 : 
  
 GKENetworkParamSet 
 metadata 
 : 
  
 name 
 : 
  
 gvnic-1 
 spec 
 : 
  
 vpc 
 : 
  
 ${GVNIC_NETWORK_PREFIX}-net 
  
 vpcSubnet 
 : 
  
 ${GVNIC_NETWORK_PREFIX}-sub 
  
 deviceMode 
 : 
  
 NetDevice 
 --- 
 apiVersion 
 : 
  
 networking.gke.io/v1 
 kind 
 : 
  
 Network 
 metadata 
 : 
  
 name 
 : 
  
 gvnic-1 
 spec 
 : 
  
 type 
 : 
  
 "Device" 
  
 parametersRef 
 : 
  
 group 
 : 
  
 networking.gke.io 
  
 kind 
 : 
  
 GKENetworkParamSet 
  
 name 
 : 
  
 gvnic-1 
 --- 
 apiVersion 
 : 
  
 networking.gke.io/v1 
 kind 
 : 
  
 GKENetworkParamSet 
 metadata 
 : 
  
 name 
 : 
  
 rdma-0 
 spec 
 : 
  
 vpc 
 : 
  
 ${RDMA_NETWORK_PREFIX}-net 
  
 vpcSubnet 
 : 
  
 ${RDMA_NETWORK_PREFIX}-sub-0 
  
 deviceMode 
 : 
  
 RDMA 
 --- 
 apiVersion 
 : 
  
 networking.gke.io/v1 
 kind 
 : 
  
 Network 
 metadata 
 : 
  
 name 
 : 
  
 rdma-0 
 spec 
 : 
  
 type 
 : 
  
 "Device" 
  
 parametersRef 
 : 
  
 group 
 : 
  
 networking.gke.io 
  
 kind 
 : 
  
 GKENetworkParamSet 
  
 name 
 : 
  
 rdma-0 
 --- 
 apiVersion 
 : 
  
 networking.gke.io/v1 
 kind 
 : 
  
 GKENetworkParamSet 
 metadata 
 : 
  
 name 
 : 
  
 rdma-1 
 spec 
 : 
  
 vpc 
 : 
  
 ${RDMA_NETWORK_PREFIX}-net 
  
 vpcSubnet 
 : 
  
 ${RDMA_NETWORK_PREFIX}-sub-1 
  
 deviceMode 
 : 
  
 RDMA 
 --- 
 apiVersion 
 : 
  
 networking.gke.io/v1 
 kind 
 : 
  
 Network 
 metadata 
 : 
  
 name 
 : 
  
 rdma-1 
 spec 
 : 
  
 type 
 : 
  
 "Device" 
  
 parametersRef 
 : 
  
 group 
 : 
  
 networking.gke.io 
  
 kind 
 : 
  
 GKENetworkParamSet 
  
 name 
 : 
  
 rdma-1 
 --- 
 apiVersion 
 : 
  
 networking.gke.io/v1 
 kind 
 : 
  
 GKENetworkParamSet 
 metadata 
 : 
  
 name 
 : 
  
 rdma-2 
 spec 
 : 
  
 vpc 
 : 
  
 ${RDMA_NETWORK_PREFIX}-net 
  
 vpcSubnet 
 : 
  
 ${RDMA_NETWORK_PREFIX}-sub-2 
  
 deviceMode 
 : 
  
 RDMA 
 --- 
 apiVersion 
 : 
  
 networking.gke.io/v1 
 kind 
 : 
  
 Network 
 metadata 
 : 
  
 name 
 : 
  
 rdma-2 
 spec 
 : 
  
 type 
 : 
  
 "Device" 
  
 parametersRef 
 : 
  
 group 
 : 
  
 networking.gke.io 
  
 kind 
 : 
  
 GKENetworkParamSet 
  
 name 
 : 
  
 rdma-2 
 --- 
 apiVersion 
 : 
  
 networking.gke.io/v1 
 kind 
 : 
  
 GKENetworkParamSet 
 metadata 
 : 
  
 name 
 : 
  
 rdma-3 
 spec 
 : 
  
 vpc 
 : 
  
 ${RDMA_NETWORK_PREFIX}-net 
  
 vpcSubnet 
 : 
  
 ${RDMA_NETWORK_PREFIX}-sub-3 
  
 deviceMode 
 : 
  
 RDMA 
 --- 
 apiVersion 
 : 
  
 networking.gke.io/v1 
 kind 
 : 
  
 Network 
 metadata 
 : 
  
 name 
 : 
  
 rdma-3 
 spec 
 : 
  
 type 
 : 
  
 "Device" 
  
 parametersRef 
 : 
  
 group 
 : 
  
 networking.gke.io 
  
 kind 
 : 
  
 GKENetworkParamSet 
  
 name 
 : 
  
 rdma-3 
 --- 
 apiVersion 
 : 
  
 networking.gke.io/v1 
 kind 
 : 
  
 GKENetworkParamSet 
 metadata 
 : 
  
 name 
 : 
  
 rdma-4 
 spec 
 : 
  
 vpc 
 : 
  
 ${RDMA_NETWORK_PREFIX}-net 
  
 vpcSubnet 
 : 
  
 ${RDMA_NETWORK_PREFIX}-sub-4 
  
 deviceMode 
 : 
  
 RDMA 
 --- 
 apiVersion 
 : 
  
 networking.gke.io/v1 
 kind 
 : 
  
 Network 
 metadata 
 : 
  
 name 
 : 
  
 rdma-4 
 spec 
 : 
  
 type 
 : 
  
 "Device" 
  
 parametersRef 
 : 
  
 group 
 : 
  
 networking.gke.io 
  
 kind 
 : 
  
 GKENetworkParamSet 
  
 name 
 : 
  
 rdma-4 
 --- 
 apiVersion 
 : 
  
 networking.gke.io/v1 
 kind 
 : 
  
 GKENetworkParamSet 
 metadata 
 : 
  
 name 
 : 
  
 rdma-5 
 spec 
 : 
  
 vpc 
 : 
  
 ${RDMA_NETWORK_PREFIX}-net 
  
 vpcSubnet 
 : 
  
 ${RDMA_NETWORK_PREFIX}-sub-5 
  
 deviceMode 
 : 
  
 RDMA 
 --- 
 apiVersion 
 : 
  
 networking.gke.io/v1 
 kind 
 : 
  
 Network 
 metadata 
 : 
  
 name 
 : 
  
 rdma-5 
 spec 
 : 
  
 type 
 : 
  
 "Device" 
  
 parametersRef 
 : 
  
 group 
 : 
  
 networking.gke.io 
  
 kind 
 : 
  
 GKENetworkParamSet 
  
 name 
 : 
  
 rdma-5 
 --- 
 apiVersion 
 : 
  
 networking.gke.io/v1 
 kind 
 : 
  
 GKENetworkParamSet 
 metadata 
 : 
  
 name 
 : 
  
 rdma-6 
 spec 
 : 
  
 vpc 
 : 
  
 ${RDMA_NETWORK_PREFIX}-net 
  
 vpcSubnet 
 : 
  
 ${RDMA_NETWORK_PREFIX}-sub-6 
  
 deviceMode 
 : 
  
 RDMA 
 --- 
 apiVersion 
 : 
  
 networking.gke.io/v1 
 kind 
 : 
  
 Network 
 metadata 
 : 
  
 name 
 : 
  
 rdma-6 
 spec 
 : 
  
 type 
 : 
  
 "Device" 
  
 parametersRef 
 : 
  
 group 
 : 
  
 networking.gke.io 
  
 kind 
 : 
  
 GKENetworkParamSet 
  
 name 
 : 
  
 rdma-6 
 --- 
 apiVersion 
 : 
  
 networking.gke.io/v1 
 kind 
 : 
  
 GKENetworkParamSet 
 metadata 
 : 
  
 name 
 : 
  
 rdma-7 
 spec 
 : 
  
 vpc 
 : 
  
 ${RDMA_NETWORK_PREFIX}-net 
  
 vpcSubnet 
 : 
  
 ${RDMA_NETWORK_PREFIX}-sub-7 
  
 deviceMode 
 : 
  
 RDMA 
 --- 
 apiVersion 
 : 
  
 networking.gke.io/v1 
 kind 
 : 
  
 Network 
 metadata 
 : 
  
 name 
 : 
  
 rdma-7 
 spec 
 : 
  
 type 
 : 
  
 "Device" 
  
 parametersRef 
 : 
  
 group 
 : 
  
 networking.gke.io 
  
 kind 
 : 
  
 GKENetworkParamSet 
  
 name 
 : 
  
 rdma-7

Apply the manifest:

 kubectl  
apply  
-f  
network-mapping.yaml

Prepare storage

In this section, you create Cloud Storage buckets and a Managed Lustre instance, which provisions the high-performance storage required for your RL workload.

Create a Cloud Storage bucket:

 gcloud  
storage  
buckets  
create  
gs:// ${ 
 GS_BUCKET 
 } 
  
 \ 
  
--location = 
 ${ 
 CONTROL_PLANE_LOCATION 
 } 
  
 \ 
  
--enable-hierarchical-namespace  
 \ 
  
--uniform-bucket-level-access

Create a Kubernetes Service Account (KSA) and bind it to the bucket:

 kubectl  
create  
serviceaccount  
 ${ 
 KSA_NAME 
 } 
  
--namespace  
 ${ 
 NAMESPACE 
 } 
gcloud  
storage  
buckets  
add-iam-policy-binding  
gs:// ${ 
 GS_BUCKET 
 } 
  
 \ 
  
--member  
 "principal://iam.googleapis.com/projects/ 
 ${ 
 PROJECT_NUMBER 
 } 
 /locations/global/workloadIdentityPools/ 
 ${ 
 PROJECT_ID 
 } 
 .svc.id.goog/subject/ns/ 
 ${ 
 NAMESPACE 
 } 
 /sa/ 
 ${ 
 KSA_NAME 
 } 
 " 
  
 \ 
  
--role  
 "roles/storage.objectUser"

Set up Managed Lustre by completing the following steps:
1. Create a Managed Lustre instance by following the steps in the Create a Managed Lustre instance . Ensure that the instance uses the same network as your GKE cluster.
2. Access the Managed Lustre instance by following the steps in Access an existing Managed Lustre instance .

Deploy RayCluster

In this section, you clone the sample repository, prepare the manifests, and run a launcher.sh script:

Clone the sample repository:

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

Navigate to the working directory:
```
  cd 
  
ai-ml/nemo-rl-on-gke/nemoRL 
```

Inspect the values.yaml manifest:

  # Copyright 2026 Google LLC 
 # 
 # Licensed under the Apache License, Version 2.0 (the "License"); 
 # you may not use this file except in compliance with the License. 
 # You may obtain a copy of the License at 
 # 
 #     http://www.apache.org/licenses/LICENSE-2.0 
 # 
 # Unless required by applicable law or agreed to in writing, software 
 # distributed under the License is distributed on an "AS IS" BASIS, 
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 
 # See the License for the specific language governing permissions and 
 # limitations under the License. 
 image 
 : 
  
 repository 
 : 
  
 "nvcr.io/nvidia/nemo-rl" 
  
 tag 
 : 
  
 "v0.5.0" 
  
  
 pullPolicy 
 : 
  
 Always 
 nameOverride 
 : 
  
 "kuberay" 
 fullnameOverride 
 : 
  
 "" 
 common 
 : 
  
 containerEnv 
 : 
  
 {} 
 configMap 
 : 
  
 fluentbit 
 : 
  
 data 
 : 
  
 fluent-bit.conf 
 : 
  
 | 
  
 [INPUT] 
  
 Name              tail 
  
 Path              /tmp/ray/session_latest/logs/worker-* 
  
 Tag               ray-worker 
  
 [INPUT] 
  
 Name              tail 
  
 Path              /tmp/ray/session_latest/logs/raylet* 
  
 Tag               raylet 
  
 [INPUT] 
  
 Name              tail 
  
 Path              /tmp/ray/session_latest/logs/* 
  
 Exclude_Path      /tmp/ray/session_latest/logs/debug_state.txt,/tmp/ray/session_latest/logs/raylet*,/tmp/ray/session_latest/logs/worker-* 
  
 Tag               ray-misc 
  
 [OUTPUT] 
  
 Name              stackdriver 
  
 Match             * 
  
 resource          gce_instance 
  
 labels_key        labels 
 # --- Head Node Configuration --- 
 head 
 : 
  
 enableInTreeAutoscaling 
 : 
  
 false 
  
 serviceAccountName 
 : 
  
 "" 
  
 rayStartParams 
 : 
  
 dashboard-host 
 : 
  
 '0.0.0.0' 
  
 template 
 : 
  
 metadata 
 : 
  
 annotations 
 : 
  
 gke-gcsfuse/volumes 
 : 
  
 "true" 
  
 networking.gke.io/default-interface 
 : 
  
 'eth0' 
  
 containerEnv 
 : 
  
 - 
  
 name 
 : 
  
 RAY_GROUP 
  
 value 
 : 
  
 "head" 
  
 resources 
 : 
  
 limits 
 : 
  
 cpu 
 : 
  
 "64" 
  
 memory 
 : 
  
 "500G" 
  
 nvidia.com/gpu 
 : 
  
 0 
  
 requests 
 : 
  
 cpu 
 : 
  
 "64" 
  
 memory 
 : 
  
 "500G" 
  
 nvidia.com/gpu 
 : 
  
 0 
  
 tolerations 
 : 
  
 # - operator: "Exists" 
  
 #   key: "components.gke.io/gke-managed-components" 
  
 # - key: "nvidia.com/gpu" 
  
 #   operator: "Exists" 
  
 #   effect: "NoSchedule" 
  
 volumeMounts 
 : 
  
 - 
  
 mountPath 
 : 
  
 /data 
  
 name 
 : 
  
 lustre-data 
  
 volumes 
 : 
  
 - 
  
 name 
 : 
  
 log-volume 
  
 emptyDir 
 : 
  
 {} 
  
 - 
  
 name 
 : 
  
 fluentbit-config-volume 
  
 configMap 
 : 
  
 name 
 : 
  
 "ray-cluster-kuberay-fluentbit-config" 
  
 - 
  
 name 
 : 
  
 lustre-data 
  
 persistentVolumeClaim 
 : 
  
 claimName 
 : 
  
 lustre-pvc 
  
 sidecarContainers 
 : 
  
 - 
  
 name 
 : 
  
 fluent-bit 
  
 image 
 : 
  
 fluent/fluent-bit:latest 
  
 env 
 : 
  
 - 
  
 name 
 : 
  
 RAY_GROUP 
  
 value 
 : 
  
 "head" 
  
 volumeMounts 
 : 
  
 - 
  
 name 
 : 
  
 fluentbit-config-volume 
  
 mountPath 
 : 
  
 /fluent-bit/etc/ 
  
 - 
  
 mountPath 
 : 
  
 /tmp/ray 
  
 name 
 : 
  
 log-volume 
  
 # --- HEAD POD STARTUP SCRIPT --- 
  
 command 
 : 
  
 - 
  
 "bash" 
  
 - 
  
 "-c" 
  
 - 
  
 | 
  
 set -ex 
  
 echo "--- Head Pod Setup ---" 
  
 apt-get update 
  
 apt-get install -y sudo netcat-openbsd pciutils 
  
 cd /opt/nemo-rl 
  
 /usr/bin/python -m pip install uv 
  
 /usr/bin/python -m uv venv 
  
 echo "Head pod setup complete. Starting Ray..." 
  
 exec ${KUBERAY_GEN_RAY_START_CMD} 
  
 args 
 : 
  
 [] 
  
 headService 
 : 
  
 {} 
  
 # nodeSelector: 
  
 #   cloud.google.com/gke-accelerator: nvidia-b200 #cloud.google.com/gke-nodepool: cpu-node-pool-llama #cpu-node-pool 
 # --- Default Worker (Disabled) --- 
 worker 
 : 
  
 disabled 
 : 
  
 true 
 # --- A4 GPU Worker Groups --- 
 additionalWorkerGroups 
 : 
  
 worker-grp-0 
 : 
  
 disabled 
 : 
  
 false 
  
 replicas 
 : 
  
 4 
  
 annotations 
 : 
  
 networking.gke.io/default-interface 
 : 
  
 'eth0' 
  
 networking.gke.io/interfaces 
 : 
  
 | 
  
 [ 
  
 {"interfaceName":"eth0","network":"default"}, 
  
 {"interfaceName":"eth1","network":"gvnic-1"}, 
  
 {"interfaceName":"eth2","network":"rdma-0"}, 
  
 {"interfaceName":"eth3","network":"rdma-1"}, 
  
 {"interfaceName":"eth4","network":"rdma-2"}, 
  
 {"interfaceName":"eth5","network":"rdma-3"}, 
  
 {"interfaceName":"eth6","network":"rdma-4"}, 
  
 {"interfaceName":"eth7","network":"rdma-5"}, 
  
 {"interfaceName":"eth8","network":"rdma-6"}, 
  
 {"interfaceName":"eth9","network":"rdma-7"} 
  
 ] 
  
 containerEnv 
 : 
  
 - 
  
 name 
 : 
  
 RAY_GROUP 
  
 valueFrom 
 : 
  
 fieldRef 
 : 
  
 fieldPath 
 : 
  
 metadata.labels['ray.io/group'] 
  
 - 
  
 name 
 : 
  
 NCCL_NET 
  
  
 value 
 : 
  
 "gIB" 
  
 - 
  
 name 
 : 
  
 NCCL_IB_GID_INDEX 
  
 value 
 : 
  
 "3" 
  
  
 - 
  
 name 
 : 
  
 GLOO_SOCKET_IFNAME 
  
 value 
 : 
  
 "eth0" 
  
 - 
  
 name 
 : 
  
 NCCL_CROSS_NIC 
  
 value 
 : 
  
 "0" 
  
 - 
  
 name 
 : 
  
 NCCL_SOCKET_IFNAME 
  
 value 
 : 
  
 "eth0" 
  
 - 
  
 name 
 : 
  
 TP_SOCKET_IFNAME 
  
 # Specific to DTensor/PyTorch Distributed 
  
 value 
 : 
  
 "eth0" 
  
 - 
  
 name 
 : 
  
 NCCL_TUNER_CONFIG_PATH 
  
 value 
 : 
  
 "/usr/local/gib/configs/tuner_config_a4.txtpb" 
  
 - 
  
 name 
 : 
  
 NCCL_NET_GDR_LEVEL 
  
 value 
 : 
  
 "PIX" 
  
 - 
  
 name 
 : 
  
 LD_LIBRARY_PATH 
  
 value 
 : 
  
 /usr/local/nvidia/lib64 
  
 resources 
 : 
  
 limits 
 : 
  
 nvidia.com/gpu 
 : 
  
 8 
  
 cpu 
 : 
  
 "206" 
  
 memory 
 : 
  
 "2400Gi" 
  
 requests 
 : 
  
 nvidia.com/gpu 
 : 
  
 8 
  
 cpu 
 : 
  
 "206" 
  
 memory 
 : 
  
 "2400Gi" 
  
 nodeSelector 
 : 
  
 cloud.google.com/gke-accelerator 
 : 
  
 nvidia-b200 
  
 tolerations 
 : 
  
 - 
  
 operator 
 : 
  
 "Exists" 
  
 key 
 : 
  
 "nvidia.com/gpu" 
  
 - 
  
 operator 
 : 
  
 "Exists" 
  
 key 
 : 
  
 "cloud.google.com/impending-node-termination" 
  
 - 
  
 operator 
 : 
  
 "Exists" 
  
 key 
 : 
  
 "user-workload" 
  
 securityContext 
 : 
  
 privileged 
 : 
  
 true 
  
 volumes 
 : 
  
 - 
  
 name 
 : 
  
 log-volume 
  
 emptyDir 
 : 
  
 {} 
  
 - 
  
 name 
 : 
  
 shared-memory 
  
 emptyDir 
 : 
  
 medium 
 : 
  
 "Memory" 
  
 sizeLimit 
 : 
  
 240Gi 
  
 - 
  
 name 
 : 
  
 ray-tmp 
  
 emptyDir 
 : 
  
 medium 
 : 
  
 "Memory" 
  
 - 
  
 name 
 : 
  
 fluentbit-config-volume 
  
 configMap 
 : 
  
 name 
 : 
  
 "ray-cluster-kuberay-fluentbit-config" 
  
 - 
  
 name 
 : 
  
 nvidia-install-dir-host 
  
 hostPath 
 : 
  
 path 
 : 
  
 /home/kubernetes/bin/nvidia 
  
 - 
  
 name 
 : 
  
 gib-nccl-plugin-volume 
  
 hostPath 
 : 
  
  
 path 
 : 
  
 /home/kubernetes/bin/gib 
  
 - 
  
 name 
 : 
  
 lustre-data 
  
 persistentVolumeClaim 
 : 
  
 claimName 
 : 
  
 lustre-pvc 
  
 volumeMounts 
 : 
  
 - 
  
 mountPath 
 : 
  
 /tmp/ray 
  
 name 
 : 
  
 log-volume 
  
 - 
  
 name 
 : 
  
 shared-memory 
  
 mountPath 
 : 
  
 /dev/shm 
  
 - 
  
 name 
 : 
  
 nvidia-install-dir-host 
  
 mountPath 
 : 
  
 /usr/local/nvidia 
  
 - 
  
 name 
 : 
  
 gib-nccl-plugin-volume 
  
 mountPath 
 : 
  
 /usr/local/gib 
  
 - 
  
 mountPath 
 : 
  
 /data 
  
 name 
 : 
  
 lustre-data 
  
  
 # --- WORKER POD STARTUP SCRIPT --- 
  
 command 
 : 
  
 - 
  
 "bash" 
  
 - 
  
 "-c" 
  
 - 
  
 | 
  
 set -ex 
  
 echo "--- Worker Pod Setup ---" 
  
 apt-get update 
  
 apt-get install -y sudo netcat-openbsd pciutils 
  
 cd /opt/nemo-rl 
  
 /usr/bin/python -m pip install uv 
  
 /usr/bin/python -m uv venv 
  
 ldconfig /usr/local/nvidia/lib64/ 
  
 ldconfig -p | grep libcuda | sed 's/^/  /' 
  
 export LD_LIBRARY_PATH="/usr/local/gib/lib64:$LD_LIBRARY_PATH" 
  
 source /usr/local/gib/scripts/set_nccl_env.sh 
  
 echo "Worker pod setup complete. Starting Ray..." 
  
 exec ${KUBERAY_GEN_RAY_START_CMD} 
  
 sidecarContainers 
 : 
  
 - 
  
 name 
 : 
  
 fluent-bit 
  
 env 
 : 
  
 - 
  
 name 
 : 
  
 RAY_GROUP 
  
 valueFrom 
 : 
  
 fieldRef 
 : 
  
 fieldPath 
 : 
  
 metadata.labels['ray.io/group'] 
  
 image 
 : 
  
 fluent/fluent-bit:latest 
  
 volumeMounts 
 : 
  
 - 
  
 name 
 : 
  
 fluentbit-config-volume 
  
 mountPath 
 : 
  
 /fluent-bit/etc/ 
  
 - 
  
 mountPath 
 : 
  
 /tmp/ray 
  
 name 
 : 
  
 log-volume 
 # --- Service Config --- 
 service 
 : 
  
 type 
 : 
  
 ClusterIP

Replace NCCL_TUNER_CONFIG_PATH with any of the following values, based on the accelerator you use in this tutorial:

NVIDIA B200 (180 GB): /usr/local/gib/configs/tuner_config_a4.txtpb
NVIDIA H200 (141 GB): /usr/local/gib/configs/tuner_config_a3u.txtpb

In this manifest, the head node manages the Job and hosts the Ray Dashboard. The worker nodes run the training Jobs.

Install the Ray cluster:

  export 
  
 REPLICA_COUNT 
 = 
 2 
helm  
install  
ray-cluster  
.  
 \ 
  
--set  
values.additionalWorkerGroups.worker-grp-0.replicas = 
 $REPLICA_COUNT

For this tutorial, you use two worker nodes. If you want to change the number of worker nodes, change the REPLICA_COUNT value.

To deploy the Ray cluster, run the launcher.sh script:
```
 bash  
launcher.sh 
```

Verify the worker and head nodes are running:

 kubectl  
get  
pods

The output is similar to the following:

 NAME                                          READY STATUS RESTARTS AGE
ray-cluster-kuberay-head-sw7dp                3/3   Running 0      33h
ray-cluster-kuberay-worker-grp-0-worker-gkbxw 3/3   Running 0      33h
ray-cluster-kuberay-worker-grp-0-worker-kdg62 3/3   Running 0      33h

Verify the Ray cluster is running:

 kubectl  
ray  
get  
clusters

The output is similar to the following:

 NAME                 NAMESPACE DESIRED WORKERS AVAILABLE WORKERS CPUS GPUS TPUS MEMORY CONDITION STATUS AGE
ray-cluster-kuberay  default   2       2           618     17   0    1573741824k RayClusterProvisioned ready 33h

Launch the GRPO Job

After your Ray cluster is ready, you can submit a Ray Job to your running Ray cluster on GKE. NeMo RL automatically downloads the model during the execution of the RL training Job.

To submit a Ray Job, start an interactive session to execute the Job.

To establish a local connection to your Ray cluster, run this command:
```
   
kubectl  
ray  
session  
ray-cluster-kuberay 
```
This command initiates port forwarding between your local machine and the Ray head node in your GKE cluster. Note that your terminal will be occupied while this session is active; to proceed, open a separate terminal instance.

Edit the gemma3-27b-gsm8k.sh file:

  # Copyright 2026 Google LLC 
 # 
 # Licensed under the Apache License, Version 2.0 (the "License"); 
 # you may not use this file except in compliance with the License. 
 # You may obtain a copy of the License at 
 # 
 #     http://www.apache.org/licenses/LICENSE-2.0 
 # 
 # Unless required by applicable law or agreed to in writing, software 
 # distributed under the License is distributed on an "AS IS" BASIS, 
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 
 # See the License for the specific language governing permissions and 
 # limitations under the License. 
 #!/bin/bash 
 WANDB_API_KEY 
 = 
 ' YOUR_WANDB_API_KEY 
' 
  
 # Update this with your WANDB API key 
 HF_TOKEN 
 = 
 ' YOUR_HF_TOKEN 
' 
  
 # Update this with your HF token 
 WORLD_SIZE 
 = 
 16 
 # --- Step 1: Find the Ray Head Pod --- 
 echo 
  
 "Finding Ray head pod..." 
 export 
  
 HEAD_POD_NAME 
 = 
 $( 
kubectl  
get  
pods  
--selector = 
ray.io/node-type = 
head  
-o  
 jsonpath 
 = 
 '{.items[0].metadata.name}' 
 ) 
 if 
  
 [ 
  
-z  
 " 
 $HEAD_POD_NAME 
 " 
  
 ] 
 ; 
  
 then 
  
 echo 
  
 "Error: No running Ray head pod found. Please check your cluster." 
  
 exit 
  
 1 
 fi 
 echo 
  
 "Found head pod: 
 $HEAD_POD_NAME 
 " 
 echo 
  
 "" 
 # --- Step 2: Define the Job Script to Run --- 
 # This is the script that will be executed *inside* the head pod. 
 # It assumes the 'uv venv' setup from the values.yaml is already done. 
 JOB_SCRIPT 
 = 
 $( 
cat  
<<EOF set 
  
-ex echo 
  
 "--- Running on Ray Head Pod ( 
 $HOSTNAME 
 ) ---" 
 cd 
  
/opt/nemo-rl

git  
pull && 
git  
checkout  
main

sed  
-i  
 's/subset: Optional\[str\] = None/subset: Optional[str] = "main"/' 
  
/opt/nemo-rl/nemo_rl/data/datasets/response_datasets/response_dataset.py
sed  
-i  
 's/raw_dataset = load_dataset(data_path)/raw_dataset = load_dataset(data_path, "main")/' 
  
/opt/nemo-rl/nemo_rl/data/datasets/utils.py echo 
  
 "Setting environment variables..." 
 export 
  
 WANDB_API_KEY 
 = 
 $WANDB_API_KEY 
 export 
  
 HF_TOKEN 
 = 
 $HF_TOKEN 
 export 
  
 HF_HOME 
 = 
/opt/nemo-rl/ ###-----Example to launch Gemma3-27B on 3 nodes (24 GPUs)---------- 
uv  
run  
python  
examples/run_grpo_math.py  
 \ 
  
--config  
examples/configs/recipes/llm/grpo-gemma3-27b-it-8n4g-fsdp2tp4-actckpt-long.yaml  
 \ 
  
cluster.num_nodes = 
 2 
  
 \ 
  
cluster.gpus_per_node = 
 8 
  
 \ 
  
grpo.max_num_steps = 
 300 
  
 \ 
  
checkpointing.checkpoint_dir = 
/data/nemo_rl_gemma3_27b_3_17  
 \ 
  
data.dataset_name = 
ResponseDataset  
 \ 
  
+data.train_data_path = 
openai/gsm8k  
 \ 
  
+data.val_data_path = 
openai/gsm8k  
 \ 
  
+data.val_split = 
 test 
  
 \ 
  
+data.train_split = 
train  
 \ 
  
+data.subset = 
 "main" 
  
 \ 
  
+data.input_key = 
 "question" 
  
 \ 
  
+data.output_key = 
 "answer" 
  
 \ 
  
logger.tensorboard_enabled = 
False  
 \ 
  
logger.wandb_enabled = 
True  
 \ 
  
logger.wandb.name = 
 'nemo_rl_gemma3_27b_3_17' 
  
 \ 
  
grpo.num_prompts_per_step = 
 16 
  
 \ 
  
grpo.num_generations_per_prompt = 
 64 
  
 \ 
  
policy.generation.colocated.enabled = 
False  
 \ 
  
policy.generation.colocated.resources.num_nodes = 
 1 
  
 \ 
  
policy.generation.colocated.resources.gpus_per_node = 
 8 
  
 \ 
  
policy.generation.vllm_cfg.tensor_parallel_size = 
 8 
  
 \ 
  
policy.generation.vllm_cfg.gpu_memory_utilization = 
 0 
.9  
 \ 
  
policy.dtensor_cfg.tensor_parallel_size = 
 8 
 echo 
  
 "--- Job Finished ---" 
EOF ) 
 # --- Step 3: Execute the Job --- 
 echo 
  
 "Submitting job to 
 $HEAD_POD_NAME 
 ..." 
 echo 
  
 " 
 $JOB_SCRIPT 
 " 
  
 | 
  
tr  
-d  
 '\r' 
  
 | 
  
kubectl  
 exec 
  
-i  
 $HEAD_POD_NAME 
  
-c  
ray-head  
--  
/bin/bash echo 
  
 "" 
 echo 
  
 "Job submission complete."

Replace the following values in the gemma3-27b-gsm8k.sh file:

YOUR_WANDB_API_KEY : your WandB API key.
YOUR_HF_TOKEN : your Hugging Face token.

In this file, you can see the configuration to run a Job with the gemma3-27b-it model on the GSM8k dataset. To complete the GRPO training pipeline, this script defines the following parameters:

num_prompts_per_step: 16 and num_generations_per_prompt: 64 : the Gemma3-27b-it model generates a large group of responses for every prompt. In this configuration, the model produces 1,024 total responses (16 × 64 = 1,024).
policy.generation.colocated.enabled=False : this parameter disables the colocated generation feature, which means that the model doesn't generate responses in the same node as the training process. In standard RL, the same GPUs handle both training and generation. In this NeMo RL setup, you dedicate specific nodes (managed with the policy.generation.colocated.resources parameter) solely to vLLM inference, while the rest of the cluster focuses on the heavy-duty training math. By separating these workloads, you prevent resource contention between the memory-intensive training buffers and the compute-intensive inference workloads.

To submit the Job, run the following command:
```
 bash  
gemma3-27b-it/gemma3-27b-gsm8k.sh 
```
When the Job is running, the output shows the training results, timing, and performance metrics.

Monitor the health of the GRPO Job

After Ray finishes the Job, NeMo RL stores the checkpoints in the configured path.

To check the output of the of the GRPO Job, create a SSH session to the ray-head container:

 kubectl  
 exec 
  
-it  
 $( 
kubectl  
get  
pods  
-l  
ray.io/node-type = 
head  
-o  
name ) 
  
-c  
ray-head  
--  
bash

Install the apt tree utility within the ray-head container's terminal:

 apt  
update && 
apt  
install  
-y  
tree

The output is similar to the following:

 root@ray-cluster-kuberay-worker-grp-0-worker-gkbxw:/opt/nemo-rl# tree /data/nemo_rl_gemma3_27b_3_17/
 /data/nemo_rl_gemma3_27b_3_17/
 `-- step_10
     |-- config.yaml
     |-- policy
     |   |-- optimizer
     |   |   |-- __0_0.distcp
     |   |   |-- __10_0.distcp
     |   |   |-- __11_0.distcp
     |   |   |-- __12_0.distcp
     |   |   |-- __13_0.distcp
     |   |   |-- __14_0.distcp
     |   |   |-- __15_0.distcp
     |   |   |-- __1_0.distcp
     |   |   |-- __2_0.distcp
     |   |   |-- __3_0.distcp
     |   |   |-- __4_0.distcp
     |   |   |-- __5_0.distcp
     |   |   |-- __6_0.distcp
     |   |   |-- __7_0.distcp
     |   |   |-- __8_0.distcp
     |   |   `-- __9_0.distcp
     |   |-- tokenizer
     |   |   |-- chat_template.jinja
     |   |   |-- special_tokens_map.json
     |   |   |-- tokenizer.json
     |   |   `-- tokenizer_config.json
     |   `-- weights
     |       |-- __0_0.distcp
     |       |-- __10_0.distcp
     |       |-- __11_0.distcp
     |       |-- __12_0.distcp
     |       |-- __13_0.distcp
     |       |-- __14_0.distcp
     |       |-- __15_0.distcp
     |       |-- __1_0.distcp
     |       |-- __2_0.distcp
     |       |-- __3_0.distcp
     |       |-- __4_0.distcp
     |       |-- __5_0.distcp
     |       |-- __6_0.distcp
     |       |-- __7_0.distcp
     |       |-- __8_0.distcp
     |       `-- __9_0.distcp
     |-- train_dataloader.pt
     `-- training_info.json

 6 directories, 39 files
 ```

Clean up

To avoid incurring charges, delete the resources:

 helm  
delete  
ray-cluster
gcloud  
container  
clusters  
delete  
 ${ 
 CLUSTER_NAME 
 } 
  
--location = 
 ${ 
 CONTROL_PLANE_LOCATION 
 } 
gcloud  
storage  
rm  
-r  
gs:// ${ 
 GS_BUCKET 
 }

Fine-tune and scale reinforcement learning with NVIDIA NeMo RL on GKE Stay organized with collections Save and categorize content based on your preferences.

Background

Reinforcement learning (RL)

Group Relative Policy Optimization (GRPO)

NVIDIA NeMo RL

GSM8k dataset

Objectives

Before you begin

Prepare your environment

Set up infrastructure

Create a VPC network

Create the GKE cluster

Autopilot

Standard

Configure network mappings

Prepare storage

Deploy RayCluster

Launch the GRPO Job

Monitor the health of the GRPO Job

Clean up

What's next

Fine-tune and scale reinforcement learning with NVIDIA NeMo RL on GKE