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Maximize GPU network bandwidth in Standard mode clusters | GKE networking

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This page shows you how to maximize network bandwidth and throughput for high-performance GPU workloads in Google Kubernetes Engine (GKE) Standard clusters by using GPUDirect-TCPXO, GPUDirect-TCPX, gVNIC, and multi-networking. If you use Autopilot clusters, see

.

This page is intended for machine learning (ML) engineers and platform administrators who facilitate ML workloads. To learn more about common roles and example tasks that we reference in Google Cloud content, see Common GKE user roles and tasks.

Artificial intelligence (AI), ML, and high performance computing (HPC) applications require powerful acceleration to optimize performance by reducing job completion times. For example, ML models that focus on conversational AI and image generation require high scalability and compute power.

Before reading this page, ensure that you're familiar with networking technologies, such as network interface cards (NICs) and TCP, and with accelerator technologies like the NVIDIA Collective Communications Library (NCCL).

Google Cloud has accelerator-optimized supercomputers that are built for scalable, massive models. These machines have the following benefits:

• Eight NVIDIA B200, H200, or H100 GPUs per machine.
• Up to 200 Gbps bandwidth on the primary NIC.
• Secondary NICs (up to eight on A3 Mega machine types and up to four on A3 High machine types), each supporting up to 200 Gbps bandwidth for GPU data transfer.

Your GKE workload must use all available GPUs and all available secondary NICs on a single node and use a significant portion of the available bandwidth. The solution described in this document is ideal for workloads that require high performance, high throughput, and low latency.

To maximize your network bandwidth in GPU supercomputer nodes, use all of the following features:

• GPUDirect networking stack: The A3 machine series supports three networking stacks for custom, remote direct memory access (RDMA):
  • On A3 High machine types and NVIDIA H100 GPUs, utilize GPUDirect-TCPX to reduce the overhead required to transfer packet payloads to and from GPUs, which significantly improves throughput at scale compared to GPUs that don't use GPUDirect.
  • On A3 Mega machine types and NVIDIA H100 Mega GPUs, utilize GPUDirect-TCPXO which further improves GPU to VM communication.
  • On A3 Ultra machine types and NVIDIA H200 GPUs, and A4 machine types and NVIDIA B200 GPUs, utilize GPUDirect RDMA to run distributed AI workloads with further throughput improvements. To get started, create a custom AI-optimized GKE cluster.
• gVNIC: Enable GPUDirect capabilities such as packet header splitting, flow steering, and buffer management. gVNIC is required to use GPUDirect-TCPX or GPUDirect-TCPXO. For details about gVNIC, see Increase network traffic speed for GPU nodes.
• Multi-networking: Add secondary NICs to the accelerator-optimized machine. Each NIC is associated with a separate subnet in its own VPC to avoid conflicts. For details about multi-network support, see Setup multi-network support for Pods.
• Placement policies: Use a resource placement policy to place all GPU nodes for a specific workload on physically close servers to minimize latency. For details, see Define compact placement for GKE nodes.

To use all of these capabilities together, you'll do the following:

1. Create Virtual Private Cloud (VPC)s and subnets
2. Create the GKE environment.
3. Install the GPUDirect binary and the NCCL plugin
4. Deploy the NRI device injector plugin
5. Deploy a test workload to verify GPUDirect setup

Before you start, make sure that you have performed the following tasks:

• Enable the Google Kubernetes Engine API.
Enable Google Kubernetes Engine API
• If you want to use the Google Cloud CLI for this task, install and then initialize the gcloud CLI. If you previously installed the gcloud CLI, get the latest version by running gcloud components update. Note: For existing gcloud CLI installations, make sure to set the compute/region property. If you use primarily zonal clusters, set the compute/zone instead. By setting a default location, you can avoid errors in the gcloud CLI like the following: One of [--zone, --region] must be supplied: Please specify location. You might need to specify the location in certain commands if the location of your cluster differs from the default that you set.

• Ensure that you have enough quota for H100 GPUs. To request more quota, see GPU quotas.

The following requirements apply to both GPUDirect-TCPX and GPUDirect-TCPXO unless otherwise indicated.

• GPUDirect-TCPX is supported on GKE version 1.27 or later and requires:
  • * The a3-highgpu-8g machine type.
  • For GKE version 1.27, use GKE patch version 1.27.7-gke.1121000 or later.
  • For GKE version 1.28, use GKE patch version 1.28.10-gke.1141000 or later.
  • For GKE version 1.29, use GKE patch version 1.29.5-gke.1121000or later.

• GPUDirect-TCPXO is supported on GKE version 1.28 or later and requires:

  • The a3-megagpu-8g machine type.
  • For GKE version 1.28, use GKE patch version 1.28.9-gke.1250000 or later.
  • For GKE version 1.29, use GKE patch version 1.29.4-gke.1542000 or later.
  • For GKE version 1.30, use GKE patch version 1.30.4-gke.1129000 or later.
  • For GKE version 1.31, use GKE patch version 1.31.1-gke.2008000 or later.
  • For GKE version 1.32, use GKE patch version 1.32.2-gke.1489001 or later.

• The GKE node must use a Container-Optimized OS (COS) node image. Ubuntu and Windows node images are not supported.

• Your GPU nodes must use NVIDIA driver version 535 or later.
• You must use GKE Dataplane V2.
• For GPUDirect-TCPX or GPUDirect-TCPXO workloads that run across multiple node pools, all of the node pools must be in the same Compute Engine zones and must use the same network sets, such as VPCs and subnets.

The following limitations apply:

• GPUDirect-TCPX and GPUDirect-TCPXO are not supported with multi-instance GPUs, GPU time-sharing, or NVIDIA MPS.
• You can't use NCCL FastSocket with GPUDirect-TCPX or GPUDirect-TCPXO .
• Your GKE workload must use all available GPUs and all available secondary NICs on a single node. Multiple pods cannot use GPUDirect-TCPX or GPUDirect-TCPXO on a single node.
• You can only use the a3-highgpu-8g and the a3-megagpu-8g machine types. Other A3 machine types aren't supported.

Create separate VPC networks in your project for each virtual NIC that you'll add to your nodes. Each VPC network must have a subnet and a firewall rule that allows internal network traffic.

1. Create the VPC networks for GPUDirect in your project, each with a subnet and a firewall rule. Choose the GPUDirect-TCPX tab for A3 High machine types, or choose the GPUDirect-TCPXO tab for A3 Mega machine types, then complete the following instructions:

   GPUDirect-TCPXO

   To maximize your bandwidth, we recommend that you create eight new networks.

   for N in $(seq 1 8); do
   gcloud compute networks create PREFIX-net-$N \
       --subnet-mode=custom \
       --mtu=8244
   
   gcloud compute networks subnets create PREFIX-sub-$N \
       --network=PREFIX-net-$N \
       --region=REGION \
       --range=SUBNET_RANGE
   
   gcloud compute firewall-rules create PREFIX-internal-$N \
     --network=PREFIX-net-$N \
     --action=ALLOW \
     --rules=tcp:0-65535,udp:0-65535,icmp \
     --source-ranges=SOURCE_RANGE
   done
   

   Replace the following:

   • PROJECT_ID: your Google Cloud project ID.
   • REGION: the Compute Engine region for each subnet.
   • SUBNET_RANGE: the IP address range of each subnet in CIDR notation. This example command iterates for eight subnets, so you should use a variable to change the IP address for each subnet. For example, specify 192.168.$N.0/24 so that the first subnet uses 192.168.1.0/24, the second subnet uses 192.168.2.0/24, and so on.
   • SOURCE_RANGE: The source IP address range for the firewall rule to allow ingress traffic, in CIDR notation. For example, 192.168.0.0/16.
   GPUDirect-TCPX

   To maximize your bandwidth, we recommend that you create four new networks.

   for N in $(seq 1 4); do
   gcloud compute networks create PREFIX-net-$N \
       --subnet-mode=custom \
       --mtu=8244
   
   gcloud compute networks subnets create PREFIX-sub-$N \
       --network=PREFIX-net-$N \
       --region=REGION \
       --range=SUBNET_RANGE
   
   gcloud compute firewall-rules create PREFIX-internal-$N \
     --network=PREFIX-net-$N \
     --action=ALLOW \
     --rules=tcp:0-65535,udp:0-65535,icmp \
     --source-ranges=SOURCE_RANGE
   done
   

   Replace the following:

   • PROJECT_ID: your Google Cloud project ID.
   • REGION: the Compute Engine region for each subnet.
   • SUBNET_RANGE: the IP address range of each subnet in CIDR notation. This example command iterates for four subnets, so you should use a variable to change the IP address for each subnet. For example, specify 192.168.$N.0/24 so that the first subnet uses 192.168.1.0/24, the second subnet uses 192.168.2.0/24, etc.
   • SOURCE_RANGE: The source IP address range for the firewall rule to allow ingress traffic, in CIDR notation. For example, 192.168.0.0/16.

2. Verify that the networks were created:

   gcloud compute networks list
   

Create a new GKE cluster that uses multi-networking (Preview) and create a GPU node pool that has the following characteristics:

• gVNIC enabled
• Multi-networking subnets specified for each secondary NIC
• A3 machine series with H100 GPUs backing the nodes
• Latest NVIDIA drivers installed

You can't update an existing cluster to use multi-networking.

1. Choose an available GKE version that supports GPUDirect-TCPXO. To list the versions, run this command:

   gcloud container get-server-config \
       --format="yaml(validMasterVersions)" \
       --region=REGION \
       --project=PROJECT_ID
   

   Replace the following:

   • REGION: the compute region for the cluster control plane.
   • PROJECT_ID: your Google Cloud project ID.

2. Create a cluster:

   gcloud beta container clusters create CLUSTER_NAME \
     --enable-dataplane-v2 --enable-ip-alias --zone=ZONE \
     --enable-multi-networking --cluster-version=VERSION \
     --no-enable-autoupgrade \
     --project=PROJECT_ID
   

   Replace the following:

   • CLUSTER_NAME: the name of your new cluster.
   • VERSION: a GKE version that supports GPUDirect-TCPXO, as described in Requirements.
   • ZONE: the compute zone for the cluster.

3. Create Network and GKENetworkParamSet resources in the cluster that correspond to the VPC networks and subnetworks that you created:

   kubectl apply -f - <<EOF
   apiVersion: networking.gke.io/v1
   kind: Network
   metadata:
     name: vpc1
   spec:
     parametersRef:
       group: networking.gke.io
       kind: GKENetworkParamSet
       name: vpc1
     type: Device
   ---
   apiVersion: networking.gke.io/v1
   kind: Network
   metadata:
     name: vpc2
   spec:
     parametersRef:
       group: networking.gke.io
       kind: GKENetworkParamSet
       name: vpc2
     type: Device
   ---
   apiVersion: networking.gke.io/v1
   kind: Network
   metadata:
     name: vpc3
   spec:
     parametersRef:
       group: networking.gke.io
       kind: GKENetworkParamSet
       name: vpc3
     type: Device
   ---
   apiVersion: networking.gke.io/v1
   kind: Network
   metadata:
     name: vpc4
   spec:
     parametersRef:
       group: networking.gke.io
       kind: GKENetworkParamSet
       name: vpc4
     type: Device
   ---
   apiVersion: networking.gke.io/v1
   kind: Network
   metadata:
     name: vpc5
   spec:
     parametersRef:
       group: networking.gke.io
       kind: GKENetworkParamSet
       name: vpc5
     type: Device
   ---
   apiVersion: networking.gke.io/v1
   kind: Network
   metadata:
     name: vpc6
   spec:
     parametersRef:
       group: networking.gke.io
       kind: GKENetworkParamSet
       name: vpc6
     type: Device
   ---
   apiVersion: networking.gke.io/v1
   kind: Network
   metadata:
     name: vpc7
   spec:
     parametersRef:
       group: networking.gke.io
       kind: GKENetworkParamSet
       name: vpc7
     type: Device
   ---
   apiVersion: networking.gke.io/v1
   kind: Network
   metadata:
     name: vpc8
   spec:
     parametersRef:
       group: networking.gke.io
       kind: GKENetworkParamSet
       name: vpc8
     type: Device
   ---
   apiVersion: networking.gke.io/v1
   kind: GKENetworkParamSet
   metadata:
     name: vpc1
   spec:
     vpc: PREFIX-net-1
     vpcSubnet: PREFIX-sub-1
     deviceMode: NetDevice
   ---
   apiVersion: networking.gke.io/v1
   kind: GKENetworkParamSet
   metadata:
     name: vpc2
   spec:
     vpc: PREFIX-net-2
     vpcSubnet: PREFIX-sub-2
     deviceMode: NetDevice
   ---
   apiVersion: networking.gke.io/v1
   kind: GKENetworkParamSet
   metadata:
     name: vpc3
   spec:
     vpc: PREFIX-net-3
     vpcSubnet: PREFIX-sub-3
     deviceMode: NetDevice
   ---
   apiVersion: networking.gke.io/v1
   kind: GKENetworkParamSet
   metadata:
     name: vpc4
   spec:
     vpc: PREFIX-net-4
     vpcSubnet: PREFIX-sub-4
     deviceMode: NetDevice
   ---
   apiVersion: networking.gke.io/v1
   kind: GKENetworkParamSet
   metadata:
     name: vpc5
   spec:
     vpc: PREFIX-net-5
     vpcSubnet: PREFIX-sub-5
     deviceMode: NetDevice
   ---
   apiVersion: networking.gke.io/v1
   kind: GKENetworkParamSet
   metadata:
     name: vpc6
   spec:
     vpc: PREFIX-net-6
     vpcSubnet: PREFIX-sub-6
     deviceMode: NetDevice
   ---
   apiVersion: networking.gke.io/v1
   kind: GKENetworkParamSet
   metadata:
     name: vpc7
   spec:
     vpc: PREFIX-net-7
     vpcSubnet: PREFIX-sub-7
     deviceMode: NetDevice
   ---
   apiVersion: networking.gke.io/v1
   kind: GKENetworkParamSet
   metadata:
     name: vpc8
   spec:
     vpc: PREFIX-net-8
     vpcSubnet: PREFIX-sub-8
     deviceMode: NetDevice
   EOF
   

   These resources tell GKE to configure the NICs for GPU traffic in passthrough mode. GKE doesn't apply built-in networking programming using eBPF to this traffic.

1. Create a cluster:

   gcloud beta container clusters create CLUSTER_NAME \
     --enable-dataplane-v2 --enable-ip-alias --zone=ZONE \
     --enable-multi-networking --cluster-version=VERSION \
     --no-enable-autoupgrade \
     --project=PROJECT_ID
   

   Replace the following: * CLUSTER_NAME: the name of your new cluster. * VERSION: a GKE version that supports GPUDirect-TCPX, as described in Requirements. * ZONE: the compute zone for the cluster.

2. Create Network and GKENetworkParamSet resources in the cluster that correspond to the VPC networks and subnetworks that you created:

   kubectl apply -f - <<EOF
   apiVersion: networking.gke.io/v1
   kind: Network
   metadata:
     name: vpc1
   spec:
     parametersRef:
       group: networking.gke.io
       kind: GKENetworkParamSet
       name: vpc1
     type: Device
   ---
   apiVersion: networking.gke.io/v1
   kind: Network
   metadata:
     name: vpc2
   spec:
     parametersRef:
       group: networking.gke.io
       kind: GKENetworkParamSet
       name: vpc2
     type: Device
   ---
   apiVersion: networking.gke.io/v1
   kind: Network
   metadata:
     name: vpc3
   spec:
     parametersRef:
       group: networking.gke.io
       kind: GKENetworkParamSet
       name: vpc3
     type: Device
   ---
   apiVersion: networking.gke.io/v1
   kind: Network
   metadata:
     name: vpc4
   spec:
     parametersRef:
       group: networking.gke.io
       kind: GKENetworkParamSet
       name: vpc4
     type: Device
   ---
   apiVersion: networking.gke.io/v1
   kind: GKENetworkParamSet
   metadata:
     name: vpc1
   spec:
     vpc: PREFIX-net-1
     vpcSubnet: PREFIX-sub-1
     deviceMode: NetDevice
   ---
   apiVersion: networking.gke.io/v1
   kind: GKENetworkParamSet
   metadata:
     name: vpc2
   spec:
     vpc: PREFIX-net-2
     vpcSubnet: PREFIX-sub-2
     deviceMode: NetDevice
   ---
   apiVersion: networking.gke.io/v1
   kind: GKENetworkParamSet
   metadata:
     name: vpc3
   spec:
     vpc: PREFIX-net-3
     vpcSubnet: PREFIX-sub-3
     deviceMode: NetDevice
   ---
   apiVersion: networking.gke.io/v1
   kind: GKENetworkParamSet
   metadata:
     name: vpc4
   spec:
     vpc: PREFIX-net-4
     vpcSubnet: PREFIX-sub-4
     deviceMode: NetDevice
   EOF
   

   These resources tell GKE to configure the NICs for GPU traffic in passthrough mode. GKE doesn't apply built-in networking programming using eBPF to this traffic.

Create a node pool for the H100 GPUs:

gcloud beta container node-pools create NODE_POOL_NAME \
    --zone=ZONE \
    --cluster=CLUSTER_NAME \
    --project=PROJECT_ID \
    --accelerator=type=nvidia-h100-mega-80gb,count=8,gpu-driver-version=LATEST \
    --machine-type=a3-megagpu-8g \
    --num-nodes=2 \
    --additional-node-network network=PREFIX-net-1,subnetwork=PREFIX-sub-1 \
    --additional-node-network network=PREFIX-net-2,subnetwork=PREFIX-sub-2 \
    --additional-node-network network=PREFIX-net-3,subnetwork=PREFIX-sub-3 \
    --additional-node-network network=PREFIX-net-4,subnetwork=PREFIX-sub-4 \
    --additional-node-network network=PREFIX-net-5,subnetwork=PREFIX-sub-5 \
    --additional-node-network network=PREFIX-net-6,subnetwork=PREFIX-sub-6 \
    --additional-node-network network=PREFIX-net-7,subnetwork=PREFIX-sub-7 \
    --additional-node-network network=PREFIX-net-8,subnetwork=PREFIX-sub-8 \
    --enable-gvnic \
    --no-enable-autoupgrade \
    --scopes "https://www.googleapis.com/auth/cloud-platform" \
    [--placement-policy=POLICY_NAME \
    --reservation-affinity=specific \
    --reservation=RESERVATION_NAME \
    --host-maintenance-interval=PERIODIC]

Replace NODE_POOL_NAME with your node pool name.

In the example, the --scopes "https://www.googleapis.com/auth/cloud-platform" argument sets the node instance's scope to be cloud-platform for testing convenience. For production, you may want to limit the scope to configure finer-grained credentials.

Use the --placement-policy, --reservation-affinity, and --reservation flags if you are using a reservation. Specify these flags to configure the policy name and reservation in the node pool.

If this command fails, you might not have enough H100 GPU quota in your project. Ensure that you have sufficient quota and retry the command.

Create a node pool for the H100 GPUs:

gcloud container node-pools create NODE_POOL_NAME \
    --cluster=CLUSTER_NAME \
    --location=LOCATION \
    --machine-type=a3-highgpu-8g \
    --accelerator=type=nvidia-h100-80gb,count=8,gpu-driver-version=LATEST \
    --additional-node-network=network=PREFIX-net-1,subnetwork=PREFIX-sub-1 \
    --additional-node-network=network=PREFIX-net-2,subnetwork=PREFIX-sub-2 \
    --additional-node-network=network=PREFIX-net-3,subnetwork=PREFIX-sub-3 \
    --additional-node-network=network=PREFIX-net-4,subnetwork=PREFIX-sub-4 \
    --enable-gvnic \
    --no-enable-autoupgrade

Replace NODE_POOL_NAME with the name of the node pool.

If this command fails, you might not have enough H100 GPU quota in your project. Ensure that you have quota and retry the command.

After you create the node pool, verify that each node has the attached GPUs:

1. Get a list of nodes in the cluster:

   kubectl get nodes
   

2. Verify that each GPU node has eight GPUs:

   kubectl describe node NODE_NAME
   

   Replace NODE_NAME with the name of the node to describe.

   The output is similar to the following:

   Capacity:
     ...
     nvidia.com/gpu:             8
   Allocatable:
     ...
     nvidia.com/gpu:             8
   

This section shows you how to install the GPUDirect binary, based on your A3 machine type (GPUDirect-TCPX for A3 High, GPUDirect-TCPXO for A3 Mega) and a specific NCCL library version using a DaemonSet.

This DaemonSet does the following:

1. Pre-installation to setup GPUDirect-TCPXO related configurations.
2. Installs the NCCL library and GPUDirect-TCPXO binary on the node.
3. Stores the library and the binary in the /home/kubernetes/bin/nvidia/lib64 directory on the VM. By default, GKE mounts this directory into the /usr/local/nvidia/lib64 path in GPU containers that need to use NCCL and GPUDirect-TCPXO.

To install the binary and configure NCCL, do the following steps:

1. Review the nccl-tcpxo-installer.yaml Daemonset manifest in GitHub.

2. Deploy the DaemonSet:

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

   The NCCL plugin takes approximately two minutes to start running.

3. Verify the status of the DaemonSet Pods:

   kubectl get pods -n=kube-system -l=name=nccl-tcpxo-installer
   

   The output is similar to the following:

   # Output
   nccl-tcpxo-installer-6c2pv                    1/1     Running   0          2m11s
   nccl-tcpxo-installer-qgg82                    1/1     Running   0          2m11s
   

This DaemonSet does the following:

1. Installs the NCCL library and GPUDirect-TCPX binary on the node.
2. Stores the library and the binary in the /home/kubernetes/bin/nvidia/lib64 directory on the VM. By default, GKE mounts this directory into the /usr/local/nvidia/lib64 path in GPU containers that need to use NCCL and GPUDirect-TCPX.

To install the binary and configure NCCL, do the following:

1. Review the nccl-tcpx-installer.yaml Daemonset manifest in GitHub.

2. Deploy the DaemonSet:

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

   The NCCL plugin takes approximately two minutes to start running.

3. Verify the status of the DaemonSet Pods:

   kubectl get pods -n=kube-system -l=name=nccl-tcpx-installer
   

   The output is similar to the following:

   nccl-tcpx-installer-6c2pv                    1/1     Running   0          2m11s
   nccl-tcpx-installer-qgg82                    1/1     Running   0          2m11s
   

This section shows you how to install the NRI device injector by using a DaemonSet. Both H100 GPU machine types install the same NRI device injector plugin. This plugin does the following:

1. Enables Node Resource Interface (NRI) on the node that has H100 GPUs. NRI is enabled by default on GKE version 1.29 and later.
2. Deploys a NRI device injector plugin container that injects GPU devices into containers specified by Pod annotations.

To install the plugin, do the following:

1. Review the nri-device-injector.yaml Deployment manifest in GitHub.

2. Deploy the DaemonSet:

   kubectl apply -f https://raw.githubusercontent.com/GoogleCloudPlatform/container-engine-accelerators/master/nri_device_injector/nri-device-injector.yaml
   

   The NCCL plugin takes approximately two minutes to start running.

3. Verify the status of the DaemonSet Pods:

   kubectl get pods -n=kube-system -l=name=device-injector
   

   The output is similar to the following:

   # Output
   device-injector-md6hb                         1/1     Running   0       4h54m
   device-injector-vh9bm                         1/1     Running   0       4h54m
   

In this section, you deploy a sample workload to verify that NCCL and GPUDirect-TCPX or GPUDirect-TCPXO work as expected. This sample workload does the following:

1. Deploys two Pods, each of which runs in a node that has H100 GPUs.
2. Deploys a sidecar container in each Pod to let those Pods use GPUDirect-TCPXO or GPUDirect-TCPX.

To deploy this sample workload, do the following:

This workload includes a sidecar container named the tcpxo-daemon, which runs a service that lets the Pod use GPUDirect-TCPXO. You must add this sidecar container to any Pods in your own environment that need to use GPUDirect-TCPXO. For a snippet of the required fields to add to your manifests, see Add GPUDirect to your manifest.

1. Review the nccl-test-latest.yaml manifest in GitHub.

2. Deploy two Pods with the test workload:

   kubectl apply -f https://raw.githubusercontent.com/GoogleCloudPlatform/container-engine-accelerators/master/gpudirect-tcpxo/nccl-test-latest.yaml
   

3. After the Pods deploy, trigger an all-gather test:

   kubectl exec --stdin --tty --container=nccl-test nccl-test-host-1 -- /scripts/allgather.sh nccl-host-1 nccl-host-2
   

   The output is similar to the following:

   #                                                              out-of-place                       in-place
   #        size         count      type   redop    root     time   algbw   busbw #wrong     time   algbw   busbw #wrong
   #         (B)    (elements)                               (us)  (GB/s)  (GB/s)            (us)  (GB/s)  (GB/s)
               0             0     float    none      -1     0.24    0.00    0.00      0     0.18    0.00    0.00      0
               0             0     float    none      -1     0.19    0.00    0.00      0     0.17    0.00    0.00      0
               0             0     float    none      -1     0.17    0.00    0.00      0     0.17    0.00    0.00      0
               0             0     float    none      -1     0.17    0.00    0.00      0     0.17    0.00    0.00      0
               0             0     float    none      -1     0.17    0.00    0.00      0     0.17    0.00    0.00      0
             256             4     float    none      -1    235.2    0.00    0.00      0    235.1    0.00    0.00      0
             512             8     float    none      -1    241.0    0.00    0.00      0    236.1    0.00    0.00      0
            1024            16     float    none      -1    236.3    0.00    0.00      0    233.3    0.00    0.00      0
            2048            32     float    none      -1    234.1    0.01    0.01      0    233.4    0.01    0.01      0
            4096            64     float    none      -1    237.1    0.02    0.02      0    235.3    0.02    0.02      0
            8192           128     float    none      -1    236.2    0.03    0.03      0    235.2    0.03    0.03      0
           16384           256     float    none      -1    236.6    0.07    0.06      0    238.5    0.07    0.06      0
           32768           512     float    none      -1    237.9    0.14    0.13      0    238.8    0.14    0.13      0
           65536          1024     float    none      -1    242.3    0.27    0.25      0    239.4    0.27    0.26      0
          131072          2048     float    none      -1    263.0    0.50    0.47      0    275.1    0.48    0.45      0
          262144          4096     float    none      -1    279.2    0.94    0.88      0    269.9    0.97    0.91      0
          524288          8192     float    none      -1    273.5    1.92    1.80      0    273.5    1.92    1.80      0
         1048576         16384     float    none      -1    315.1    3.33    3.12      0    314.1    3.34    3.13      0
         2097152         32768     float    none      -1    319.2    6.57    6.16      0    311.5    6.73    6.31      0
         4194304         65536     float    none      -1    331.8   12.64   11.85      0    331.3   12.66   11.87      0
         8388608        131072     float    none      -1    356.3   23.54   22.07      0    353.8   23.71   22.23      0
        16777216        262144     float    none      -1    409.1   41.01   38.45      0    405.2   41.40   38.81      0
        33554432        524288     float    none      -1    451.4   74.34   69.69      0    447.7   74.94   70.26      0
        67108864       1048576     float    none      -1    713.4   94.07   88.19      0    713.8   94.01   88.13      0
       134217728       2097152     float    none      -1   1122.1  119.62  112.14      0   1116.3  120.23  112.72      0
       268435456       4194304     float    none      -1   1785.8  150.32  140.92      0   1769.2  151.72  142.24      0
       536870912       8388608     float    none      -1   2859.7  187.74  176.00      0   2852.6  188.20  176.44      0
      1073741824      16777216     float    none      -1   5494.1  195.44  183.22      0   5568.2  192.83  180.78      0
      2147483648      33554432     float    none      -1    10841  198.09  185.71      0    10798  198.88  186.45      0
      4294967296      67108864     float    none      -1    21453  200.21  187.70      0    21490  199.86  187.37      0
      8589934592     134217728     float    none      -1    42603  201.63  189.03      0    42670  201.31  188.73      0
   # Out of bounds values : 0 OK
   # Avg bus bandwidth    : 45.7587
   #
   

   Success: At this point, you've successfully installed GPUDirect-TCPXO on your nodes and can use it to optimize the throughput of GPU-heavy workloads that run on those nodes. The required fields to use GPUDirect-TCPXO in your own Pods are described in Add GPUDirect to your manifests in this document.

This workload includes a sidecar container named the tcpx-daemon, which runs a service that lets the Pod use GPUDirect-TCPX. You must add this sidecar container to any Pods in your own environment that need to use GPUDirect-TCPX. For a snippet of the required fields to add to your manifests, see Add GPUDirect to your manifest.

1. Review the nccl-config.yaml ConfigMap manifest in GitHub. This manifest deploys scripts that initialize an NCCL all-gather test and sets NCCL-specific configuration settings.

2. Review the nccl-test-latest.yaml Deployment manifest in GitHub.

3. Deploy the ConfigMap and the test workload:

   kubectl apply -f https://raw.githubusercontent.com/GoogleCloudPlatform/container-engine-accelerators/master/gpudirect-tcpx/nccl-config.yaml
   kubectl apply -f https://raw.githubusercontent.com/GoogleCloudPlatform/container-engine-accelerators/master/gpudirect-tcpx/nccl-test-latest.yaml
   

4. Run the following commands to trigger an NCCL all-gather test for the nodes:

   kubectl exec \
     --stdin --tty --container=nccl-test nccl-test-host-1 \
     -- /configs/allgather.sh nccl-host-1 nccl-host-2
   

   The output is similar to the following:

   #                                                              out-of-place                       in-place
   #       size         count      type   redop    root     time   algbw   busbw #wrong     time   algbw   busbw #wrong
   #        (B)    (elements)                               (us)  (GB/s)  (GB/s)            (us)  (GB/s)  (GB/s)
       1048576         16384     float    none      -1    696.8    1.50    1.41      0    729.0    1.44    1.35      0
       2097152         32768     float    none      -1    776.4    2.70    2.53      0    726.7    2.89    2.71      0
       4194304         65536     float    none      -1    774.3    5.42    5.08      0    805.1    5.21    4.88      0
       8388608        131072     float    none      -1    812.1   10.33    9.68      0    817.6   10.26    9.62      0
      16777216        262144     float    none      -1   1035.2   16.21   15.19      0   1067.8   15.71   14.73      0
      33554432        524288     float    none      -1   1183.3   28.36   26.59      0   1211.8   27.69   25.96      0
      67108864       1048576     float    none      -1   1593.4   42.12   39.49      0   1510.5   44.43   41.65      0
     134217728       2097152     float    none      -1   2127.8   63.08   59.13      0   2312.7   58.03   54.41      0
     268435456       4194304     float    none      -1   3603.0   74.50   69.85      0   3586.2   74.85   70.17      0
     536870912       8388608     float    none      -1   7101.7   75.60   70.87      0   7060.9   76.03   71.28      0
   # Out of bounds values : 0 OK
   # Avg bus bandwidth    : 29.8293
   

The following key-value pairs are the required NCCL configuration settings for GPUDirect-TCPX and GPUDirect-TCPXO. When deploying your workloads that use NCCL, set them as environment variables to optimize performance.

"LD_LIBRARY_PATH=\"${LD_LIBRARY_PATH}:/usr/local/nvidia/lib64\"",
"NCCL_FASTRAK_CTRL_DEV=eth0",
"NCCL_FASTRAK_IFNAME=eth1,eth2,eth3,eth4,eth5,eth6,eth7,eth8",
"NCCL_SOCKET_IFNAME=eth0",
"NCCL_CROSS_NIC=0",
"NCCL_ALGO=Ring,Tree",
"NCCL_PROTO=Simple,LL128",
"NCCL_MIN_NCHANNELS=4",
"NCCL_TUNER_PLUGIN=libnccl-tuner.so",
"NCCL_TUNER_CONFIG_PATH=/usr/local/nvidia/lib64/a3plus_tuner_config.textproto",
"NCCL_SHIMNET_GUEST_CONFIG_CHECKER_CONFIG_FILE=/usr/local/nvidia/lib64/a3plus_guest_config.textproto",
"NCCL_DYNAMIC_CHUNK_SIZE=524288",
"NCCL_P2P_NET_CHUNKSIZE=524288",
"NCCL_P2P_PCI_CHUNKSIZE=524288",
"NCCL_P2P_NVL_CHUNKSIZE=1048576",
"NCCL_FASTRAK_NUM_FLOWS=2",
"NCCL_FASTRAK_USE_SNAP=1",
"NCCL_FASTRAK_PLUGIN_ACCEPT_TIMEOUT_MS=600000",
"NCCL_FASTRAK_ENABLE_CONTROL_CHANNEL=0",
"NCCL_BUFFSIZE=8388608",
"CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7",
"NCCL_NET_GDR_LEVEL=PIX",
"NCCL_FASTRAK_ENABLE_HOTPATH_LOGGING=0",
"NCCL_FASTRAK_USE_LLCM=1",
"NCCL_NVLS_ENABLE=0"

Optionally, you can set all the configurations at once by following these steps:

1. In your workload container manifest, add the following key-value pair as an environment variable:

   NCCL_LIB_DIR="/usr/local/nvidia/lib64"
   

2. Ensure the nccl-env-profile.sh script is executed when your workload container starts. For example, you can do this in your Pod specification by overriding the container's command to include the following:

   source ${NCCL_LIB_DIR}/nccl-env-profile.sh
   

The NVIDIA LL128 (low-latency 128) NCCL communication protocol can significantly improve performance for small-to-medium sized collectives. GPUDirect-TCPXO supports the LL128 protocol.

To use LL128, ensure that the nccl-tcpxo-installer.yaml file in the Install the GPUDirect binary and configure NCCL section uses the following container image version or later:

us-docker.pkg.dev/gce-ai-infra/gpudirect-tcpxo/nccl-plugin-gpudirecttcpx-
dev:v1.0.8-1

To set up LL128, do the following:

• For the us-docker.pkg.dev/gce-ai-infra/gpudirect-tcpxo/nccl-plugin-gpudirecttcpx- dev:v1.0.8-1 NCCL plugin version, do these steps:

  1. In your workload manifest, set the following environment variable:

     NCCL_LIB_DIR="/usr/local/nvidia/lib64
     

  2. Configure your workload to execute the nccl-env-profile-ll128.sh script when the container starts. In your workload manifest, set the following command:

     source ${NCCL_LIB_DIR}/nccl-env-profile-ll128.sh
     

     The nccl-env-profile-ll128.sh script has the following environment variables:

     NCCL_PROTO=Simple,LL128
     NCCL_TUNER_CONFIG_PATH=/usr/local/nvidia/lib64/a3plus_tuner_config_ll128.textproto
     NCCL_SHIMNET_GUEST_CONFIG_CHECKER_CONFIG_FILE=/usr/local/nvidia/lib64/a3plus_guest_config_ll128.textproto
     

• For the us-docker.pkg.dev/gce-ai-infra/gpudirect-tcpxo/nccl-plugin-gpudirecttcpx-dev:v1.0.9-1 NCCL plugin version and later, LL128 becomes a default parameter, so using either nccl-env-profile.sh script or nccl-env-profile-ll128.sh script enables LL128. To disable LL128:

  1. In your workload manifest, set the following environment variable:

     NCCL_LIB_DIR="/usr/local/nvidia/lib64
     

  2. Configure your workload to execute the nccl-env-profile-ll128.sh script when the container starts. In your workload manifest, set the following command:

     source ${NCCL_LIB_DIR}/nccl-env-profile-simple.sh
     

     The nccl-env-profile-simple.sh script has the following environment variables:

     NCCL_PROTO=Simple
     NCCL_TUNER_CONFIG_PATH=/usr/local/nvidia/lib64/a3plus_tuner_config_simple.textproto
     NCCL_SHIMNET_GUEST_CONFIG_CHECKER_CONFIG_FILE=/usr/local/nvidia/lib64/a3plus_tuner_config_simple.textproto
     

"LD_LIBRARY_PATH=\"${LD_LIBRARY_PATH}:/usr/local/tcpx/lib64\"",
"NCCL_SOCKET_IFNAME=\"eth0\"",
"NCCL_ALGO=Ring",
"NCCL_PROTO=Simple",
"NCCL_CROSS_NIC=0",
"NCCL_NET_GDR_LEVEL=PIX",
"NCCL_P2P_PXN_LEVEL=0",
"NCCL_GPUDIRECTTCPX_SOCKET_IFNAME=eth1,eth2,eth3,eth4",
"NCCL_GPUDIRECTTCPX_CTRL_DEV=eth0",
"NCCL_DYNAMIC_CHUNK_SIZE=524288",
"NCCL_P2P_NET_CHUNKSIZE=524288",
"NCCL_P2P_PCI_CHUNKSIZE=524288",
"NCCL_P2P_NVL_CHUNKSIZE=1048576",
"NCCL_BUFFSIZE=4194304",
"NCCL_NSOCKS_PERTHREAD=4",
"NCCL_SOCKET_NTHREADS=1",
"NCCL_GPUDIRECTTCPX_TX_BINDINGS=\"eth1:8-21,112-125;eth2:8-21,112-125;eth3:60-73,164-177;eth4:60-73,164-177\"",
"NCCL_GPUDIRECTTCPX_RX_BINDINGS=\"eth1:22-35,126-139;eth2:22-35,126-139;eth3:74-87,178-191;eth4:74-87,178-191\"",
"NCCL_GPUDIRECTTCPX_PROGRAM_FLOW_STEERING_WAIT_MICROS=500000"

To log NCCL errors, we recommend that you add the following NCCL config:

NCCL_DEBUG=INFO
NCCL_DEBUG_SUBSYS=INIT,NET,ENV,COLL,GRAPH
NCCL_DEBUG_FILE=/DIRECTORY/FILE_NAME.%h.%p

• NCCL_DEBUG=INFO: prints debugging information.
  • For large-scale workloads (64 nodes or more), extensive logging can occur. To avoid this scenario—and unless you specified NCCL_DEBUG_FILE—we recommend setting NCCL_DEBUG=WARN to limit logs to errors only.

• NCCL_DEBUG_SUBSYS: filters the subsystems for which NCCL collects debugging information. We recommend that you collect logs for the following subsystems:

  • INIT: the initialization phase of NCCL.
  • NET: the NCCL network.
  • ENV: the environment variables that NCCL uses.
  • COLL: collective operations.
  • GRAPH: topology detection and graph search.

  If you want to collect logs for different subsystems, see NCCL_DEBUG_SUBSYS in the NCCL documentation for a list of accepted values.

• NCCL_DEBUG_FILE (Optional): directs the NCCL debug logging output to a file that you specify. This variable writes NCCL logs to standard files, which prevents the log output from mixing with application output. This variable also writes logs from different NCCL ranks to different files, which prevents the logs from mixing.

  Use the following filename format:

  /DIRECTORY/FILE_NAME.%h.%p
  

  Replace the following:

  • DIRECTORY: the directory where you want to store the log files.
  • FILE_NAME: the name of the log files.

  The placeholder %h resolves to the hostname of the node, while %p resolves to the process ID (PID) of the process that's generating the log.

For more information about debugging NCCL logs, see Troubleshoot GPUs in GKE.

This section shows the required fields that you must add to your Kubernetes manifests for your Pods to use GPUDirect.

Depending on the type of GPUDirect, do the following:

1. Add the following annotations to the Pod metadata. Without these annotations, hostNetwork:true will be required for the Pod, and privileged:true will be required for the tcpxo-daemon container.

   metadata:
     annotations:
       devices.gke.io/container.tcpxo-daemon: |+
         - path: /dev/nvidia0
         - path: /dev/nvidia1
         - path: /dev/nvidia2
         - path: /dev/nvidia3
         - path: /dev/nvidia4
         - path: /dev/nvidia5
         - path: /dev/nvidia6
         - path: /dev/nvidia7
         - path: /dev/nvidiactl
         - path: /dev/nvidia-uvm
         - path: /dev/dmabuf_import_helper
       networking.gke.io/default-interface: 'eth0'
       networking.gke.io/interfaces: |
         [
           {"interfaceName":"eth0","network":"default"},
           {"interfaceName":"eth1","network":"vpc1"},
           {"interfaceName":"eth2","network":"vpc2"},
           {"interfaceName":"eth3","network":"vpc3"},
           {"interfaceName":"eth4","network":"vpc4"},
           {"interfaceName":"eth5","network":"vpc5"},
           {"interfaceName":"eth6","network":"vpc6"},
           {"interfaceName":"eth7","network":"vpc7"},
           {"interfaceName":"eth8","network":"vpc8"}
         ]
   

2. Add the following fields to the Pod specification:

   spec:
     volumes:
     - name: libraries
       hostPath:
         path: /home/kubernetes/bin/nvidia/lib64
     - name: sys
       hostPath:
         path: /sys
     - name: proc-sys
       hostPath:
         path: /proc/sys
     - name: aperture-devices
       hostPath:
         path: /dev/aperture_devices
   

3. Add the following container to the manifest to run the tcpxo-daemon service. Replace (TCPXO_DAEMON_IMAGE) with the latest image, us-docker.pkg.dev/gce-ai-infra/gpudirect-tcpxo/tcpgpudmarxd-dev:v1.0.17:

   - name: tcpxo-daemon
     image: TCPXO_DAEMON_IMAGE
     imagePullPolicy: Always
     command: ["/bin/sh", "-c"]
     args:
       - |
         set -ex
         chmod 755 /fts/entrypoint_rxdm_container.sh
         /fts/entrypoint_rxdm_container.sh --num_hops=2 --num_nics=8 --uid= --alsologtostderr
     securityContext:
       capabilities:
         add:
           - NET_ADMIN
           - NET_BIND_SERVICE
     volumeMounts:
       - name: libraries
         mountPath: /usr/local/nvidia
       - name: sys
         mountPath: /hostsysfs
       - name: proc-sys
         mountPath: /hostprocsysfs
     env:
       - name: LD_LIBRARY_PATH
         value: /usr/local/nvidia/lib64
   

4. Add the following environment variable to every GPU container:

   env:
   - name: LD_LIBRARY_PATH
     value: /usr/local/nvidia/lib64
   - name: NCCL_FASTRAK_LLCM_DEVICE_DIRECTORY
     value: /dev/aperture_devices
   

5. Add the following volumeMounts to every GPU container. Without aperture_devices setups, privileged:true is required for GPU containers:

   volumeMounts:
     - name: aperture-devices
       mountPath: /dev/aperture_devices
   

6. Add environment variables to configure NCCL options. For details, see Use recommended NCCL configuration settings to improve performance.

A completed Pod specification looks like the following:

apiVersion: v1
kind: Pod
metadata:
name: a3plus-workloads
annotations:
  devices.gke.io/container.tcpxo-daemon: |+
    - path: /dev/nvidia0
    - path: /dev/nvidia1
    - path: /dev/nvidia2
    - path: /dev/nvidia3
    - path: /dev/nvidia4
    - path: /dev/nvidia5
    - path: /dev/nvidia6
    - path: /dev/nvidia7
    - path: /dev/nvidiactl
    - path: /dev/nvidia-uvm
    - path: /dev/dmabuf_import_helper
  networking.gke.io/default-interface: 'eth0'
  networking.gke.io/interfaces: |
    [
      {"interfaceName":"eth0","network":"default"},
      {"interfaceName":"eth1","network":"vpc1"},
      {"interfaceName":"eth2","network":"vpc2"},
      {"interfaceName":"eth3","network":"vpc3"},
      {"interfaceName":"eth4","network":"vpc4"},
      {"interfaceName":"eth5","network":"vpc5"},
      {"interfaceName":"eth6","network":"vpc6"},
      {"interfaceName":"eth7","network":"vpc7"},
      {"interfaceName":"eth8","network":"vpc8"}
    ]
...
containers:
  - name: tcpxo-daemon
    image: TCPXO_DAEMON_IMAGE
    imagePullPolicy: Always
    command: ["/bin/sh", "-c"]
    args:
      - |
        set -ex
        chmod 755 /fts/entrypoint_rxdm_container.sh
        /fts/entrypoint_rxdm_container.sh --num_hops=2 --num_nics=8 --uid= --alsologtostderr
    securityContext:
      capabilities:
        add:
          - NET_ADMIN
          - NET_BIND_SERVICE
    volumeMounts:
      - name: libraries
        mountPath: /usr/local/nvidia
      - name: sys
        mountPath: /hostsysfs
      - name: proc-sys
        mountPath: /hostprocsysfs
    env:
      - name: LD_LIBRARY_PATH
        value: /usr/local/nvidia/lib64
  - name: main-application-container
...
   env:
      - name: LD_LIBRARY_PATH
        value: /usr/local/nvidia/lib64
      - name: NCCL_FASTRAK_LLCM_DEVICE_DIRECTORY
        value: /dev/aperture_devices
    securityContext:
    volumeMounts:
      - name: aperture-devices
        mountPath: /dev/aperture_devices
    resources:
      limits:
        nvidia.com/gpu: 8
volumes:
  - name: libraries
    hostPath:
      path: /home/kubernetes/bin/nvidia
  - name: sys
    hostPath:
      path: /sys
  - name: proc-sys
    hostPath:
      path: /proc/sys
  - name: aperture-devices
    hostPath:
      path: /dev/aperture_devices

1. Add the following annotations to the Pod metadata. Without these annotations, hostNetwork:true will be required for the Pod, and privileged:true will be required for the tcpx-daemon container.

   metadata:
     annotations:
       devices.gke.io/container.tcpx-daemon: |+
         - path: /dev/nvidia0
         - path: /dev/nvidia1
         - path: /dev/nvidia2
         - path: /dev/nvidia3
         - path: /dev/nvidia4
         - path: /dev/nvidia5
         - path: /dev/nvidia6
         - path: /dev/nvidia7
         - path: /dev/nvidiactl
         - path: /dev/nvidia-uvm
       networking.gke.io/default-interface: 'eth0'
       networking.gke.io/interfaces: |
         [
           {"interfaceName":"eth0","network":"default"},
           {"interfaceName":"eth1","network":"vpc1"},
           {"interfaceName":"eth2","network":"vpc2"},
           {"interfaceName":"eth3","network":"vpc3"},
           {"interfaceName":"eth4","network":"vpc4"},
         ]
   

2. Add the following fields to the Pod specification:

   spec:
     volumes:
     - name: libraries
       hostPath:
         path: /home/kubernetes/bin/nvidia/lib64
     - name: sys
       hostPath:
         path: /sys
     - name: proc-sys
       hostPath:
         path: /proc/sys
   

3. Add the following container to the manifest to run the tcpx-daemon service:

   - name: tcpx-daemon
     image: us-docker.pkg.dev/gce-ai-infra/gpudirect-tcpx/tcpgpudmarxd-dev:v2.0.9
     command:
       - /tcpgpudmarxd/build/app/tcpgpudmarxd
       - --gpu_nic_preset
       - a3vm
       - --gpu_shmem_type
       - fd
       - --uds_path
       - /run/tcpx
       - --setup_param
       - \"--verbose 128 2 0 \"
     securityContext:
       capabilities:
           add:
             - NET_ADMIN
     volumeMounts:
       - name: libraries
         mountPath: /usr/local/nvidia/lib64
       - name: tcpx-socket
         mountPath: /run/tcpx
       - name: sys
         mountPath: /hostsysfs
       - name: proc-sys
         mountPath: /hostprocsysfs
     env:
       - name: LD_LIBRARY_PATH
         value: /usr/local/nvidia/lib64
   

4. Add the following volume mounts to any containers that request GPUs:

   volumeMounts:
   - name: tcpx-socket
     mountPath: /tmp
   - name: libraries
     mountPath: /usr/local/nvidia/lib64
   

   Note: The default tcpx-socket path is /tmp for containers that request GPUs. If you set the NCCL_GPUDIRECTTCPX_UNIX_CLIENT_PREFIX environment variable to a value other than /tmp, GKE mounts the tcpx-socket volume to that mountPath.

5. Add environment variables to configure NCCL options. For details, see the Use recommended NCCL configuration settings to improve performance section in this document.

6. Add the following environment variable to every GPU container:

   env:
   - name: LD_LIBRARY_PATH
     value: /usr/local/nvidia/lib64
   

A completed Pod specification looks like the following:

apiVersion: v1
kind: Pod
metadata:
name: a3-gpu-workloads-example
labels:
  name: a3-gpu-workloads-example
annotations:
  devices.gke.io/container.tcpx-daemon: |+
        - path: /dev/nvidia0
        - path: /dev/nvidia1
        - path: /dev/nvidia2
        - path: /dev/nvidia3
        - path: /dev/nvidia4
        - path: /dev/nvidia5
        - path: /dev/nvidia6
        - path: /dev/nvidia7
        - path: /dev/nvidiactl
        - path: /dev/nvidia-uvm
  networking.gke.io/default-interface: 'eth0'
  networking.gke.io/interfaces: |
    [
      {"interfaceName":"eth0","network":"default"},
      {"interfaceName":"eth1","network":"vpc1"},
      {"interfaceName":"eth2","network":"vpc2"},
      {"interfaceName":"eth3","network":"vpc3"},
      {"interfaceName":"eth4","network":"vpc4"}
    ]
spec:
containers:
  - name: tcpx-daemon
    image: us-docker.pkg.dev/gce-ai-infra/gpudirect-tcpx/tcpgpudmarxd-dev:v2.0.11
    imagePullPolicy: Always
    command:
      - /tcpgpudmarxd/build/app/tcpgpudmarxd
      - --gpu_nic_preset
      - a3vm
      - --gpu_shmem_type
      - fd
      - --uds_path
      - /run/tcpx
      - --setup_param
      - \"--verbose 128 2 0 \"
    securityContext:
capabilities:
        add:
          - NET_ADMIN
    volumeMounts:
      - name: libraries
        mountPath: /usr/local/nvidia/lib64
        readOnly: true
      - name: tcpx-socket
        mountPath: /run/tcpx
      - name: sys
        mountPath: /hostsysfs
      - name: proc-sys
        mountPath: /hostprocsysfs
    env:
      - name: LD_LIBRARY_PATH
        value: /usr/local/nvidia/lib64
  - name: a3-gpu-workloads-example
    ...
    volumeMounts:
      - name: tcpx-socket
        mountPath: /tmp
      - name: libraries
        mountPath: /usr/local/nvidia/lib64
        readOnly: true
    resources:
      limits:
        nvidia.com/gpu: 8
    env:
      - name: LD_LIBRARY_PATH
        value: /usr/local/nvidia/lib64
...
volumes:
  - name: libraries
    hostPath:
      path: /home/kubernetes/bin/nvidia/lib64
  - name: tcpx-socket
    emptyDir:
  - name: sys
    hostPath:
      path: /sys
  - name: proc-sys
    hostPath:
      path: /proc/sys

• Read the GPUDirect-TCPXO Release Notes
• Learn more about the best practice to run workloads with GPUDirect-TCPX(O)
• Learn about best practices for GKE networking.
• Learn more about the Nvidia GPUDirect family of technologies for data movement and access on Nvidia GPUs.
• Learn about current GPU version availability and requesting GPUs in GKE.

Except as otherwise noted, the content of this page is licensed under the Creative Commons Attribution 4.0 License, and code samples are licensed under the Apache 2.0 License. For details, see the Google Developers Site Policies. Java is a registered trademark of Oracle and/or its affiliates.

Last updated 2025-08-12 UTC.

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