This page describes how to run NVIDIA Collective Communications Library (NCCL) tests on custom GKE clusters that use
A3 Mega and A3 High with
GPUDirect-TCPXO and GPUDirect-TCPX
networking protocols. A custom GKE cluster is a cluster that you create by
using gcloud commands.
You can use the tests that are described on this page for the following scenarios:
- If your GKE cluster uses Flex-start nodes, then use a basic test on two nodes.
- If your GKE cluster uses different types of nodes such as on-demand or reservation-bound nodes, then use an NCCL test with Topology Aware Scheduling.
Before you begin
The tests on this page use JobSet and Kueue with Topology Aware Scheduling (TAS). Before running any tests, you must set up your cluster and do the following:
Install Kueue.
kubectl apply --server-side -f https://github.com/kubernetes-sigs/kueue/releases/download/v0.16.5/manifests.yaml
Set up your cluster with Jobset and Kueue
After you install JobSet and Kueue, take the following steps:
Save the following manifest as a
kueue-config.yamlfile:A3 High
apiVersion: kueue.x-k8s.io/v1beta2 kind: Topology metadata: name: "gke-default" spec: levels: - nodeLabel: "cloud.google.com/gce-topology-block" - nodeLabel: "cloud.google.com/gce-topology-subblock" - nodeLabel: "cloud.google.com/gce-topology-host" - nodeLabel: "kubernetes.io/hostname" --- apiVersion: kueue.x-k8s.io/v1beta2 kind: ResourceFlavor metadata: name: a3-high-flavor spec: nodeLabels: cloud.google.com/gke-accelerator: nvidia-h100-80gb topologyName: "gke-default" --- apiVersion: kueue.x-k8s.io/v1beta2 kind: ResourceFlavor metadata: name: a3-high-dws-flavor spec: nodeLabels: cloud.google.com/gke-accelerator: nvidia-h100-80gb topologyName: "gke-default" tolerations: - key: "cloud.google.com/gke-queued" operator: "Exists" effect: NoSchedule --- apiVersion: kueue.x-k8s.io/v1beta2 kind: AdmissionCheck metadata: name: dws-prov spec: controllerName: kueue.x-k8s.io/provisioning-request parameters: apiGroup: kueue.x-k8s.io kind: ProvisioningRequestConfig name: dws-config --- apiVersion: kueue.x-k8s.io/v1beta2 kind: ProvisioningRequestConfig metadata: name: dws-config spec: provisioningClassName: queued-provisioning.gke.io podSetUpdates: - key: autoscaling.gke.io/provisioning-request valueFromProvisioningClassDetail: ResizeRequestName managedResources: - nvidia.com/gpu --- apiVersion: kueue.x-k8s.io/v1beta2 kind: ClusterQueue metadata: name: cq-tas spec: namespaceSelector: {} clusterQueueingStrategy: BestEffortFIFO resourceGroups: - flavors: - name: a3-high-flavor resources: - name: "cpu" nominalQuota: 1000 - name: "memory" nominalQuota: 1000Ti - name: "nvidia.com/gpu" nominalQuota: 1000 - name: a3-high-dws-flavor resources: - name: "cpu" nominalQuota: 1000 - name: "memory" nominalQuota: 1000Ti - name: "nvidia.com/gpu" nominalQuota: 1000 admissionChecksStrategy: admissionChecks: - name: "dws-prov" onFlavors: [a3-high-dws-flavor] --- apiVersion: kueue.x-k8s.io/v1beta2 kind: LocalQueue metadata: namespace: default name: lq-tas spec: clusterQueue: cq-tasA3 Mega
apiVersion: kueue.x-k8s.io/v1beta2 kind: Topology metadata: name: "gke-default" spec: levels: - nodeLabel: "cloud.google.com/gce-topology-block" - nodeLabel: "cloud.google.com/gce-topology-subblock" - nodeLabel: "cloud.google.com/gce-topology-host" - nodeLabel: "kubernetes.io/hostname" --- apiVersion: kueue.x-k8s.io/v1beta2 kind: ResourceFlavor metadata: name: a3-mega-flavor spec: nodeLabels: cloud.google.com/gke-accelerator: nvidia-h100-mega-80gb topologyName: "gke-default" --- apiVersion: kueue.x-k8s.io/v1beta2 kind: ResourceFlavor metadata: name: a3-mega-dws-flavor spec: nodeLabels: cloud.google.com/gke-accelerator: nvidia-h100-mega-80gb topologyName: "gke-default" tolerations: - key: "cloud.google.com/gke-queued" operator: "Exists" effect: NoSchedule --- apiVersion: kueue.x-k8s.io/v1beta2 kind: AdmissionCheck metadata: name: dws-prov spec: controllerName: kueue.x-k8s.io/provisioning-request parameters: apiGroup: kueue.x-k8s.io kind: ProvisioningRequestConfig name: dws-config --- apiVersion: kueue.x-k8s.io/v1beta2 kind: ProvisioningRequestConfig metadata: name: dws-config spec: provisioningClassName: queued-provisioning.gke.io podSetUpdates: - key: autoscaling.gke.io/provisioning-request valueFromProvisioningClassDetail: ResizeRequestName managedResources: - nvidia.com/gpu --- apiVersion: kueue.x-k8s.io/v1beta2 kind: ClusterQueue metadata: name: cq-tas spec: namespaceSelector: {} clusterQueueingStrategy: BestEffortFIFO resourceGroups: - flavors: - name: a3-mega-flavor resources: - name: "cpu" nominalQuota: 1000 - name: "memory" nominalQuota: 1000Ti - name: "nvidia.com/gpu" nominalQuota: 1000 - name: a3-mega-dws-flavor resources: - name: "cpu" nominalQuota: 1000 - name: "memory" nominalQuota: 1000Ti - name: "nvidia.com/gpu" nominalQuota: 1000 admissionChecksStrategy: admissionChecks: - name: "dws-prov" onFlavors: [a3-mega-dws-flavor] --- apiVersion: kueue.x-k8s.io/v1beta2 kind: LocalQueue metadata: namespace: default name: lq-tas spec: clusterQueue: cq-tasApply the manifest:
kubectl apply -f kueue-config.yaml
When running workloads with Topology Aware Scheduling (TAS) enabled, you can specify how strictly topology constraints are enforced by using one of the following annotations in your workload manifest:
kueue.x-k8s.io/podset-required-topology: If you use this annotation, Kueue blocks scheduling until the workload can be scheduled within the requested topology constraint. Use this annotation to ensure that pods are placed together for optimal performance.kueue.x-k8s.io/podset-preferred-topology: If you use this annotation, Kueue attempts to schedule pods within the requested topology constraint, but if that's not possible, it admits the workload without meeting topology constraints.
For either annotation, specify one of the following values as the topology constraint:
cloud.google.com/gce-topology-block: Schedules pods within the same network block.cloud.google.com/gce-topology-subblock: Schedules pods within the same rack.cloud.google.com/gce-topology-host: Schedules pods on the same physical host.
Test on two Flex-start nodes
To run NCCL tests on a GKE cluster that uses A3 Mega or A3 High Flex-start VMs, use the following procedure. This procedure uses a JobSet manifest to run an NCCL test on two nodes.
Save the following manifest as a
nccl-tas-jobset.yamlfile:A3 High
apiVersion: v1 kind: ConfigMap metadata: name: nccl-config data: allgather.sh: | #!/bin/bash for script in /configs/*; do name=$(basename $script) cp $script "/scripts/$name" chmod +x "/scripts/$name" done /scripts/init_ssh.sh ${@}; pushd /scripts; /scripts/gen_hostfiles.sh ${@}; popd; /scripts/run-allgather.sh 8 eth1,eth2,eth3,eth4 1M 512M ${#}; --- apiVersion: jobset.x-k8s.io/v1alpha2 kind: JobSet metadata: name: nccl-tas-test labels: kueue.x-k8s.io/queue-name: lq-tas spec: suspend: true network: enableDNSHostnames: true replicatedJobs: - name: worker replicas: 2 template: spec: parallelism: 1 completions: 1 template: metadata: annotations: kueue.x-k8s.io/podset-preferred-topology: "cloud.google.com/gce-topology-block" networking.gke.io/default-interface: 'eth0' networking.gke.io/interfaces: | [ {"interfaceName":"eth0","network":"default"}, {"interfaceName":"eth1","network":"vpc0"}, {"interfaceName":"eth2","network":"vpc1"}, {"interfaceName":"eth3","network":"vpc2"}, {"interfaceName":"eth4","network":"vpc3"} ] spec: terminationGracePeriodSeconds: 0 nodeSelector: cloud.google.com/gke-accelerator: nvidia-h100-80gb tolerations: - key: cloud.google.com/gke-queued effect: NoSchedule value: "true" - key: "nvidia.com/gpu" operator: "Exists" effect: "NoSchedule" setHostnameAsFQDN: true containers: - name: tcpx-daemon image: us-docker.pkg.dev/gce-ai-infra/gpudirect-tcpx/tcpgpudmarxd-dev:v2.0.11 command: - /tcpgpudmarxd/build/app/tcpgpudmarxd - --gpu_nic_preset - a3vm - --gpu_shmem_type - fd - --uds_path - /run/tcpx - --setup_param - "--verbose 128 2 0 " securityContext: privileged: true 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 - name: nccl-test image: us-docker.pkg.dev/gce-ai-infra/gpudirect-tcpx/nccl-plugin-gpudirecttcpx-dev:v3.1.8 command: - bash - -c - | /scripts/container_entry.sh daemon; sleep infinity; securityContext: privileged: true volumeMounts: - name: tcpx-socket mountPath: /tmp - name: libraries mountPath: /usr/local/nvidia/lib64 - name: nccl-config mountPath: /configs - name: shared-memory mountPath: /dev/shm resources: limits: cpu: "200" memory: "1800Gi" nvidia.com/gpu: 8 requests: cpu: "200" memory: "1800Gi" nvidia.com/gpu: 8 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 - name: shared-memory emptyDir: medium: Memory sizeLimit: 250Gi - name: nccl-config configMap: name: nccl-config defaultMode: 0777A3 Mega
apiVersion: v1 kind: ConfigMap metadata: name: nccl-configmap data: allgather.sh: | #!/bin/bash service ssh restart; /scripts/init_ssh.sh ${@}; pushd /scripts; /scripts/gen_hostfiles.sh ${@}; popd; # Set up environment variables for GPUDirect-TCPXO export LD_LIBRARY_PATH=/usr/local/nvidia/lib64 export NCCL_FASTRAK_CTRL_DEV=eth0 export NCCL_FASTRAK_IFNAME=eth1,eth2,eth3,eth4,eth5,eth6,eth7,eth8 export NCCL_SOCKET_IFNAME=eth0 export NCCL_CROSS_NIC=0 export NCCL_ALGO=Ring,Tree export NCCL_PROTO=Simple export NCCL_NET_GDR_LEVEL=PIX # Run the benchmark /scripts/demo-run-nccl-test-tcpxo-via-mpi.sh --- apiVersion: jobset.x-k8s.io/v1alpha2 kind: JobSet metadata: name: nccl-tas-test labels: kueue.x-k8s.io/queue-name: lq-tas spec: ttlSecondsAfterFinished: 1200 suspend: true network: enableDNSHostnames: true replicatedJobs: - name: worker replicas: 2 template: spec: parallelism: 1 completions: 1 template: metadata: annotations: kueue.x-k8s.io/podset-preferred-topology: "cloud.google.com/gce-topology-block" networking.gke.io/default-interface: 'eth0' networking.gke.io/interfaces: | [ {"interfaceName":"eth0","network":"default"}, {"interfaceName":"eth1","network":"vpc0"}, {"interfaceName":"eth2","network":"vpc1"}, {"interfaceName":"eth3","network":"vpc2"}, {"interfaceName":"eth4","network":"vpc3"}, {"interfaceName":"eth5","network":"vpc4"}, {"interfaceName":"eth6","network":"vpc5"}, {"interfaceName":"eth7","network":"vpc6"}, {"interfaceName":"eth8","network":"vpc7"} ] spec: activeDeadlineSeconds: 3600 restartPolicy: Never nodeSelector: cloud.google.com/gke-accelerator: nvidia-h100-mega-80gb tolerations: - key: cloud.google.com/gke-queued effect: NoSchedule value: "true" - key: "nvidia.com/gpu" operator: "Exists" effect: "NoSchedule" setHostnameAsFQDN: true volumes: - name: nvidia hostPath: path: /home/kubernetes/bin/nvidia - name: lib64 hostPath: path: /lib64 - name: proc hostPath: path: /proc - name: shared-memory emptyDir: medium: "Memory" sizeLimit: 250Gi - name: nccl-config configMap: name: nccl-configmap defaultMode: 0755 containers: - name: nccl-test image: us-docker.pkg.dev/gce-ai-infra/gpudirect-tcpxo/nccl-plugin-gpudirecttcpx-dev:v1.0.15 stdin: true tty: true securityContext: privileged: true env: - name: LD_LIBRARY_PATH value: /usr/local/nvidia/lib64 volumeMounts: - name: nvidia mountPath: /usr/local/nvidia - name: shared-memory mountPath: /dev/shm - name: nccl-config mountPath: /configs resources: limits: cpu: "200" memory: "3700Gi" nvidia.com/gpu: 8 requests: cpu: "200" memory: "3700Gi" nvidia.com/gpu: 8 - name: tcpxo-daemon image: us-docker.pkg.dev/gce-ai-infra/gpudirect-tcpxo/tcpgpudmarxd-dev:v1.0.21 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: privileged: true capabilities: add: - NET_ADMIN - NET_BIND_SERVICE volumeMounts: - name: nvidia mountPath: /usr/local/nvidia/lib64 - name: proc mountPath: /proc env: - name: LD_LIBRARY_PATH value: /usr/local/nvidia/lib64Apply the manifest to your cluster:
kubectl apply -f nccl-tas-jobset.yamlCheck that the JobSet is admitted and running:
kubectl get jobset nccl-tas-testWait for the JobSet to be unsuspended and Pods to reach the
Runningstatus.Trigger the NCCL test by executing the
allgather.shscript from the first worker Pod:kubectl exec --stdin --tty --container=nccl-test nccl-tas-test-worker-0-0 -- /configs/allgather.sh nccl-tas-test-worker-0-0 nccl-tas-test-worker-1-0The output for a two-node test is similar to the following:
A3 High
# 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 ... 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 : 29.8293A3 Mega
# 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 ... 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
Deploy an NCCL test workload with TAS
If you have more than two nodes, we recommend using the following test, which uses (TAS). To run NCCL tests with TAS on a GKE cluster that uses A3 Mega or A3 High Flex-start VMs, use the following procedure.
Save the following manifest as a
nccl-jobset-test.yamlfile. ReplaceNUM_NODESwith the number of nodes in the node pool:A3 High
apiVersion: jobset.x-k8s.io/v1alpha2 kind: JobSet metadata: name: nccl-ag labels: kueue.x-k8s.io/queue-name: lq-tas spec: ttlSecondsAfterFinished: 1200 suspend: true network: enableDNSHostnames: true replicatedJobs: - name: worker template: spec: parallelism: NUM_NODES completions: NUM_NODES template: metadata: annotations: kueue.x-k8s.io/podset-preferred-topology: "cloud.google.com/gce-topology-subblock" networking.gke.io/default-interface: 'eth0' networking.gke.io/interfaces: | [ {"interfaceName":"eth0","network":"default"}, {"interfaceName":"eth1","network":"vpc0"}, {"interfaceName":"eth2","network":"vpc1"}, {"interfaceName":"eth3","network":"vpc2"}, {"interfaceName":"eth4","network":"vpc3"} ] spec: activeDeadlineSeconds: 3600 restartPolicy: Never nodeSelector: cloud.google.com/gke-accelerator: nvidia-h100-80gb tolerations: - key: cloud.google.com/gke-queued operator: "Exists" effect: NoSchedule - key: "nvidia.com/gpu" operator: "Exists" effect: "NoSchedule" setHostnameAsFQDN: true volumes: - name: proc hostPath: path: /proc - name: nvidia hostPath: path: /home/kubernetes/bin/nvidia - name: libraries hostPath: path: /home/kubernetes/bin/nvidia/lib64 - name: tcpx-socket emptyDir: {} - name: shared-memory emptyDir: medium: "Memory" sizeLimit: 250Gi containers: - name: tcpx-daemon image: us-docker.pkg.dev/gce-ai-infra/gpudirect-tcpx/tcpgpudmarxd-dev:v2.0.11 command: - /tcpgpudmarxd/build/app/tcpgpudmarxd - --gpu_nic_preset - a3vm - --gpu_shmem_type - fd - --uds_path - /run/tcpx - --setup_param - "--verbose 128 2 0 " securityContext: privileged: true volumeMounts: - name: tcpx-socket mountPath: /run/tcpx - name: libraries mountPath: /usr/local/nvidia/lib64 - name: nccl-test stdin: true tty: true image: us-docker.pkg.dev/gce-ai-infra/gpudirect-tcpx/nccl-plugin-gpudirecttcpx-dev:v3.1.8 securityContext: privileged: true env: - name: MY_NODE_NAME valueFrom: fieldRef: fieldPath: spec.nodeName - name: OMPI_ALLOW_RUN_AS_ROOT value: "1" - name: OMPI_ALLOW_RUN_AS_ROOT_CONFIRM value: "1" - name: N_NODES value: "NUM_NODES" - name: LD_LIBRARY_PATH value: /usr/local/nvidia/lib64 command: - bash - -c - | /scripts/container_entry.sh daemon & export POSTFIX=$(hostname | cut -d . -f 2-) export WORKERS_BASENAME=$(hostname | cut -d . -f 1 | rev | cut -d - -f 2- | rev ) export NODE_RANK=$JOB_COMPLETION_INDEX for i in `seq 0 $(($N_NODES-1))`; do OTHER=${WORKERS_BASENAME}-${i}.${POSTFIX} until ssh -p 222 -o StrictHostKeyChecking=no $OTHER hostname; do sleep 10 done echo ${OTHER} port=222 slots=8 | tee -a /tmp/hostfile; done if [[ "${NODE_RANK}" -eq "0" ]]; then /scripts/run-allgather.sh 8 eth1,eth2,eth3,eth4 1M 512M ${N_NODES} else while ping -c 1 ${WORKERS_BASENAME}-0.${POSTFIX}; do sleep 5 done fi exit 0 volumeMounts: - name: nvidia mountPath: /usr/local/nvidia - name: tcpx-socket mountPath: /tmp - name: libraries mountPath: /usr/local/nvidia/lib64 - name: shared-memory mountPath: /dev/shm resources: limits: cpu: "200" memory: "1800Gi" nvidia.com/gpu: 8 requests: cpu: "200" memory: "1800Gi" nvidia.com/gpu: 8A3 Mega
apiVersion: jobset.x-k8s.io/v1alpha2 kind: JobSet metadata: name: nccl-ag labels: kueue.x-k8s.io/queue-name: lq-tas spec: ttlSecondsAfterFinished: 1200 suspend: true network: enableDNSHostnames: true replicatedJobs: - name: worker template: spec: parallelism: NUM_NODES completions: NUM_NODES template: metadata: annotations: kueue.x-k8s.io/podset-preferred-topology: "cloud.google.com/gce-topology-subblock" networking.gke.io/default-interface: 'eth0' networking.gke.io/interfaces: | [ {"interfaceName":"eth0","network":"default"}, {"interfaceName":"eth1","network":"vpc0"}, {"interfaceName":"eth2","network":"vpc1"}, {"interfaceName":"eth3","network":"vpc2"}, {"interfaceName":"eth4","network":"vpc3"}, {"interfaceName":"eth5","network":"vpc4"}, {"interfaceName":"eth6","network":"vpc5"}, {"interfaceName":"eth7","network":"vpc6"}, {"interfaceName":"eth8","network":"vpc7"} ] spec: activeDeadlineSeconds: 3600 restartPolicy: Never nodeSelector: cloud.google.com/gke-accelerator: nvidia-h100-mega-80gb tolerations: - key: cloud.google.com/gke-queued operator: "Exists" effect: NoSchedule - key: "nvidia.com/gpu" operator: "Exists" effect: "NoSchedule" setHostnameAsFQDN: true volumes: - name: proc hostPath: path: /proc - name: nvidia hostPath: path: /home/kubernetes/bin/nvidia - name: lib64 hostPath: path: /lib64 - name: shared-memory emptyDir: medium: "Memory" sizeLimit: 250Gi containers: - name: nccl-test stdin: true tty: true image: us-docker.pkg.dev/gce-ai-infra/gpudirect-tcpxo/nccl-plugin-tcpxo-diagnostic:v1.0.6 securityContext: privileged: true env: - name: MY_NODE_NAME valueFrom: fieldRef: fieldPath: spec.nodeName - name: OMPI_ALLOW_RUN_AS_ROOT value: "1" - name: OMPI_ALLOW_RUN_AS_ROOT_CONFIRM value: "1" - name: N_NODES value: "NUM_NODES" - name: NCCL_SOCKET_IFNAME value: eth0 - name: NCCL_FASTRAK_CTRL_DEV value: eth0 - name: NCCL_FASTRAK_IFNAME value: eth1,eth2,eth3,eth4,eth5,eth6,eth7,eth8 - name: NCCL_CROSS_NIC value: "0" - name: NCCL_ALGO value: Ring,Tree - name: NCCL_PROTO value: Simple - name: NCCL_NET_GDR_LEVEL value: PIX - name: LD_LIBRARY_PATH value: /usr/local/nvidia/lib64 command: - bash - -c - | set -x /scripts/container_entry.sh daemon & export POSTFIX=$(hostname | cut -d . -f 2-) export WORKERS_BASENAME=$(hostname | cut -d . -f 1 | rev | cut -d - -f 2- | rev ) export NODE_RANK=$JOB_COMPLETION_INDEX for i in `seq 0 $(($N_NODES-1))`; do OTHER=${WORKERS_BASENAME}-${i}.${POSTFIX} until ssh -p 222 -o StrictHostKeyChecking=no $OTHER hostname; do sleep 10 done echo ${OTHER} port=222 slots=8 | tee -a /tmp/hostfile; done if [[ "${NODE_RANK}" -eq "0" ]]; then export NCCL_TESTS_SPLIT_MASK="0x0"; ENV_VARS=$(echo ${!NCCL*} ${!OMPI*} LD_LIBRARY_PATH PATH | sed 's/ / -x /g') mpirun --hostfile /tmp/hostfile \ -x $ENV_VARS \ -mca plm_rsh_no_tree_spawn 1 \ --mca orte_keep_fqdn_hostnames 1 \ --mca btl self,tcp \ --mca btl_tcp_if_include eth0 \ --bind-to none \ --mca plm_rsh_agent "ssh -q -o LogLevel=ERROR -o StrictHostKeyChecking=no -p 222" \ /third_party/nccl-tests/build/all_gather_perf -b 1K -e 8G -f 2 -g 1 -w 5 --iters 100 -c 1 else while ping -c 1 ${WORKERS_BASENAME}-0.${POSTFIX}; do sleep 5 done fi exit 0 volumeMounts: - name: nvidia mountPath: /usr/local/nvidia - name: lib64 mountPath: /lib64 - name: shared-memory mountPath: /dev/shm resources: limits: cpu: "200" memory: "3700Gi" nvidia.com/gpu: 8 requests: cpu: "200" memory: "3700Gi" nvidia.com/gpu: 8 - name: tcpxo-daemon image: us-docker.pkg.dev/gce-ai-infra/gpudirect-tcpxo/tcpxo-daemon:v1.0.1 imagePullPolicy: Always command: - bash - -c - | /usr/bin/tcpxo_daemon securityContext: privileged: true volumeMounts: - name: nvidia mountPath: /usr/local/nvidia - name: proc mountPath: /proc env: - name: LD_LIBRARY_PATH value: /usr/local/nvidia/lib64Apply the manifest:
kubectl apply -f nccl-jobset-test.yamlCheck that the workload is admitted and reaches the
Completedstate.Fetch logs for the Pod matching
nccl-ag-worker-0-0-.*to see the results:kubectl logs $(kubectl get pods -o go-template='{{range .items}}{{.metadata.name}}{{"\n"}}{{end}}' | grep nccl-ag-worker-0-0)
What's next
- Collect and Understand NCCL Logs for Troubleshooting to understand the test outputs and troubleshoot issues.
- Learn about troubleshooting slow performance.