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在采用 A3 Edge 的 GKE 集群上运行 NCCL

通用 GPU

本页介绍了如何在自定义 GKE 集群上运行 NVIDIA Collective Communications Library (NCCL) 测试，这些集群使用 A3 Edge 和 GPUDirect-TCPX 网络协议。自定义 GKE 集群是指您使用 gcloud 命令创建的集群。

本页所述的测试可用于以下场景：

如果您的 GKE 集群使用灵活启动节点，请使用双节点基本测试。
如果您的 GKE 集群使用不同类型的节点（例如按需节点或预留绑定节点），请使用具有拓扑感知调度的 NCCL 测试。

准备工作

本页中的测试使用 JobSet 和搭配拓扑感知调度 (TAS) 的 Kueue。在运行任何测试之前，您必须设置集群并执行以下操作：

安装 JobSet。

安装 Kueue。

kubectl apply --server-side -f https://github.com/kubernetes-sigs/kueue/releases/download/v0.16.5/manifests.yaml

使用 JobSet 和 Kueue 设置集群

安装 JobSet 和 Kueue 后，请执行以下步骤：

将以下清单保存为 kueue-config.yaml 文件：

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-edge-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-edge-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-edge-flavor
      resources:
      - name: "cpu"
        nominalQuota: 1000
      - name: "memory"
        nominalQuota: 1000Ti
      - name: "nvidia.com/gpu"
        nominalQuota: 1000
    - name: a3-edge-dws-flavor
      resources:
      - name: "cpu"
        nominalQuota: 1000
      - name: "memory"
        nominalQuota: 1000Ti
      - name: "nvidia.com/gpu"
        nominalQuota: 1000
  admissionChecksStrategy:
    admissionChecks:
    - name: "dws-prov"
      onFlavors: [a3-edge-dws-flavor]
---
apiVersion: kueue.x-k8s.io/v1beta2
kind: LocalQueue
metadata:
  namespace: default
  name: lq-tas
spec:
  clusterQueue: cq-tas

应用清单：
```
kubectl apply -f kueue-config.yaml
```

在运行启用了拓扑感知调度 (TAS) 的工作负载时，您可以使用工作负载清单中的以下注释之一来指定拓扑约束的强制执行严格程度：

kueue.x-k8s.io/podset-required-topology：如果您使用此注解，Kueue 会阻止调度，直到工作负载可以在请求的拓扑约束内进行调度。使用此注解可确保 pod 放置在一起，以实现最佳性能。
kueue.x-k8s.io/podset-preferred-topology：如果您使用此注解，Kueue 会尝试在请求的拓扑约束内调度 Pod，但如果无法实现，它会在不满足拓扑约束的情况下准许工作负载。

对于任一注解，请指定以下某个值作为拓扑约束：

cloud.google.com/gce-topology-block：在同一网络块内调度 pod。
cloud.google.com/gce-topology-subblock：在同一机架内调度 Pod。
cloud.google.com/gce-topology-host：在同一物理主机上调度 pod。

在两个灵活启动节点上进行测试

如需在采用 A3 Edge 灵活启动虚拟机的 GKE 集群上运行 NCCL 测试，请按以下步骤操作。此过程使用 JobSet 清单在两个节点上运行 NCCL 测试。

将以下清单保存为 nccl-tas-jobset.yaml 文件：

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: 0777

将清单应用到您的集群：
```
kubectl apply -f nccl-tas-jobset.yaml
```
检查 JobSet 是否已被允许并正在运行：
```
kubectl get jobset nccl-tas-test
```
等待 JobSet 恢复运行，并等待 Pod 达到 Running 状态。

通过从第一个工作器 pod 执行 allgather.sh 脚本来触发 NCCL 测试：

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-1-0

双节点测试的输出类似于以下内容：

  #                                                              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    : 29.8293

部署使用 TAS 的 NCCL 测试工作负载

如果您有 2 个以上的节点，建议使用以下测试（使用 TAS）。如需在采用 A3 Edge 灵活启动虚拟机的 GKE 集群上运行 NCCL 测试并使用 TAS，请按以下步骤操作。

将以下清单保存为 nccl-jobset-test.yaml 文件。将 NUM_NODES 替换为节点池中的节点数：

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: 8

应用清单：
```
kubectl apply -f nccl-jobset-test.yaml
```
检查工作负载是否已被允许并达到 Completed 状态。

提取与 nccl-ag-worker-0-0-.* 匹配的 Pod 的日志，以查看结果：

kubectl logs $(kubectl get pods -o go-template='{{range .items}}{{.metadata.name}}{{"\n"}}{{end}}' | grep nccl-ag-worker-0-0)

后续步骤

收集并了解 NCCL 日志以进行问题排查，了解测试输出并排查问题。
了解如何排查性能缓慢问题。