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Prevent user from defining NCCL_TOPO_FILE when topologyFileConfingMap is set (#189)
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‎tools/pytorchjob-generator/chart/templates/_helpers.tpl‎

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@@ -90,6 +90,12 @@ env:
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fieldPath: metadata.labels['sakkara.member.rank']
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{{- end }}
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{{- if .Values.topologyFileConfigMap }}
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{{- range $variable := .Values.environmentVariables }}
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{{- if eq $variable.name "NCCL_TOPO_FILE" }}
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{{ required "If topologyFileConfigMap is defined, environment variables must not define NCCL_TOPO_FILE" nil }}
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{{- end }}
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{{- end }}
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# Put the path to virtualTopology.xml file that was volume-mounted into the expected environment variable for CUDA
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- name: NCCL_TOPO_FILE
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value: /var/run/nvidia-topologyd/virtualTopology.xml
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{{- end }}

‎tools/pytorchjob-generator/chart/tests/__snapshot__/helloworld_test.yaml.snap‎

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@@ -1362,6 +1362,160 @@ Enabling sshGitConfig injects the envvars, volumes, and volumeMounts:
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- emptyDir:
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medium: Memory
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name: dshm
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Harmless environment variables can be set when topologyFileConfigMap is provided:
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1: |
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apiVersion: workload.codeflare.dev/v1beta2
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kind: AppWrapper
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metadata:
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annotations:
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workload.codeflare.dev.mlbatch/pytorchGeneratorVersion: 1.1.9
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labels:
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kueue.x-k8s.io/queue-name: default-queue
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name: my-job
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namespace: my-namespace
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spec:
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components:
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- template:
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apiVersion: kubeflow.org/v1
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kind: PyTorchJob
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metadata:
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name: my-job
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spec:
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pytorchReplicaSpecs:
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Master:
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replicas: 1
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restartPolicy: Never
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template:
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spec:
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affinity:
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nodeAffinity:
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requiredDuringSchedulingIgnoredDuringExecution:
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nodeSelectorTerms:
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- matchExpressions:
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- key: autopilot.ibm.com/gpuhealth
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operator: NotIn
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values:
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- ERR
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- TESTING
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- EVICT
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containers:
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- command:
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- sh
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- -c
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- |
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echo "Environment variables set by the kubeflow training operator:"
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echo ${MASTER_ADDR}:${MASTER_PORT}
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echo "PYTHONUNBUFFERED:"${PYTHONUNBUFFERED}
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echo My global rank is ${RANK} / ${WORLD_SIZE}
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echo "Other injected environment variables:"
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echo "NVME_MOUNT_PATH: "${NVME_MOUNT_PATH}
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#
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# User commands
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#
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git clone https://github.com/dbarnett/python-helloworld
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cd python-helloworld
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echo executing: torchrun --nnodes=${WORLD_SIZE} --node_rank=${RANK} --nproc_per_node=8 --rdzv_id=101 --rdzv_endpoint="${MASTER_ADDR}:${MASTER_PORT}" helloworld.py
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torchrun --nnodes=${WORLD_SIZE} --node_rank=${RANK} --nproc_per_node=8 --rdzv_id=101 --rdzv_endpoint="${MASTER_ADDR}:${MASTER_PORT}" helloworld.py
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env:
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- name: NCCL_TOPO_FILE
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value: /var/run/nvidia-topologyd/virtualTopology.xml
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- name: EXAMPLE_VAR1
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value: "42"
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image: ghcr.io/foundation-model-stack/base:pytorch-latest-nightly-20230126
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imagePullPolicy: IfNotPresent
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name: pytorch
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resources:
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limits:
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cpu: 500m
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memory: 1Gi
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nvidia.com/gpu: 8
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nvidia.com/roce_gdr: 0
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requests:
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cpu: 500m
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memory: 1Gi
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nvidia.com/gpu: 8
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nvidia.com/roce_gdr: 0
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volumeMounts:
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- mountPath: /var/run/nvidia-topologyd
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name: topology-volume
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- mountPath: /dev/shm
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name: dshm
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imagePullSecrets: []
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priorityClassName: default-priority
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volumes:
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- configMap:
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name: nvidia-topo-gdr
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name: topology-volume
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- emptyDir:
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medium: Memory
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name: dshm
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Worker:
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replicas: 3
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restartPolicy: Never
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template:
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spec:
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affinity:
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nodeAffinity:
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requiredDuringSchedulingIgnoredDuringExecution:
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nodeSelectorTerms:
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- matchExpressions:
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- key: autopilot.ibm.com/gpuhealth
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operator: NotIn
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values:
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- ERR
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- TESTING
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- EVICT
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containers:
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- command:
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- sh
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- -c
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- |
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echo "Environment variables set by the kubeflow training operator:"
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echo ${MASTER_ADDR}:${MASTER_PORT}
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echo "PYTHONUNBUFFERED:"${PYTHONUNBUFFERED}
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echo My global rank is ${RANK} / ${WORLD_SIZE}
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echo "Other injected environment variables:"
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echo "NVME_MOUNT_PATH: "${NVME_MOUNT_PATH}
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#
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# User commands
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#
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git clone https://github.com/dbarnett/python-helloworld
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cd python-helloworld
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echo executing: torchrun --nnodes=${WORLD_SIZE} --node_rank=${RANK} --nproc_per_node=8 --rdzv_id=101 --rdzv_endpoint="${MASTER_ADDR}:${MASTER_PORT}" helloworld.py
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torchrun --nnodes=${WORLD_SIZE} --node_rank=${RANK} --nproc_per_node=8 --rdzv_id=101 --rdzv_endpoint="${MASTER_ADDR}:${MASTER_PORT}" helloworld.py
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env:
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- name: NCCL_TOPO_FILE
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value: /var/run/nvidia-topologyd/virtualTopology.xml
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- name: EXAMPLE_VAR1
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value: "42"
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image: ghcr.io/foundation-model-stack/base:pytorch-latest-nightly-20230126
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imagePullPolicy: IfNotPresent
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name: pytorch
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resources:
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limits:
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cpu: 500m
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memory: 1Gi
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nvidia.com/gpu: 8
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nvidia.com/roce_gdr: 0
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requests:
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cpu: 500m
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memory: 1Gi
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nvidia.com/gpu: 8
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nvidia.com/roce_gdr: 0
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volumeMounts:
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- mountPath: /var/run/nvidia-topologyd
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name: topology-volume
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- mountPath: /dev/shm
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name: dshm
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imagePullSecrets: []
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priorityClassName: default-priority
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volumes:
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- configMap:
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name: nvidia-topo-gdr
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name: topology-volume
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- emptyDir:
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medium: Memory
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name: dshm
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scheduler can be set:
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1: |
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apiVersion: workload.codeflare.dev/v1beta2

‎tools/pytorchjob-generator/chart/tests/helloworld_test.yaml‎

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@@ -270,3 +270,22 @@ tests:
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asserts:
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- matchSnapshot:
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path: spec.components[0].template
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- it: Harmless environment variables can be set when topologyFileConfigMap is provided
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set:
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topologyFileConfigMap: nvidia-topo-gdr
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environmentVariables:
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- name: EXAMPLE_VAR1
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value: 42
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asserts:
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- matchSnapshot:
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path: spec.components[0].template
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- it: NCCL_TOPO_FILE environment variables cannot be set when topologyFileConfigMap is provided
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set:
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topologyFileConfigMap: nvidia-topo-gdr
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environmentVariables:
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- name: NCCL_TOPO_FILE
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value: myFile
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asserts:
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- failedTemplate: {}

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