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chore: run yamlft pre-commit hook (#3729)
only on `ray-operator/config/samples` dir to keep YAML formatting consistent. Signed-off-by: David Xia <[email protected]>
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‎.pre-commit-config.yaml

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@@ -75,6 +75,12 @@ repos:
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- --ignore=.github/pull_request_template.md
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- --disable=MD033
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- repo: https://github.com/google/yamlfmt
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rev: v0.17.0
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hooks:
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- id: yamlfmt
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files: ^ray-operator/config/samples/
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- repo: https://github.com/norwoodj/helm-docs
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rev: v1.14.2
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hooks:

‎.yamlfmt

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formatter:
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indentless_arrays: true
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retain_line_breaks: true

‎ray-operator/config/samples/pytorch-mnist/ray-job.pytorch-mnist.yaml

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@@ -23,38 +23,38 @@ spec:
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template:
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spec:
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containers:
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- name: ray-head
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image: rayproject/ray:2.46.0
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ports:
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- containerPort: 6379
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name: gcs-server
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- containerPort: 8265 # Ray dashboard
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name: dashboard
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- containerPort: 10001
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name: client
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resources:
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limits:
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cpu: "1"
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memory: "4Gi"
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requests:
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cpu: "1"
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memory: "4Gi"
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- name: ray-head
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image: rayproject/ray:2.46.0
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ports:
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- containerPort: 6379
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name: gcs-server
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- containerPort: 8265 # Ray dashboard
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name: dashboard
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- containerPort: 10001
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name: client
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resources:
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limits:
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cpu: "1"
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memory: "4Gi"
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requests:
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cpu: "1"
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memory: "4Gi"
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workerGroupSpecs:
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- replicas: 2
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minReplicas: 1
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maxReplicas: 5
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groupName: small-group
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rayStartParams: {}
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# Pod template
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template:
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spec:
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containers:
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- name: ray-worker
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image: rayproject/ray:2.46.0
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resources:
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limits:
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cpu: "3"
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memory: "4Gi"
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requests:
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cpu: "3"
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memory: "4Gi"
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- replicas: 2
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minReplicas: 1
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maxReplicas: 5
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groupName: small-group
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rayStartParams: {}
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# Pod template
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template:
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spec:
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containers:
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- name: ray-worker
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image: rayproject/ray:2.46.0
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resources:
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limits:
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cpu: "3"
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memory: "4Gi"
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requests:
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cpu: "3"
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memory: "4Gi"

‎ray-operator/config/samples/pytorch-resnet-image-classifier/ray-job.pytorch-image-classifier.yaml

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@@ -30,82 +30,82 @@ spec:
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spec:
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serviceAccountName: pytorch-distributed-training
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containers:
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- name: ray-head
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image: rayproject/ray:2.46.0
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env:
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- name: NUM_WORKERS
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value: "4"
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ports:
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- containerPort: 6379
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name: gcs-server
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- containerPort: 8265
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name: dashboard
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- containerPort: 10001
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name: client
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resources:
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limits:
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cpu: "1"
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memory: "8G"
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requests:
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cpu: "1"
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memory: "8G"
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volumeMounts:
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- mountPath: /tmp/ray
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name: ray-logs
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- mountPath: /mnt/cluster_storage
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name: cluster-storage
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- name: ray-head
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image: rayproject/ray:2.46.0
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env:
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- name: NUM_WORKERS
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value: "4"
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ports:
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- containerPort: 6379
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name: gcs-server
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- containerPort: 8265
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name: dashboard
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- containerPort: 10001
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name: client
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resources:
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limits:
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cpu: "1"
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memory: "8G"
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requests:
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cpu: "1"
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memory: "8G"
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volumeMounts:
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- mountPath: /tmp/ray
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name: ray-logs
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- mountPath: /mnt/cluster_storage
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name: cluster-storage
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volumes:
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- name: ray-logs
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emptyDir: {}
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- name: cluster-storage
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csi:
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driver: gcsfuse.csi.storage.gke.io
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volumeAttributes:
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bucketName: GCS_BUCKET
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mountOptions: "implicit-dirs,uid=1000,gid=100"
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- name: ray-logs
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emptyDir: {}
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- name: cluster-storage
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csi:
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driver: gcsfuse.csi.storage.gke.io
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volumeAttributes:
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bucketName: GCS_BUCKET
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mountOptions: "implicit-dirs,uid=1000,gid=100"
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workerGroupSpecs:
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- replicas: 4
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minReplicas: 4
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maxReplicas: 4
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groupName: gpu-group
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rayStartParams:
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dashboard-host: '0.0.0.0'
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template:
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metadata:
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annotations:
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gke-gcsfuse/volumes: "true"
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gke-gcsfuse/cpu-limit: "0"
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gke-gcsfuse/memory-limit: 5Gi
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gke-gcsfuse/ephemeral-storage-limit: 10Gi
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spec:
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serviceAccountName: pytorch-distributed-training
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tolerations:
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- key: "nvidia.com/gpu"
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operator: "Exists"
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effect: "NoSchedule"
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containers:
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- name: ray-worker
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image: rayproject/ray-ml:2.46.0.0e19ea-py39
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resources:
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limits:
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cpu: "1"
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memory: "8G"
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nvidia.com/gpu: "1"
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requests:
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cpu: "1"
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memory: "8G"
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nvidia.com/gpu: "1"
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volumeMounts:
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- mountPath: /tmp/ray
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name: ray-logs
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- mountPath: /mnt/cluster_storage
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name: cluster-storage
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volumes:
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- name: ray-logs
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emptyDir: {}
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- name: cluster-storage
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csi:
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driver: gcsfuse.csi.storage.gke.io
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volumeAttributes:
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bucketName: GCS_BUCKET
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mountOptions: "implicit-dirs,uid=1000,gid=100"
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- replicas: 4
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minReplicas: 4
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maxReplicas: 4
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groupName: gpu-group
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rayStartParams:
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dashboard-host: '0.0.0.0'
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template:
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metadata:
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annotations:
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gke-gcsfuse/volumes: "true"
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gke-gcsfuse/cpu-limit: "0"
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gke-gcsfuse/memory-limit: 5Gi
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gke-gcsfuse/ephemeral-storage-limit: 10Gi
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spec:
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serviceAccountName: pytorch-distributed-training
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tolerations:
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- key: "nvidia.com/gpu"
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operator: "Exists"
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effect: "NoSchedule"
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containers:
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- name: ray-worker
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image: rayproject/ray-ml:2.46.0.0e19ea-py39
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resources:
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limits:
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cpu: "1"
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memory: "8G"
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nvidia.com/gpu: "1"
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requests:
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cpu: "1"
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memory: "8G"
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nvidia.com/gpu: "1"
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volumeMounts:
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- mountPath: /tmp/ray
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name: ray-logs
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- mountPath: /mnt/cluster_storage
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name: cluster-storage
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volumes:
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- name: ray-logs
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emptyDir: {}
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- name: cluster-storage
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csi:
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driver: gcsfuse.csi.storage.gke.io
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volumeAttributes:
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bucketName: GCS_BUCKET
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mountOptions: "implicit-dirs,uid=1000,gid=100"

‎ray-operator/config/samples/pytorch-text-classifier/ray-job.pytorch-distributed-training.yaml

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operator: "Exists"
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effect: "NoSchedule"
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containers:
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- name: ray-head
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image: rayproject/ray-ml:2.46.0.0e19ea-py311-gpu
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ports:
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- containerPort: 6379
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name: gcs-server
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- containerPort: 8265
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name: dashboard
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- containerPort: 10001
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name: client
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resources:
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limits:
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cpu: "2"
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memory: "8G"
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nvidia.com/gpu: "1"
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requests:
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cpu: "2"
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memory: "8G"
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nvidia.com/gpu: "1"
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volumeMounts:
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- mountPath: /tmp/ray
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name: ray-logs
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- name: ray-head
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image: rayproject/ray-ml:2.46.0.0e19ea-py311-gpu
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ports:
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- containerPort: 6379
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name: gcs-server
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- containerPort: 8265
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name: dashboard
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- containerPort: 10001
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name: client
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resources:
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limits:
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cpu: "2"
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memory: "8G"
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nvidia.com/gpu: "1"
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requests:
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cpu: "2"
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memory: "8G"
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nvidia.com/gpu: "1"
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volumeMounts:
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- mountPath: /tmp/ray
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name: ray-logs
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volumes:
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- name: ray-logs
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emptyDir: {}
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- name: ray-logs
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emptyDir: {}

‎ray-operator/config/samples/ray-cluster-alb-ingress.yaml

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spec:
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ingressClassName: alb
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rules:
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- http:
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paths:
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- path: /
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pathType: Prefix
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backend:
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service:
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name: ray-cluster-kuberay-head-svc # Your head pod service
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port:
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number: 8265
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- http:
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paths:
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- path: /
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pathType: Prefix
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backend:
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service:
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name: ray-cluster-kuberay-head-svc # Your head pod service
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port:
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number: 8265

‎ray-operator/config/samples/ray-cluster.auth.yaml

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---
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apiVersion: v1
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kind: ConfigMap
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metadata:
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name: kube-rbac-proxy
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data:
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config-file.yaml: |+
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config-file.yaml: |
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authorization:
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resourceAttributes:
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namespace: default
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command:
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- bash
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- -c
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- wget -T 2 -q -O- http://localhost:52365/api/local_raylet_healthz | grep
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success && wget -T 10 -q -O- http://localhost:8443/api/gcs_healthz | grep
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success
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- wget -T 2 -q -O- http://localhost:52365/api/local_raylet_healthz | grep success && wget -T 10 -q -O- http://localhost:8443/api/gcs_healthz | grep success
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failureThreshold: 10
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initialDelaySeconds: 10
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periodSeconds: 5
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command:
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- bash
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- -c
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- wget -T 2 -q -O- http://localhost:52365/api/local_raylet_healthz | grep
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success && wget -T 10 -q -O- http://localhost:8443/api/gcs_healthz | grep
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success
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- wget -T 2 -q -O- http://localhost:52365/api/local_raylet_healthz | grep success && wget -T 10 -q -O- http://localhost:8443/api/gcs_healthz | grep success
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failureThreshold: 120
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initialDelaySeconds: 30
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periodSeconds: 5
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- name: kube-rbac-proxy
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image: quay.io/brancz/kube-rbac-proxy:v0.18.1
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args:
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- "--insecure-listen-address=0.0.0.0:8265"
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- "--upstream=http://127.0.0.1:8443/"
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- "--config-file=/etc/kube-rbac-proxy/config-file.yaml"
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- "--logtostderr=true"
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- "--insecure-listen-address=0.0.0.0:8265"
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- "--upstream=http://127.0.0.1:8443/"
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- "--config-file=/etc/kube-rbac-proxy/config-file.yaml"
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- "--logtostderr=true"
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volumeMounts:
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- name: config
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mountPath: /etc/kube-rbac-proxy

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