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https://github.com/vale981/ray
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3293317c40
This PR Adds notes and example on logging for Ray/K8s. Implements an API Reference paging pointing to the configuration guide and the RayCluster CR definition. Takes managed K8s services out of the tabbed structure, to make that page look less sad. Adds a comparison of the KubeRay operator and legacy K8s operator Adds an architecture diagram for the autoscaling sections Fixes some other minor items Adds some info about networking to the configuration guide, removes the previously planned networking page Signed-off-by: Dmitri Gekhtman <dmitri.m.gekhtman@gmail.com>
82 lines
3.2 KiB
YAML
82 lines
3.2 KiB
YAML
# This is a RayCluster configuration for exploration of the 100Gi Ray-on-XGBoost workload.
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# This configuration here modifies the file xgboost-benchmark.yaml in this directory
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# to demonstrate autoscaling.
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#
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# See the discussion in xgboost-benchmark.yaml for further details.
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---
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apiVersion: ray.io/v1alpha1
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kind: RayCluster
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metadata:
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labels:
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controller-tools.k8s.io: "1.0"
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name: raycluster-xgboost-benchmark
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spec:
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# The KubeRay operator will insert the Ray autoscaler sidecar
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# into the Ray head node's pod config:
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enableInTreeAutoscaling: true
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# The version of Ray you are using. Make sure all Ray containers are running this version of Ray.
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rayVersion: '2.0.0'
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headGroupSpec:
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serviceType: ClusterIP
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rayStartParams:
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dashboard-host: '0.0.0.0'
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block: 'true'
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template:
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spec:
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containers:
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# The Ray head container
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- name: ray-head
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image: rayproject/ray-ml:2.0.0
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imagePullPolicy: Always
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# Optimal resource allocation will depend on your Kubernetes infrastructure and might
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# require some experimentation.
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# Setting requests=limits is recommended with Ray. K8s limits are used for Ray-internal
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# resource accounting. K8s requests are not used by Ray.
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resources:
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limits:
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cpu: "14"
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memory: "54Gi"
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requests:
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cpu: "14"
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memory: "54Gi"
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lifecycle:
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preStop:
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exec:
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command: ["/bin/sh","-c","ray stop"]
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workerGroupSpecs:
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# Start with 0 workers. Allow scaling up to 9 workers.
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- replicas: 0
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minReplicas: 0
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maxReplicas: 9
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groupName: large-group
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# the following params are used to complete the ray start: ray start --block --node-ip-address= ...
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rayStartParams:
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block: 'true'
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template:
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spec:
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containers:
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- name: machine-learning # must consist of lower case alphanumeric characters or '-', and must start and end with an alphanumeric character (e.g. 'my-name', or '123-abc')
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image: rayproject/ray-ml:2.0.0
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# Optimal resource allocation will depend on your Kubernetes infrastructure and might
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# require some experimentation.
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# Setting requests=limits is recommended with Ray. K8s limits are used for Ray-internal
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# resource accounting. K8s requests are not used by Ray.
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resources:
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limits:
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# Slightly less than 16 to accommodate placement on 16 vCPU virtual machine.
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cpu: "14"
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memory: "54Gi"
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requests:
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cpu: "14"
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memory: "54Gi"
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lifecycle:
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preStop:
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exec:
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command: ["/bin/sh","-c","ray stop"]
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# Waits for availability of the Ray head's GCS service.
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initContainers:
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# the env var $RAY_IP is set by the operator, with the value of the head service name
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- name: init-myservice
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image: busybox:1.28
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command: ['sh', '-c', "until nslookup $RAY_IP.$(cat /var/run/secrets/kubernetes.io/serviceaccount/namespace).svc.cluster.local; do echo waiting for myservice; sleep 2; done"]
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