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Oct 17, 2023
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update aw examples for the kuberay integration doc #663

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48 changes: 22 additions & 26 deletions doc/usage/examples/kuberay/config/aw-raycluster-1.yaml
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Original file line number Diff line number Diff line change
@@ -1,4 +1,4 @@
apiVersion: mcad.ibm.com/v1beta1
apiVersion: workload.codeflare.dev/v1beta1
kind: AppWrapper
metadata:
name: raycluster-complete-1
Expand All @@ -8,29 +8,26 @@ spec:
GenericItems:
- replicas: 1
custompodresources:
# Optional section that specifies resource requirements
# for non-standard k8s resources, follows same format as
# that of standard k8s resources.
# Each item in the custompodresources stanza should include resources consumed by target Item.
# In this example, the 2 items correspond to 1 Ray head pod and 1 Ray worker pod
- replicas: 1
limits:
cpu: 2
memory: 8G
cpu: 1
memory: 2G
nvidia.com/gpu: 0
requests:
cpu: 2
memory: 8G
cpu: 1
memory: 2G
nvidia.com/gpu: 0
# The replica should match the number of worker pods
- replicas: 1
limits:
cpu: 8
memory: 8G
cpu: 2
memory: 2G
nvidia.com/gpu: 0
requests:
cpu: 8
memory: 8G
cpu: 2
memory: 2G
nvidia.com/gpu: 0
generictemplate:
# The resource requests and limits in this config are too small for production!
Expand Down Expand Up @@ -65,7 +62,7 @@ spec:
spec:
containers:
- name: ray-head
image: rayproject/ray:2.5.0
image: quay.io/project-codeflare/ray:2.5.0-py38-cu116
ports:
- containerPort: 6379
name: gcs
Expand All @@ -88,21 +85,21 @@ spec:
# entire Kubernetes node on which it is scheduled.
resources:
limits:
cpu: "2"
memory: "8G"
cpu: "1"
memory: "2G"
requests:
# For production use-cases, we recommend specifying integer CPU reqests and limits.
# We also recommend setting requests equal to limits for both CPU and memory.
cpu: "2"
memory: "8G"
cpu: "1"
memory: "2G"
volumes:
- name: ray-logs
emptyDir: {}
workerGroupSpecs:
# the pod replicas in this group typed worker
- replicas: 1
minReplicas: 1
maxReplicas: 10
maxReplicas: 1
# logical group name, for this called small-group, also can be functional
groupName: small-group
# If worker pods need to be added, we can increment the replicas.
Expand All @@ -124,7 +121,7 @@ spec:
spec:
containers:
- name: ray-worker
image: rayproject/ray:2.5.0
image: quay.io/project-codeflare/ray:2.5.0-py38-cu116
lifecycle:
preStop:
exec:
Expand All @@ -142,19 +139,18 @@ spec:
# entire Kubernetes node on which it is scheduled.
resources:
limits:
cpu: "8"
memory: "8G"
# For production use-cases, we recommend specifying integer CPU reqests and limits.
cpu: "2"
memory: "2G"
# For production use-cases, we recommend specifying integer CPU requests and limits.
# We also recommend setting requests equal to limits for both CPU and memory.
requests:
# For production use-cases, we recommend specifying integer CPU reqests and limits.
# For production use-cases, we recommend specifying integer CPU requests and limits.
# We also recommend setting requests equal to limits for both CPU and memory.
cpu: "8"
cpu: "2"
# For production use-cases, we recommend allocating at least 8Gb memory for each Ray container.
memory: "8G"
memory: "2G"
# use volumes
# Refer to https://kubernetes.io/docs/concepts/storage/volumes/
volumes:
- name: ray-logs
emptyDir: {}

46 changes: 21 additions & 25 deletions doc/usage/examples/kuberay/config/aw-raycluster.yaml
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -8,29 +8,26 @@ spec:
GenericItems:
- replicas: 1
custompodresources:
# Optional section that specifies resource requirements
# for non-standard k8s resources, follows same format as
# that of standard k8s resources.
# Each item in the custompodresources stanza should include resources consumed by target Item.
# In this example, the 2 items correspond to 1 Ray head pod and 1 Ray worker pod
- replicas: 1
limits:
cpu: 2
memory: 8G
cpu: 1
memory: 2G
nvidia.com/gpu: 0
requests:
cpu: 2
memory: 8G
cpu: 1
memory: 2G
nvidia.com/gpu: 0
# The replica should match the number of worker pods
- replicas: 1
limits:
cpu: 8
memory: 8G
cpu: 2
memory: 2G
nvidia.com/gpu: 0
requests:
cpu: 8
memory: 8G
cpu: 2
memory: 2G
nvidia.com/gpu: 0
generictemplate:
# The resource requests and limits in this config are too small for production!
Expand Down Expand Up @@ -65,7 +62,7 @@ spec:
spec:
containers:
- name: ray-head
image: rayproject/ray:2.5.0
image: quay.io/project-codeflare/ray:2.5.0-py38-cu116
ports:
- containerPort: 6379
name: gcs
Expand All @@ -88,21 +85,21 @@ spec:
# entire Kubernetes node on which it is scheduled.
resources:
limits:
cpu: "2"
memory: "8G"
cpu: "1"
memory: "2G"
requests:
# For production use-cases, we recommend specifying integer CPU reqests and limits.
# We also recommend setting requests equal to limits for both CPU and memory.
cpu: "2"
memory: "8G"
cpu: "1"
memory: "2G"
volumes:
- name: ray-logs
emptyDir: {}
workerGroupSpecs:
# the pod replicas in this group typed worker
- replicas: 1
minReplicas: 1
maxReplicas: 10
maxReplicas: 1
# logical group name, for this called small-group, also can be functional
groupName: small-group
# If worker pods need to be added, we can increment the replicas.
Expand All @@ -124,7 +121,7 @@ spec:
spec:
containers:
- name: ray-worker
image: rayproject/ray:2.5.0
image: quay.io/project-codeflare/ray:2.5.0-py38-cu116
lifecycle:
preStop:
exec:
Expand All @@ -142,19 +139,18 @@ spec:
# entire Kubernetes node on which it is scheduled.
resources:
limits:
cpu: "8"
memory: "8G"
# For production use-cases, we recommend specifying integer CPU reqests and limits.
cpu: "2"
memory: "2G"
# For production use-cases, we recommend specifying integer CPU requests and limits.
# We also recommend setting requests equal to limits for both CPU and memory.
requests:
# For production use-cases, we recommend specifying integer CPU reqests and limits.
# For production use-cases, we recommend specifying integer CPU requests and limits.
# We also recommend setting requests equal to limits for both CPU and memory.
cpu: "8"
cpu: "2"
# For production use-cases, we recommend allocating at least 8Gb memory for each Ray container.
memory: "8G"
memory: "2G"
# use volumes
# Refer to https://kubernetes.io/docs/concepts/storage/volumes/
volumes:
- name: ray-logs
emptyDir: {}

4 changes: 2 additions & 2 deletions doc/usage/examples/kuberay/kuberay-mcad.md
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Original file line number Diff line number Diff line change
Expand Up @@ -27,7 +27,7 @@ This integration will help in queuing on [kuberay](https://github.com/ray-projec
Install kuberay operator using the [instructions](https://github.com/ray-project/kuberay#quick-start). For example, install kuberay v0.6.0 from remote helm repo:
```
helm repo add kuberay https://ray-project.github.io/kuberay-helm/
helm install kuberay-operator kuberay/kuberay-operator --version 0.6.0
helm install kuberay-operator kuberay/kuberay-operator --set image.repository=quay.io/kuberay/operator --set image.tag=v0.6.0
```

- OpenShift cluster:
Expand All @@ -38,7 +38,7 @@ This integration will help in queuing on [kuberay](https://github.com/ray-projec
#### Steps


- Submit the RayCluster custom resource to MCAD as AppWrapper using the [aw-raycluster.yaml](doc/usage/examples/kuberay/config/aw-raycluster.yaml) exmaple:
- Submit the RayCluster custom resource to MCAD as AppWrapper using the [aw-raycluster.yaml](config/aw-raycluster.yaml) exmaple:
```bash
kubectl create -f doc/usage/examples/kuberay/config/aw-raycluster.yaml
```
Expand Down