feat: upgrade to Ray 2.46.0 (#3547)

* feat: upgrade to Ray 2.46.0

everywhere including default configs, tests, and samples.

Signed-off-by: David Xia <[email protected]>

* Update install/prometheus/overrides.yaml

Signed-off-by: Kai-Hsun Chen <[email protected]>

---------

Signed-off-by: David Xia <[email protected]>
Signed-off-by: Kai-Hsun Chen <[email protected]>
Co-authored-by: Kai-Hsun Chen <[email protected]>

Author: davidxia

.buildkite/setup-env.sh

@@ -32,7 +32,7 @@ apt-get update
 apt-get install -y python3.11 python3-pip
 
 # Install requirements
-pip install --break-system-packages ray[default]==2.41.0
+pip install --break-system-packages ray[default]==2.46.0
 
 # Bypass Git's ownership check due to unconventional user IDs in Docker containers
 git config --global --add safe.directory /workdir

helm-chart/ray-cluster/ci/ci-values.yaml

@@ -1,3 +1,3 @@
 image:
   repository: rayproject/ray
-  tag: 2.41.0
+  tag: 2.46.0

helm-chart/ray-cluster/values.yaml

@@ -7,7 +7,7 @@
 
 image:
   repository: rayproject/ray
-  tag: 2.41.0
+  tag: 2.46.0
   pullPolicy: IfNotPresent
 
 nameOverride: "kuberay"
@@ -26,7 +26,7 @@ common:
 head:
   # rayVersion determines the autoscaler's image version.
   # It should match the Ray version in the image of the containers.
-  # rayVersion: 2.41.0
+  # rayVersion: 2.46.0
   # If enableInTreeAutoscaling is true, the autoscaler sidecar will be added to the Ray head pod.
   # Ray autoscaler integration is supported only for Ray versions >= 1.11.0
   # Ray autoscaler integration is Beta with KubeRay >= 0.3.0 and Ray >= 2.0.0.

kubectl-plugin/pkg/util/constant.go

@@ -1,7 +1,7 @@
 package util
 
 const (
-	RayVersion = "2.41.0"
+	RayVersion = "2.46.0"
 	RayImage   = "rayproject/ray:" + RayVersion
 
 	RayClusterLabelKey   = "ray.io/cluster"

kubectl-plugin/test/e2e/testdata/ray-job.interactive-mode-no-runtime-env.yaml

@@ -5,14 +5,14 @@ metadata:
 spec:
   submissionMode: 'InteractiveMode'
   rayClusterSpec:
-    rayVersion: '2.41.0'
+    rayVersion: '2.46.0'
     headGroupSpec:
       rayStartParams: {}
       template:
         spec:
           containers:
             - name: ray-head
-              image: rayproject/ray:2.41.0
+              image: rayproject/ray:2.46.0
               ports:
                 - containerPort: 6379
                   name: gcs-server
@@ -35,7 +35,7 @@ spec:
           spec:
             containers:
               - name: ray-worker
-                image: rayproject/ray:2.41.0
+                image: rayproject/ray:2.46.0
                 resources:
                   limits:
                     cpu: "1"

kubectl-plugin/test/e2e/testdata/ray-job.interactive-mode.yaml

@@ -14,14 +14,14 @@ spec:
       another_env_var: "second_env_var"
 
   rayClusterSpec:
-    rayVersion: '2.41.0' # should match the Ray version in the image of the containers
+    rayVersion: '2.46.0' # should match the Ray version in the image of the containers
     headGroupSpec:
       rayStartParams: {}
       template:
         spec:
           containers:
             - name: ray-head
-              image: rayproject/ray:2.41.0
+              image: rayproject/ray:2.46.0
               ports:
                 - containerPort: 6379
                   name: gcs-server
@@ -44,7 +44,7 @@ spec:
           spec:
             containers:
               - name: ray-worker
-                image: rayproject/ray:2.41.0
+                image: rayproject/ray:2.46.0
                 resources:
                   limits:
                     cpu: "1"

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

@@ -16,15 +16,15 @@ spec:
 
   # rayClusterSpec specifies the RayCluster instance to be created by the RayJob controller.
   rayClusterSpec:
-    rayVersion: '2.41.0'
+    rayVersion: '2.46.0'
     headGroupSpec:
       rayStartParams: {}
       # Pod template
       template:
         spec:
           containers:
             - name: ray-head
-              image: rayproject/ray:2.41.0
+              image: rayproject/ray:2.46.0
               ports:
                 - containerPort: 6379
                   name: gcs-server
@@ -50,7 +50,7 @@ spec:
           spec:
             containers:
               - name: ray-worker
-                image: rayproject/ray:2.41.0
+                image: rayproject/ray:2.46.0
                 resources:
                   limits:
                     cpu: "3"

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

@@ -16,7 +16,7 @@ spec:
       - transformers>=4.19.1
     working_dir: "https://github.com/ray-project/kuberay/archive/master.zip"
   rayClusterSpec:
-    rayVersion: '2.41.0'
+    rayVersion: '2.46.0'
     headGroupSpec:
       rayStartParams:
         dashboard-host: '0.0.0.0'
@@ -31,7 +31,7 @@ spec:
           serviceAccountName: pytorch-distributed-training
           containers:
             - name: ray-head
-              image: rayproject/ray:2.41.0
+              image: rayproject/ray:2.46.0
               env:
               - name: NUM_WORKERS
                 value: "4"
@@ -85,7 +85,7 @@ spec:
               effect: "NoSchedule"
             containers:
               - name: ray-worker
-                image: rayproject/ray-ml:2.41.0.021baf-py39
+                image: rayproject/ray-ml:2.46.0.0e19ea-py39
                 resources:
                   limits:
                     cpu: "1"

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

@@ -15,7 +15,7 @@ spec:
       - pytorch-lightning==1.6.5
     working_dir: "https://github.com/ray-project/kuberay/archive/master.zip"
   rayClusterSpec:
-    rayVersion: '2.41.0'
+    rayVersion: '2.46.0'
     headGroupSpec:
       rayStartParams:
         dashboard-host: '0.0.0.0'
@@ -27,7 +27,7 @@ spec:
             effect: "NoSchedule"
           containers:
             - name: ray-head
-              image: rayproject/ray-ml:2.41.0.021baf-py311
+              image: rayproject/ray-ml:2.46.0.0e19ea-py311-gpu
               ports:
                 - containerPort: 6379
                   name: gcs-server

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

@@ -60,7 +60,7 @@ spec:
         serviceAccountName: kube-rbac-proxy
         containers:
         - name: ray-head
-          image: rayproject/ray:2.41.0
+          image: rayproject/ray:2.46.0
           ports:
           - containerPort: 6379
             name: gcs
@@ -125,7 +125,7 @@ spec:
       spec:
         containers:
         - name: ray-worker
-          image: rayproject/ray:2.41.0
+          image: rayproject/ray:2.46.0
           resources:
             limits:
               cpu: 1