diff --git a/deploy/charts/ray/values.yaml b/deploy/charts/ray/values.yaml
index 6c1416b5f..eba9705b2 100644
--- a/deploy/charts/ray/values.yaml
+++ b/deploy/charts/ray/values.yaml
@@ -19,7 +19,7 @@ podTypes:
         CPU: 1
         # memory is the memory used by this Pod type.
         # (Used for both requests and limits.)
-        memory: 512Mi
+        memory: 1Gi
         # GPU is the number of NVIDIA GPUs used by this pod type.
         # (Optional, requires GPU nodes with appropriate setup. See https://docs.ray.io/en/master/cluster/kubernetes-gpu.html)
         GPU: 0
@@ -49,7 +49,7 @@ podTypes:
         maxWorkers: 3
         # memory is the memory used by this Pod type.
         # (Used for both requests and limits.)
-        memory: 512Mi
+        memory: 1Gi
         # CPU is the number of CPUs used by this pod type.
         # (Used for both requests and limits. Must be an integer, as Ray does not support fractional CPUs.)
         CPU: 1
diff --git a/deploy/components/example_cluster.yaml b/deploy/components/example_cluster.yaml
index e27eb1be5..9de770d5d 100644
--- a/deploy/components/example_cluster.yaml
+++ b/deploy/components/example_cluster.yaml
@@ -68,9 +68,10 @@ spec:
           resources:
             requests:
               cpu: 1000m
-              memory: 512Mi
+              memory: 1Gi
               ephemeral-storage: 1Gi
             limits:
+              cpu: 1000m
               # The maximum memory that this pod is allowed to use. The
               # limit will be detected by ray and split to use 10% for
               # redis, 30% for the shared memory object store, and the
@@ -78,7 +79,7 @@ spec:
               # the object store size is not set manually, ray will
               # allocate a very large object store in each pod that may
               # cause problems for other pods.
-              memory: 512Mi
+              memory: 1Gi
   - name: worker-node
     # Minimum number of Ray workers of this Pod type.
     minWorkers: 2
@@ -114,9 +115,10 @@ spec:
           resources:
             requests:
               cpu: 1000m
-              memory: 512Mi
+              memory: 1Gi
               ephemeral-storage: 1Gi
             limits:
+              cpu: 1000m
               # The maximum memory that this pod is allowed to use. The
               # limit will be detected by ray and split to use 10% for
               # redis, 30% for the shared memory object store, and the
@@ -124,7 +126,7 @@ spec:
               # the object store size is not set manually, ray will
               # allocate a very large object store in each pod that may
               # cause problems for other pods.
-              memory: 512Mi
+              memory: 1Gi
   # Commands to start Ray on the head node. You don't need to change this.
   # Note dashboard-host is set to 0.0.0.0 so that Kubernetes can port forward.
   headStartRayCommands:
diff --git a/python/ray/tests/kubernetes_e2e/test_k8s_operator_basic.py b/python/ray/tests/kubernetes_e2e/test_k8s_operator_basic.py
index 13d0aa71c..74aa5d893 100644
--- a/python/ray/tests/kubernetes_e2e/test_k8s_operator_basic.py
+++ b/python/ray/tests/kubernetes_e2e/test_k8s_operator_basic.py
@@ -299,7 +299,6 @@ class KubernetesOperatorTest(unittest.TestCase):
 
                 @ray.remote
                 class Test:
-                    @ray.method()
                     def method(self):
                         return "success"
 
diff --git a/release/kubernetes_manual_tests/README.md b/release/kubernetes_manual_tests/README.md
index 12b61f272..4f4967bc5 100644
--- a/release/kubernetes_manual_tests/README.md
+++ b/release/kubernetes_manual_tests/README.md
@@ -7,9 +7,10 @@ If you have issues running them, bug the code owner(s) for OSS Kubernetes suppor
 1. Configure kubectl and Helm 3 to access a K8s cluster.
 2. `git checkout releases/<release version>`
 3. You might have to locally pip install the Ray wheel for the relevant commit (or pip install -e) in a conda env, see Ray client note below.
-4. cd to this directory
-5. `IMAGE=rayproject/ray:<release version> bash k8s_release_tests.sh`
-6. Test outcomes will be reported at the end of the output.
+4. You might have to temporarily delete the file `ray/python/ray/tests/conftest.py`.
+5. cd to this directory
+6. `IMAGE=rayproject/ray:<release version> bash k8s_release_tests.sh`
+7. Test outcomes will be reported at the end of the output.
 
 This runs three tests and does the necessary resource creation/teardown. The tests typically take about 15 minutes to finish.