Use server-side apply

Signed-off-by: Yuki Iwai <[email protected]>
kubeflow · Jan 20, 2023 · a2a8dc6 · a2a8dc6
1 parent 8f52a72
commit a2a8dc6
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diff --git a/README.md b/README.md
@@ -31,13 +31,13 @@ run distributed or non-distributed TensorFlow/PyTorch/Apache MXNet/XGBoost/MPI j
 ### Master Branch
 
 ```bash
-kubectl apply -k "github.com/kubeflow/training-operator/manifests/overlays/standalone"
+kubectl apply --server-side -k "github.com/kubeflow/training-operator/manifests/overlays/standalone"
 ```
 
 ### Stable Release
 
 ```bash
-kubectl apply -k "github.com/kubeflow/training-operator/manifests/overlays/standalone?ref=v1.5.0"
+kubectl apply --server-side -k "github.com/kubeflow/training-operator/manifests/overlays/standalone?ref=v1.5.0"
 ```
 
 ### TensorFlow Release Only

diff --git a/scripts/gha/setup-training-operator.sh b/scripts/gha/setup-training-operator.sh
@@ -32,7 +32,7 @@ cd manifests/overlays/standalone
 kustomize edit set image kubeflow/training-operator=${TRAINING_CI_IMAGE}
 
 echo "Installing training operator manifests"
-kustomize build . | kubectl apply -f -
+kustomize build . | kubectl apply --server-side -f -
 
 TIMEOUT=30
 until kubectl get pods -n kubeflow | grep training-operator | grep 1/1 || [[ $TIMEOUT -eq 1 ]]; do