fix(README): Update instructions to only use kustomize (#1455)

kubeflow · Feb 13, 2024 · a9d7df9 · a9d7df9
1 parent b77e6f3
commit a9d7df9
Showing 1 changed file with 5 additions and 10 deletions.
diff --git a/guides/kfp_tekton_install.md b/guides/kfp_tekton_install.md
@@ -73,17 +73,12 @@ To install the standalone Kubeflow Pipelines V1 with Tekton , run the following
          -p '{"data":{"default-timeout-minutes": "0"}}'
    ```
 
-3. Install Kubeflow Pipelines with Tekton backend (`kfp-tekton`) `v1.9.2` [custom resource definitions](https://kubernetes.io/docs/concepts/extend-kubernetes/api-extension/custom-resources/)(CRDs).
+3. Install Kubeflow Pipelines with Tekton backend (`kfp-tekton`) `v1.9.2` deployment
     ```shell
-    kubectl apply --selector kubeflow/crd-install=true -f https://raw.githubusercontent.com/kubeflow/kfp-tekton/master/install/v1.9.2/kfp-tekton.yaml
+    kubectl apply -k https://github.com/kubeflow/kfp-tekton//manifests/kustomize/env/kfp-template\?ref\=v1.9.2
     ```
 
-4. Install Kubeflow Pipelines with Tekton backend (`kfp-tekton`) `v1.9.2` deployment
-    ```shell
-    kubectl apply -f https://raw.githubusercontent.com/kubeflow/kfp-tekton/master/install/v1.9.2/kfp-tekton.yaml
-    ```
-
-5. Then, if you want to expose the Kubeflow Pipelines endpoint outside the cluster, run the following commands:
+4. Then, if you want to expose the Kubeflow Pipelines endpoint outside the cluster, run the following commands:
     ```shell
     kubectl patch svc ml-pipeline-ui -n kubeflow -p '{"spec": {"type": "LoadBalancer"}}'
     ```
@@ -93,13 +88,13 @@ To install the standalone Kubeflow Pipelines V1 with Tekton , run the following
     kubectl get svc ml-pipeline-ui -n kubeflow -o jsonpath='{.status.loadBalancer.ingress[0].ip}'
     ```
 
-6. (GPU worker nodes only) If your Kubernetes cluster has a mixture of CPU and GPU worker nodes, it's recommended to disable the Tekton default affinity assistant so that Tekton won't schedule too many CPU workloads on the GPU nodes.
+5. (GPU worker nodes only) If your Kubernetes cluster has a mixture of CPU and GPU worker nodes, it's recommended to disable the Tekton default affinity assistant so that Tekton won't schedule too many CPU workloads on the GPU nodes.
     ```shell
     kubectl patch cm feature-flags -n tekton-pipelines \
       -p '{"data":{"disable-affinity-assistant": "true"}}'
     ```
 
-7. (OpenShift only) If you are running the standalone KFP-Tekton on OpenShift, apply the necessary security context constraint below
+6. (OpenShift only) If you are running the standalone KFP-Tekton on OpenShift, apply the necessary security context constraint below
    ```shell
    curl -L https://raw.githubusercontent.com/kubeflow/kfp-tekton/master/install/v1.9.2/kfp-tekton.yaml | yq 'del(.spec.template.spec.containers[].securityContext.runAsUser, .spec.template.spec.containers[].securityContext.runAsGroup)' | oc apply -f -
    oc apply -k https://github.com/kubeflow/kfp-tekton//manifests/kustomize/third-party/openshift/standalone