From a9d7df96d266a5ca04b1a2fa894957ff8ddff82c Mon Sep 17 00:00:00 2001 From: Tommy Li Date: Mon, 12 Feb 2024 16:07:04 -0800 Subject: [PATCH] fix(README): Update instructions to only use kustomize (#1455) --- guides/kfp_tekton_install.md | 15 +++++---------- 1 file changed, 5 insertions(+), 10 deletions(-) diff --git a/guides/kfp_tekton_install.md b/guides/kfp_tekton_install.md index 0eba7c004b..d9934aea65 100644 --- 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