-
windows terminal을 열어 ubuntu tab을 생성한다.
-
다음 명령어를 입력해 kustomize 를 다운 받는다.
wget https://github.com/kubernetes-sigs/kustomize/releases/download/v3.2.0/kustomize_3.2.0_linux_amd64
- 우분투 상에서 다음 명령어를 수행하여 kustomize를 설치한다.
sudo mv kustomize_3.2.0_linux_amd64 /usr/local/bin/kustomize
sudo mv kustomize /usr/local/bin/
kustomize version
git clone https://github.com/CUKykkim/manifests.git
cd manifests
kustomize명령어를 입력하여 모든 yaml파일을 k8s상에서 수행
while ! kustomize build example | kubectl apply -f -; do echo "Retrying to apply resources"; sleep 10; done
- 설치가 다 끝났다면, 다음 명령어를 사용하여 k8s 시스템상에 pod의 상태를 확인한다.
kubectl get po -A
- 설치가 완료되면 아래와 같은 결과가 나온다.
NAMESPACE NAME READY STATUS RESTARTS AGE
auth dex-5ddf47d88d-82l5c 1/1 Running 1 75m
cert-manager cert-manager-7dd5854bb4-bm2tq 1/1 Running 0 75m
cert-manager cert-manager-cainjector-64c949654c-wx66q 1/1 Running 3 75m
cert-manager cert-manager-webhook-6b57b9b886-cgvt9 1/1 Running 0 75m
istio-system authservice-0 1/1 Running 0 75m
istio-system cluster-local-gateway-75cb7c6c88-9pwrw 1/1 Running 0 75m
istio-system istio-ingressgateway-79b665c95-pknck 1/1 Running 0 75m
istio-system istiod-86457659bb-29sb2 1/1 Running 0 75m
knative-eventing eventing-controller-79895f9c56-h9gsl 1/1 Running 0 75m
knative-eventing eventing-webhook-78f897666-d5c5j 1/1 Running 0 75m
knative-eventing imc-controller-688df5bdb4-4bpdf 1/1 Running 0 75m
knative-eventing imc-dispatcher-646978d797-6cxlv 1/1 Running 0 75m
knative-eventing mt-broker-controller-67c977497-jhzsc 1/1 Running 0 75m
knative-eventing mt-broker-filter-66d4d77c8b-qs6ct 1/1 Running 0 75m
knative-eventing mt-broker-ingress-5c8dc4b5d7-qt26s 1/1 Running 0 75m
knative-serving activator-7476cc56d4-ngn67 2/2 Running 1 74m
knative-serving autoscaler-5c648f7465-5mzf7 2/2 Running 1 74m
knative-serving controller-57c545cbfb-tqwlv 2/2 Running 1 74m
knative-serving istio-webhook-578b6b7654-kpmml 2/2 Running 1 74m
knative-serving networking-istio-6b88f745c-ptq22 2/2 Running 1 74m
knative-serving webhook-6fffdc4d78-2x5fh 2/2 Running 1 74m
kube-system coredns-558bd4d5db-57xg2 1/1 Running 0 77m
kube-system coredns-558bd4d5db-mctm6 1/1 Running 0 77m
kube-system etcd-docker-desktop 1/1 Running 0 77m
kube-system kube-apiserver-docker-desktop 1/1 Running 0 77m
kube-system kube-controller-manager-docker-desktop 1/1 Running 0 77m
kube-system kube-proxy-b9hkn 1/1 Running 0 77m
kube-system kube-scheduler-docker-desktop 1/1 Running 2 77m
kube-system storage-provisioner 1/1 Running 3 76m
kube-system vpnkit-controller 1/1 Running 3 76m
kubeflow-user-example-com hellokubeflow-2-r5dd4-433021892 0/2 Completed 0 30m
kubeflow-user-example-com ml-pipeline-ui-artifact-5dd95d555b-ht5ww 2/2 Running 0 68m
kubeflow-user-example-com ml-pipeline-visualizationserver-6b44c6759f-vccd5 2/2 Running 0 68m
kubeflow admission-webhook-deployment-7c6d8876db-w7f48 1/1 Running 0 74m
kubeflow cache-deployer-deployment-79fdf9c5c9-6hjh7 2/2 Running 1 74m
kubeflow cache-server-5bdf4f4457-6skz9 2/2 Running 0 74m
kubeflow centraldashboard-6f56b874f9-p9mlm 1/1 Running 0 74m
kubeflow jupyter-web-app-deployment-7d4bfc79b-7cjtg 1/1 Running 0 74m
kubeflow katib-controller-557f4f848-ph5f9 1/1 Running 0 74m
kubeflow katib-db-manager-5cb7c77df8-p4h6d 1/1 Running 0 74m
kubeflow katib-mysql-7894994f88-dpt2g 1/1 Running 0 74m
kubeflow katib-ui-8cc499fcf-tfsvm 1/1 Running 0 74m
kubeflow kfserving-controller-manager-0 2/2 Running 0 73m
kubeflow kfserving-models-web-app-db6695f6-pjg4t 2/2 Running 0 74m
kubeflow kubeflow-pipelines-profile-controller-7b947f4748-dgkkh 1/1 Running 0 74m
kubeflow metacontroller-0 1/1 Running 0 73m
kubeflow metadata-envoy-deployment-5b4856dd5-gdchz 1/1 Running 0 74m
kubeflow metadata-grpc-deployment-6b5685488-c72pq 2/2 Running 3 74m
kubeflow metadata-writer-548bd879bb-flwcn 2/2 Running 1 74m
kubeflow minio-5b65df66c9-4h77p 2/2 Running 0 74m
kubeflow ml-pipeline-8c4b99589-h6vrn 2/2 Running 1 74m
kubeflow ml-pipeline-persistenceagent-d6bdc77bd-zq2gn 2/2 Running 0 74m
kubeflow ml-pipeline-scheduledworkflow-5db54d75c5-272tw 2/2 Running 0 74m
kubeflow ml-pipeline-ui-5bd8d6dc84-5mnxg 2/2 Running 0 74m
kubeflow ml-pipeline-viewer-crd-68fb5f4d58-fgljw 2/2 Running 1 74m
kubeflow ml-pipeline-visualizationserver-8476b5c645-7w64q 2/2 Running 0 74m
kubeflow mpi-operator-d5bfb8489-bvz6s 0/1 CrashLoopBackOff 17 74m
kubeflow mysql-f7b9b7dd4-h9trv 2/2 Running 0 74m
kubeflow notebook-controller-deployment-8b64878d4-dqw7w 1/1 Running 0 74m
kubeflow profiles-deployment-64675b488b-g5k9k 2/2 Running 0 74m
kubeflow tensorboard-controller-controller-manager-9ff658cf8-q2szk 3/3 Running 4 74m
kubeflow tensorboards-web-app-deployment-754f55996d-z76cq 1/1 Running 0 74m
kubeflow training-operator-868876bcc8-vtglh 1/1 Running 0 74m
kubeflow volumes-web-app-deployment-6dcbf595b5-74b8n 1/1 Running 0 74m
kubeflow workflow-controller-5cbbb49bd8-cgkth 2/2 Running 3 74m
다음 둘 중 하나의 방식을 사용한다.
- port forward 방식
kubectl port-forward svc/istio-ingressgateway -n istio-system 8080:80
- End Point를 외부로 노출하는 방식
kubectl edit svc istio-ingressgateway -n istio-system
초기에 비밀번호는 다음과 같다.
[email protected]
12341234
- hello pipeline
import kfp
import kfp.dsl as dsl
@dsl.pipeline(
name='HelloKubeflow',
description='Print HelloWorld'
)
def hellokubelfow_pipeline():
hello = dsl.ContainerOp(
name='HelloKubeflow',
image='alpine',
command=['sh', '-c'],
arguments=['echo "hello Kubeflow"']
)
if __name__ == '__main__':
kfp.compiler.Compiler().compile(hellokubelfow_pipeline, 'containerop.pipeline.tar.gz')
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