Kubeflow等映象部署到叢集多節點
阿新 • • 發佈:2018-11-29
為了將Kubeflow(https://github.com/kubeflow/kubeflow)/Kubernetes等映象放到本地叢集部署或者更新,需要一系列的操作。如果叢集的多個節點同時訪問外部映象服務,將帶來較大的併發網路流量,不僅速度慢、而且費用增加。因此我將其分為兩個階段來進行,第一階段將映象下載到本地,第二階段各個節點從本地檔案系統或映象服務來獲取映象的拷貝。
1、從 gcr到本地儲存
這一階段其實也分為兩個步驟。
首先,從能夠訪問到gcr的站點(https://www.katacoda.com)下載。如:
echo "" echo "=================================================================" echo "pull kubeflow images for system from gcr.io and hub.docker.com..." echo "This tools created by openthings, NO WARANTY. 2018.07.10." echo "=================================================================" echo "" echo "1. centraldashboard" docker pull gcr.io/kubeflow-images-public/centraldashboard:v0.2.1 echo "" echo "2. jupyterhub-k8s" docker pull gcr.io/kubeflow/jupyterhub-k8s:v20180531-3bb991b1 echo "" echo "3. tf_operator" docker pull gcr.io/kubeflow-images-public/tf_operator:v0.2.0 echo "" echo "4. ambassador" docker pull quay.io/datawire/ambassador:0.30.1 echo "" echo "5. redis" docker pull redis:4.0.1 echo "" echo "6. seldonio/cluster-manager" docker pull seldonio/cluster-manager:0.1.6 echo "" echo "Finished." echo ""
然後將映象推送到國內站點(如阿里雲-http://registry.cn-hangzhou.aliyuncs.com)。如
echo "" echo "=================================================================" echo "Push kubeflow images for system to aliyun.com ..." echo "This tools created by openthings, NO WARANTY. 2018.07.10." echo "=================================================================" MY_REGISTRY=registry.cn-hangzhou.aliyuncs.com/openthings echo "" echo "1. centraldashboard" docker tag gcr.io/kubeflow-images-public/centraldashboard:v0.2.1 ${MY_REGISTRY}/kubeflow-images-public-centraldashboard:v0.2.1 docker push ${MY_REGISTRY}/kubeflow-images-public-centraldashboard:v0.2.1 echo "" echo "2. jupyterhub-k8s" docker tag gcr.io/kubeflow/jupyterhub-k8s:v20180531-3bb991b1 ${MY_REGISTRY}/kubeflow-jupyterhub-k8s:v20180531-3bb991b1 docker push ${MY_REGISTRY}/kubeflow-jupyterhub-k8s:v20180531-3bb991b1 echo "" echo "3. tf_operator" docker tag gcr.io/kubeflow-images-public/tf_operator:v0.2.0 ${MY_REGISTRY}/kubeflow-images-public-tf_operator:v0.2.0 docker push ${MY_REGISTRY}/kubeflow-images-public-tf_operator:v0.2.0 echo "" echo "4. ambassador" docker tag quay.io/datawire/ambassador:0.30.1 ${MY_REGISTRY}/quay-io-datawire-ambassador:0.30.1 docker push ${MY_REGISTRY}/quay-io-datawire-ambassador:0.30.1 echo "" echo "5. redis" docker tag redis:4.0.1 ${MY_REGISTRY}/redis:4.0.1 docker push ${MY_REGISTRY}/redis:4.0.1 echo "" echo "6. seldonio/cluster-manager" docker tag seldonio/cluster-manager:0.1.6 ${MY_REGISTRY}/seldonio-cluster-manager:0.1.6 docker push ${MY_REGISTRY}/seldonio-cluster-manager:0.1.6 echo "" echo "Finished." echo ""
然後,可以從阿里雲下載到本地,恢復為原始的名稱:
echo "" echo "=================================================================" echo "Pull kubeflow images for system from aliyun.com ..." echo "This tools created by openthings, NO WARANTY. 2018.11.28." echo "=================================================================" MY_REGISTRY=registry.cn-hangzhou.aliyuncs.com/openthings echo "" echo "1. centraldashboard" docker pull ${MY_REGISTRY}/kubeflow-images-public-centraldashboard:v0.2.1 docker tag ${MY_REGISTRY}/kubeflow-images-public-centraldashboard:v0.2.1 gcr.io/kubeflow-images-public/centraldashboard:v0.2.1 echo "" echo "2. jupyterhub-k8s" docker pull ${MY_REGISTRY}/kubeflow-jupyterhub-k8s:v20180531-3bb991b1 docker tag ${MY_REGISTRY}/kubeflow-jupyterhub-k8s:v20180531-3bb991b1 gcr.io/kubeflow/jupyterhub-k8s:v20180531-3bb991b1 echo "" echo "3. tf_operator" docker pull ${MY_REGISTRY}/kubeflow-images-public-tf_operator:v0.2.0 docker tag ${MY_REGISTRY}/kubeflow-images-public-tf_operator:v0.2.0 gcr.io/kubeflow-images-public/tf_operator:v0.2.0 echo "" echo "4. ambassador" docker pull ${MY_REGISTRY}/quay-io-datawire-ambassador:0.30.1 docker tag ${MY_REGISTRY}/quay-io-datawire-ambassador:0.30.1 quay.io/datawire/ambassador:0.30.1 echo "" echo "5. redis" docker pull ${MY_REGISTRY}/redis:4.0.1 docker tag ${MY_REGISTRY}/redis:4.0.1 redis:4.0.1 echo "" echo "6. seldonio/cluster-manager" docker pull ${MY_REGISTRY}/seldonio-cluster-manager:0.1.6 docker tag ${MY_REGISTRY}/seldonio-cluster-manager:0.1.6 seldonio/cluster-manager:0.1.6 echo "" echo "Finished." echo ""
從阿里雲下載到本地後,可以推送到本地映象服務(如Harbor)或者打包為*.tar檔案。
2、從本地儲存到叢集部署
從本地Harbor中安裝,使用docker tag將映象改名後,就可以使用了。可以參考上面的從阿里雲下載的方法。
打包為*.tar檔案,參見:
echo "==================================================================="
echo "Save Kubeflow images to tar."
echo "This tool created by https://my.oschina.net/u/2306127"
echo "Please visit https://github.com/openthings/kubernetes-tools"
echo "###################################################################"
echo "Kubeflow 0.3.3 ML system images."
echo "-------------------------------------------------------------------"
echo "A1.>> centraldashboard"
docker save gcr.io/kubeflow-images-public/centraldashboard:v0.2.1 -o A1-kubeflow-centraldashboard-v0.2.1.tar
echo ""
echo "A2.>> jupyterhub-k8s"
docker save gcr.io/kubeflow/jupyterhub-k8s:v20180531-3bb991b1 -o A2-kubeflow-jupyterhub-k8s-v20180531-3bb991b1.tar
echo ""
echo "A3.>> tf_operator"
docker save gcr.io/kubeflow-images-public/tf_operator:v0.2.0 -o A3-kubeflow-tf_operator-v0.2.0.tar
echo ""
echo "A4.>> ambassador"
docker save quay.io/datawire/ambassador:0.30.1 -o A4-kubeflow-ambassador-0.30.1.tar
echo ""
echo "A5.>> redis"
docker save redis:4.0.1 -o A5-kubeflow-redis-4.0.1.tar
echo ""
echo "A6.>> seldonio/cluster-manager"
docker save seldonio/cluster-manager:0.1.6 -o A6-kubeflow-seldonio-cluster-manager-0.1.6.tar
echo ""
echo "=================================================================="
echo "Kubeflow worker engine images......"
echo "B1.>> Tensorflow notebook CPU"
docker save gcr.io/kubeflow-images-public/tensorflow-1.12.0-notebook-cpu:v-base-76107ff-897 -o A6-kubeflow-tensorflow-1.12.0-notebook-cpu-v-base-76107ff-897.tar
echo ""
echo "B2.>> Tensorflow notebook GPU"
docker save gcr.io/kubeflow-images-public/tensorflow-1.12.0-notebook-gpu:v-base-76107ff-897 -o A6-kubeflow-tensorflow-1.12.0-notebook-gpu-v-base-76107ff-897.tar
echo ""
echo "==================================================================="
echo "Save Kubeflow images Finished."
echo "This tool created by https://my.oschina.net/u/2306127"
echo "Please visit https://github.com/openthings/kubernetes-tools"
echo "==================================================================="
echo ""
將所有的映象壓縮為一個zip包,然後上傳到工作節點:
echo "Uploading 10.1.1.202"
sshpass -p xxxx scp kf-images-0.3.3.zip [email protected]:/home/supermap/
echo "Uploading 10.1.1.203"
sshpass -p xxxx scp kf-images-0.3.3.zip [email protected]:/home/supermap/
echo "Uploading 10.1.1.142"
sshpass -p xxxx scp kf-images-0.3.3.zip [email protected]:/home/supermap/
echo "Uploading 10.1.1.193"
sshpass -p xxxx scp kf-images-0.3.3.zip [email protected]:/home/supermap/
echo "Uploading 10.1.1.234"
sshpass -p xxxx scp kf-images-0.3.3.zip [email protected]:/home/supermap/
echo "Uploading 10.1.1.205"
sshpass -p xxxx scp kf-images-0.3.3.zip [email protected]:/home/supermap/
echo "Uploading 10.1.1.112"
sshpass -p xxxx scp kf-images-0.3.3.zip [email protected]:/home/supermap/
echo "Upload kf-images-0.3.3.zip Finished."
然後,在各個節點上恢復到Docker的原始映象名稱。如下:
echo "==================================================================="
echo "Load Kubeflow images from tar."
echo "This tool created by https://my.oschina.net/u/2306127"
echo "Please visit https://github.com/openthings/kubernetes-tools"
echo "###################################################################"
echo "Kubernetes core system images."
echo "-------------------------------------------------------------------"
echo "A1<< centraldashboard"
sudo docker load -i A1-kubeflow-centraldashboard-v0.2.1.tar
echo ""
echo "A2<< jupyterhub-k8s"
sudo docker load -i A2-kubeflow-jupyterhub-k8s-v20180531-3bb991b1.tar
echo ""
echo "A3<< tf_operator"
sudo docker load -i A3-kubeflow-tf_operator-v0.2.0.tar
echo ""
echo "A4<< ambassador"
sudo docker load -i A4-kubeflow-ambassador-0.30.1.tar
echo ""
echo "A5<< redis"
sudo docker load -i A5-kubeflow-redis-4.0.1.tar
echo ""
echo "A6<< seldonio/cluster-manager"
sudo docker load -i A6-kubeflow-seldonio-cluster-manager-0.1.6.tar
echo ""
echo "=================================================================="
echo "Kubeflow worker engine images......"
echo "B1<< Tensorflow notebook CPU"
sudo docker load -i A6-kubeflow-tensorflow-1.12.0-notebook-cpu-v-base-76107ff-897.tar
echo ""
echo "B2<< Tensorflow notebook GPU"
sudo docker load -i A6-kubeflow-tensorflow-1.12.0-notebook-gpu-v-base-76107ff-897.tar
echo ""
echo "==================================================================="
echo "Load Kubeflow images Finished."
echo "This tool created by https://my.oschina.net/u/2306127"
echo "Please visit https://github.com/openthings/kubernetes-tools"
echo "==================================================================="
在每一個節點執行上面的指令碼,也可以使用 ansible來遠端批量執行。
ansible all -i hosts_ansible -m shell -a "unzip -u /home/supermap/kf-images-0.3.3.zip && cd /home/supermap/kf-images-0.3.3 && ./kf-images-load.sh" --ask-sudo-pass --become --become-method=sudo
上面的hosts_ansible為ansible的hosts列表檔案(請參考 Ansible快速開始-指揮叢集 )。
上面的這個過程也適用於Kubernetes本身映象的下載和更新。更多參考:
檢視映象是否有新的版本: