Kubeflow鏡像的快速下載（V0.3.3）

Kubeflow是一個面向Kubernetes集羣運行的機器學習框架。要想使用得先想辦法把鏡像搬到本身的環境裏來。

目前版本0.3.3的容器鏡像已經搬回來，可使用下面的腳原本從Aliyun的鏡像服務站下載：

• Kubeflow系統容器鏡像（0.3.3）：

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 ""

• TensorFlow機器學習引擎容器鏡像（1.12.0）：

## 添加Tag for registry.cn-hangzhou.aliyuncs.com/openthings
MY_REGISTRY=registry.cn-hangzhou.aliyuncs.com/openthings
MY_IMAGE_CPU=tensorflow-1.12.0-notebook-cpu:v-base-76107ff-897
MY_IMAGE_GPU=tensorflow-1.12.0-notebook-gpu:v-base-76107ff-897

## Push鏡像
## Tag to original docker iamges name.

echo ""
echo "1. tensorflow-1.12.0-notebook-cpu"
echo "PULL: ${MY_REGISTRY}/kubeflow-images-public-${MY_IMAGE_CPU}"
docker pull ${MY_REGISTRY}/kubeflow-images-public-${MY_IMAGE_CPU}
docker tag ${MY_REGISTRY}/kubeflow-images-public-${MY_IMAGE_CPU} gcr.io/kubeflow-images-public/${MY_IMAGE_CPU} 

echo ""
echo "2. tensorflow-1.12.0-notebook-gpu"
echo "PULL: ${MY_REGISTRY}/kubeflow-images-public-${MY_IMAGE_GPU}"
docker pull ${MY_REGISTRY}/kubeflow-images-public-${MY_IMAGE_GPU}
docker tag ${MY_REGISTRY}/kubeflow-images-public-${MY_IMAGE_GPU} gcr.io/kubeflow-images-public/${MY_IMAGE_GPU}

echo ""
echo "FINISHED."
echo ""

• 若是想本身學習當一回搬運工，請參考：
  • 源碼腳本，參見：https://github.com/openthings/kubernetes-tools/kubeflow
  • 完整過程，參見：KubeFlow等鏡像部署到本地集羣的流程

更多的參考：

• Kubeflow-機器學習工做流框架
• Kubeflow 使用指南
• Kubeflow 快速入門
• Kubeflow 入門——爲 Kubernetes 打造的組件化、可移植、可擴展的機器學習堆棧
• PyTorch支持Kubernetes集羣
• 爲JupyterHub自定義Notebook Images
• 基於Kubernetes的機器學習系統
• Kubernetes集成TensorFlow服務
• Spark機器學習工具鏈-MLflow簡介