Kubeflow镜像的快速下载（V0.3.3）

Kubeflow是一个面向Kubernetes集群运行的机器学习框架。要想使用得先想办法把镜像搬到自己的环境里来。

目前版本0.3.3的容器镜像已经搬回来，可以使用下面的脚本来从Aliyun的镜像服务站下载：

Kubeflow系统容器镜像（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机器学习引擎容器镜像（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镜像
## 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 ""

如果想自己学习当一回搬运工，请参考：
- 源码脚本，参见：https://github.com/openthings/kubernetes-tools/kubeflow
- 完整过程，参见：KubeFlow等镜像部署到本地集群的流程

更多的参考：

来源：oschina

链接：https://my.oschina.net/u/2306127/blog/2962269

标签

kubeflow

Kubernetes

MLflow

JupyterHub