没有Google Cloud Storage的Kubeflow

问题描述

是否有可能用替代的本地解决方案代替Google Cloud Storage存储桶的使用，从而可以运行例如Kubeflow管道完全独立于Google Cloud Platform吗?

解决方案

是可以的.您可以使用 minio ，就像s3/gs一样，但是它在本地存储的持久卷上运行. /p>

以下是有关如何将其用作kfserving推理存储的说明:

验证minio是否在您的kubeflow安装中运行:

$ kubectl get svc -n kubeflow |grep minio
minio-service                                  ClusterIP   10.101.143.255   <none>        9000/TCP            81d

为迷你模型启用隧道:

$ kubectl port-forward svc/minio-service -n kubeflow 9000:9000
Forwarding from 127.0.0.1:9000 -> 9000
Forwarding from [::1]:9000 -> 9000

浏览 http://localhost:9000 进入minio UI并创建存储桶/上载模型.凭据minio/minio123.或者，您可以使用mc命令从终端执行此操作:

$ mc ls minio/models/flowers/0001/
[2020-03-26 13:16:57 CET]  1.7MiB saved_model.pb
[2020-04-25 13:37:09 CEST]      0B variables/

创建用于minio访问的密码和服务帐户，请注意s3端点定义了minio的路径，keyid& eseskey是base64中编码的凭据:

$ kubectl get secret mysecret -n homelab -o yaml
apiVersion: v1
data:
  awsAccessKeyID: bWluaW8=
  awsSecretAccessKey: bWluaW8xMjM=
kind: Secret
metadata:
  annotations:
    serving.kubeflow.org/s3-endpoint: minio-service.kubeflow:9000
    serving.kubeflow.org/s3-usehttps: "0"
  name: mysecret
  namespace: homelab

$ kubectl get serviceAccount -n homelab sa -o yaml
apiVersion: v1
kind: ServiceAccount
metadata:
  name: sa
  namespace: homelab
secrets:
- name: mysecret

最后，如下创建您的inferenceservice:

$ kubectl get inferenceservice tensorflow-flowers -n homelab -o yaml
apiVersion: serving.kubeflow.org/v1alpha2
kind: InferenceService
metadata:
  name: tensorflow-flowers
  namespace: homelab
spec:
  default:
    predictor:
      serviceAccountName: sa
      tensorflow:
        storageUri: s3://models/flowers

Is it possible to replace the usage of Google Cloud Storage buckets with an alternative on-premises solution so that it is possible to run e.g. Kubeflow Pipelines completely independent from the Google Cloud Platform?

解决方案

Yes it is possible. You can use minio, it's like s3/gs but it runs on a persistent volume of your on-premises storage.

Here are the instructions on how to use it as a kfserving inference storage:

Validate that minio is running in your kubeflow installation:

$ kubectl get svc -n kubeflow |grep minio
minio-service                                  ClusterIP   10.101.143.255   <none>        9000/TCP            81d

Enable a tunnel for your minio:

$ kubectl port-forward svc/minio-service -n kubeflow 9000:9000
Forwarding from 127.0.0.1:9000 -> 9000
Forwarding from [::1]:9000 -> 9000

Browse http://localhost:9000 to get to the minio UI and create a bucket/upload your model. Credentials minio/minio123. Alternatively you can use the mc command to do it from your terminal:

$ mc ls minio/models/flowers/0001/
[2020-03-26 13:16:57 CET]  1.7MiB saved_model.pb
[2020-04-25 13:37:09 CEST]      0B variables/

Create a secret&serviceaccount for the minio access, note that the s3-endpoint defines the path to the minio, keyid&acceskey are the credentials encoded in base64:

$ kubectl get secret mysecret -n homelab -o yaml
apiVersion: v1
data:
  awsAccessKeyID: bWluaW8=
  awsSecretAccessKey: bWluaW8xMjM=
kind: Secret
metadata:
  annotations:
    serving.kubeflow.org/s3-endpoint: minio-service.kubeflow:9000
    serving.kubeflow.org/s3-usehttps: "0"
  name: mysecret
  namespace: homelab

$ kubectl get serviceAccount -n homelab sa -o yaml
apiVersion: v1
kind: ServiceAccount
metadata:
  name: sa
  namespace: homelab
secrets:
- name: mysecret

Finally, create your inferenceservice as follows:

$ kubectl get inferenceservice tensorflow-flowers -n homelab -o yaml
apiVersion: serving.kubeflow.org/v1alpha2
kind: InferenceService
metadata:
  name: tensorflow-flowers
  namespace: homelab
spec:
  default:
    predictor:
      serviceAccountName: sa
      tensorflow:
        storageUri: s3://models/flowers

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