使用Google Cloud Registry时避免错误429(超出配额) [英] Avoiding Error 429 (quota exceeded) while working with Google Cloud Registry

问题描述

我在Google Container Registry中遇到429错误，同时拉出了太多图像

I'm hitting 429 error in Google Container Registry, too many images are pulled simultaneously

Error: Status 429 trying to pull repository [...] "Quota Exceeded."

有一个Kubernetes集群，其中包含多个节点，并且Pod实现了Kubeflow步骤.在Google 指南中，他们建议以下内容:

There is a Kubernetes cluster with multiple nodes and pods implement Kubeflow steps. In the Google guide they suggest the following:

To avoid hitting the fixed quota limit, you can:
- Increase the number of IP addresses talking to Container Registry. 
  Quotas are per IP address.
- Add retries that introduce a delay. 
  For example, you could use exponential backoff.

问题:

• 如何增加与Container Registry对话的IP地址数量?
• 如何以及在何处添加重试会导致延迟? Pod在Kubeflow管道中作为步骤运行.

推荐答案

对于Cloud Registry配额限制，似乎没有任何办法，因为这些配额是固定的.根据容器注册表>文档>资源>配额和限制:

It seems nothing can be done in terms of the Cloud Registry quota limits because those are fixed. According to Container Registry > Doc > Resources > Quotas and limits:

任何发送到Container Registry的请求都具有2小时的超时限制.

Any request sent to Container Registry has a 2 hour timeout limit.

每个客户端IP地址的固定速率限制为:

The fixed rate limits per client IP address are:

每10分钟
• 50,000个HTTP请求
每天
• 每天1,000,000个HTTP请求

• 50,000 HTTP requests every 10 minutes
• 1,000,000 HTTP requests per day

Google提供了对GKE的支持，但是Google不支持Kubeflow本身.这个问题应该向Kubeflow支持人员解决.

Google provides support for GKE, but Kubeflow itself is not supported by Google. This question should be addressed to the Kubeflow support.

可以在GitHub的项目页面上注册一个Kubeflow问题，该问题违反配额限制，以及有关如何使容器拉取以使用更多IP地址的问题: https://github.com/kubeflow/kubeflow/issues

A Kubeflow issue with breaching quota limits and a question about how to get container pulls to use more IP addresses can be registered on the project page on GitHub: https://github.com/kubeflow/kubeflow/issues

此处提供其他支持选项: https://www.kubeflow.org/docs/other-guides/support/

Other support options are available here: https://www.kubeflow.org/docs/other-guides/support/

如果使用CLI，则可以尝试在部署之前自定义Kubeflow配置文件，或将其拆分为单独的部署以克服Cloud Registry配额限制.这种方法有助于进行一些复杂的部署.这里重要的是要注意依赖项.首次运行

If you use CLI, you may try to customize Kubeflow configuration file before deployment or split it into separate deployments to overcome Cloud Registry quota limits. This approach helps for some complicated deployments. An important thing here is to take care of dependencies. First run

kfctl build -v -f ${CONFIG_URI}

在文件${KF_DIR}/kfctl_gcp_iap.yaml中进行更改，然后运行

make changes in the file ${KF_DIR}/kfctl_gcp_iap.yaml, and then run

kfctl apply -v -f ${CONFIG_URI}

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