SSL：没有替代证书使用者名称与目标主机名$ name.storage.googleapis.com匹配 [英] SSL: no alternative certificate subject name matches target host name $name.storage.googleapis.com

问题描述

我想在google cloud ml中运行tensorflow培训脚本。来自外部项目的一个桶。我已经创建了云ml引擎服务帐户，并将其作为用户添加到此外部项目中。

之后，在gcloud初始化项目中执行了以下命令：

  gcloud auth activate-service-account --my-service-acc-key.json 
  

<然后提交我的工作：

  gcloud ml-engine作业提交培训..参数
  

作业已成功提交并正在运行，直到从 file_io.FileIO（'gs：/ / external-bucket'）

我得到以下错误 SSL：没有替代证书主题名称匹配目标主机$ {bucket-name} .storage.googleapis.com 改为。

看起来像凭证有问题，但找不到任何有用的内容文档。

有什么可能是问题？

解决方案

像 bucketname.domainname.com 这样的存储桶不正确目前支持TensorFlow内的GCS客户端库。

今天在TensorFlow Google知识库中解决了此问题。

此修补程序应在现在2-3天内在 TensorFlow github 上提供，之后您应该能够从头部构建TensorFlow或者采用夜间Linux构建并将其作为在向Cloud ML Engine提交培训作业时提供package_uris之一。另外，您也可以等到Cloud ML Engine支持的下一个官方TensorFlow版本发布后才能使用它。

I want to run tensorflow traning script in google cloud ml. One of the buckets from an external project. I have created cloud ml engine service account and add it as an user to this external project.

After that, have executed the following command in my terminal with gcloud initialised project:

gcloud auth activate-service-account --my-service-acc-key.json

And then submit my job as:

gcloud ml-engine jobs submit training ..arguments

Job was submitted successfully and was running until accessing resources from external bucket with file_io.FileIO('gs://external-bucket')

I got the following error SSL: no alternative certificate subject name matches target host ${bucket-name}.storage.googleapis.com instead.

Looks like something wrong with credentials, but can't find anything useful in documentation.

What could be a problem?

解决方案

Unfortunately domain buckets like bucketname.domainname.com are not properly supported by the GCS client library inside TensorFlow at the moment.

This problem was fixed in the TensorFlow Google repository today.

The fix should become available on TensorFlow github within 2-3 days from now, after which you should be able to either build TensorFlow from head or take a nightly Linux build and provide it as one of the package_uris when submitting a training job to Cloud ML Engine.

Alternatively you can wait until it gets picked up by the next official TensorFlow release supported by Cloud ML Engine.

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