由于Tensorflow问题，无法在本地运行Google ML引擎 [英] Cannot run Google ML engine locally due to Tensorflow issues

问题描述

我试图通过运行命令 gcloud ml-engine local predict --model-dir = fasttext_cloud / --json-instances = debug_instance来在本地运行Google Cloud ML引擎以进行调试。 JSON 。但是，我收到错误：错误：（gcloud.ml-engine.local.predict）无法导入Tensorflow。

这很奇怪，因为Tensorflow在我的机器上正常工作。即使是一个简单的例子，像 python -c'import tensorflow'也没有任何问题。

解决方案 TensorFlow是否安装在虚拟环境中或非标准位置，当它从gcloud运行时不在Python路径中？

它有点笨拙但我会做以下检查gcloud正在使用的Python路径。修改文件

  $ {GCLOUD_INSTALL_LOCATION} / google-cloud-sdk / lib / surface / ml_engine / __ init__.py 
  

在文件顶部添加

  import sys 
 print（\\\
.join（sys.path））
  

然后运行

  gcloud ml-engine 
  

这应该打印python路径，现在可以检查它是否包含TensorFlow的安装位置。

I'm trying to run the Google Cloud ML engine locally for debugging purposes by running the command gcloud ml-engine local predict --model-dir=fasttext_cloud/ --json-instances=debug_instance.json. However, I'm getting the error: ERROR: (gcloud.ml-engine.local.predict) Cannot import Tensorflow.

This is strange as Tensorflow works fine on my machine. Even a simple example like python -c 'import tensorflow' has no issues whatsoever.

解决方案

Is TensorFlow installed in a virtual environment or a non-standard location that isn't on the Python path when running from gcloud?

Its a bit kludgy but I would do the following to check the Python path being used by gcloud. Modify the file

${GCLOUD_INSTALL_LOCATION}/google-cloud-sdk/lib/surface/ml_engine/__init__.py

At the top of the file add

import sys
print("\n".join(sys.path))

Then run

gcloud ml-engine

This should print out the python path and you can now check that it includes the location where TensorFlow is installed.

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