在Google Cloud Machine Learning中读取档案 [英] reading files in google cloud machine learning

问题描述

我尝试使用gcloud ml-engine jobs submit training在Google云ml-engine上运行 tensorflow-wavenet 但是当尝试读取json配置文件时，云作业崩溃了:

I tried to run tensorflow-wavenet on the google cloud ml-engine with gcloud ml-engine jobs submit training but the cloud job crashed when it was trying to read the json configuration file:

with open(args.wavenet_params, 'r') as f:
   wavenet_params = json.load(f)

arg.wavenet_params只是我上载到google cloud storage bucket的json文件的文件路径.文件路径如下所示:gs://BUCKET_NAME/FILE_PATH.json.

arg.wavenet_params is simply a file path to a json file which I uploaded to the google cloud storage bucket. The file path looks like this: gs://BUCKET_NAME/FILE_PATH.json.

我仔细检查了文件路径是否正确，并且由于我注释掉了其他所有内容，因此我确定这部分是导致崩溃的原因.

I double-checked that the file path is correct and I'm sure that this part is responsible for the crash since I commented out everything else.

崩溃日志文件没有提供有关已发生事件的太多信息:

The crash log file doesn't give much information about what has happened:

Module raised an exception for failing to call a subprocess Command '['python', '-m', u'gcwavenet.train', u'--data_dir', u'gs://wavenet-test-data/VCTK-Corpus-Small/', u'--logdir_root', u'gs://wavenet-test-data//gcwavenet10/logs']' returned non-zero exit status 1.

我用f.close()替换了wavenet_params = json.load(f)，但仍然得到相同的结果.

I replaced wavenet_params = json.load(f) by f.close() and I still get the same result.

当我使用gcloud ml-engine local train在本地运行时，一切正常.

Everything works when I run it locally with gcloud ml-engine local train.

我认为问题通常是使用gcloud ml-engine读取文件，或者我无法使用gs://BUCKET_NAME/FILE_PATH从python文件中访问Google云存储桶.

I think the problem is with reading files with gcloud ml-engine in general or that I can't access the google cloud bucket from within a python file with gs://BUCKET_NAME/FILE_PATH.

推荐答案

Python的open函数无法从GCS读取文件.您将需要使用能够执行此操作的库. TensorFlow包括一个这样的库:

Python's open function cannot read files from GCS. You will need to use a library capable of doing so. TensorFlow includes one such library:

import tensorflow as tf
from tensorflow.python.lib.io import file_io

with file_io.FileIO(args.wavenet_params, 'r') as f:
  wavenet_params = json.load(f)

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