Api 谷歌认证gcloud.beta.ml.jobs.submit.training

我正试图在google ml云中提交作业

  gcloud beta ml jobs submit training readlips_resnet1 \
  --package-path=trainer \
  --module-name=trainer.run \
  --staging-bucket=gs://xxxxbucket/ \
  --region=us-central1 \
  --scale-tier=BASIC_GPU \
  -- \
  --input_path gs://xxxxbucket/readlips/m1/readlips-test-1-{}.tfrecords \
  --input_path_test gs://xxxxbucket/readlips/m1/readlips-test-1-6.tfrecords \
  --board_path gs://my-first-bucket-mosnoi/readlips/m1/TFboard3_readlips_resnet \
  --model_dir gs://xxxxbucket/readlips/m1/models3 \
  --filenameNr 2 \
  --save_step 1000 \
  --display_step 100 \
  --max_steps 2000 \
  --batch_size 20 \
  --learning_rate 0.001 \
  --keep_prob 0.8 \
  --layers 3 \
  --hidden 150 \
  --rnn_cell LSTM \
  --optimizer ADAM \
  --initializer  graves \
  --bias -0.1 \
  --gpu

我得到了下一个错误

 Job [readlips_resnet1] submitted successfully.
 INFO    2017-02-28 12:14:48 +0200       unknown_task            Validating job requirements...
 INFO    2017-02-28 12:14:48 +0200       unknown_task            Job creation request has been successfully validated.
 INFO    2017-02-28 12:14:49 +0200       unknown_task            Job readlips_resnet1 is queued.
 ERROR: (gcloud.beta.ml.jobs.submit.training) UNAUTHENTICATED: Request
 had invalid authentication credentials. Expected OAuth 2 access token,
login cookie or other v alid authentication credential. See
 https://developers.google.com/identity/sign-in/web/devconsole-project.

我不知道如何设置这2个访问令牌，我已经查看了文档，尝试了gcloud beta init--account=和gcloud beta auth应用程序默认登录--client id file=。

---

我已经创建了凭据、api密钥、OAuth 2.0客户端ID和服务帐户密钥，但我不知道将其放在哪里才能运行我的作业。

确保为项目启用云api，创建服务帐户，并下载一个作为JSON的私钥。在这种情况下，服务帐户是最重要的，因为错误似乎指向无效的凭据

您可以阅读“”文档的更多详细信息

运行以下命令

gcloud iam service-accounts list

它应该向您显示与GCP关联的服务帐户列表。 使用以下代码验证服务帐户：

from googleapiclient import discovery
from googleapiclient import http
from oauth2client.client import GoogleCredentials

credentials = GoogleCredentials.get_application_default()
my_project_id = 'my_current_project_id' # change according to your 
project id
projects = 'projects/' + my_project_id
ml_client = discovery.build(
    'ml',
    'v1',        
    requestBuilder=http.HttpRequest,
    credentials=credentials)
projs = ml_client.projects()
response = projs.getConfig(name = projects).execute()
SERVICE_ACCOUNT = response.get('serviceAccount')
print('Your Service Acc:', SERVICE_ACCOUNT)