在没有gcloud的情况下在生产中使用CloudML预测API

Question

在生产服务中使用CloudML预测API的最佳方法是什么？

我见过:https: //cloud.google.com/ml/docs/quickstarts/prediction但它依赖于gcloud工具

我正在寻找的解决方案不依赖于安装gcloud并在发出请求的机器上进行初始化.拥有适用于GCP,AWS和其他云的解决方案会很棒.

谢谢

Answer 1

我将向您展示如何验证您的生产环境以使用CloudML在线预测.CloudML快速入门用于gcloud通过用户名,密码等对最终用户进行身份验证, gcloud但不能很好地扩展到100台机器启动和停止的环境.下面,我将引导您完成创建云服务帐户和生成私钥的步骤,以便您的生产实例能够向Google服务器标识自己.见身份验证文档在这里.

这是您可以使用的食谱.

PROJECT=
MODEL_NAME=
SERVICE_ACCOUNT_PREFIX=cloud-ml-predict
SERVICE_ACCOUNT="${SERVICE_ACCOUNT_PREFIX}@${PROJECT}.iam.gserviceaccount.com"

这些步骤只需执行一次,并为您创建服务帐户和私钥.

# Make a new service account
gcloud iam service-accounts create  ${SERVICE_ACCOUNT_PREFIX} \
  --display-name ${SERVICE_ACCOUNT_PREFIX}

# Provide correct role to service account permissions:
gcloud projects add-iam-policy-binding $PROJECT \
  --member "serviceAccount:$SERVICE_ACCOUNT" --role roles/viewer

# Create private key for the service account:
gcloud iam service-accounts keys create --iam-account \
  $SERVICE_ACCOUNT private_key.json

现在我们有了一个私钥(in private_key.json),我们可以从任何具有googleapiclientPython库的机器调用预测API .现在,无论是否有任何机器,gcloud您只需要包含以下行,即可通过HTTP访问CloudML预测服务

scopes = ['https://www.googleapis.com/auth/cloud-platform']
credentials = ServiceAccountCredentials.from_json_keyfile_name(key_filename, scopes=scopes)
ml_service = discovery.build('ml', 'v1beta1', credentials=credentials)

最后,这是一个有用的例子,假设你有一个从快速入门部署的MNIST模型.

cat > key_pair_cloud_ml_serve.py <<EOD
from googleapiclient import discovery
import json
from oauth2client.service_account import ServiceAccountCredentials
import sys

def get_mnist_prediction(ml_service, project, model_name, instance):
  parent = 'projects/{}/models/{}'.format(project, model_name)
  request_dict = {'instances': [json.loads(instance)]}

  request = ml_service.projects().predict(name=parent, body=request_dict)
  print request.execute()  # waits till request is returned

if __name__ == '__main__':
  usage_str = 'usage: python prog private_key.json MODEL_NAME data/predict*json'
  assert len(sys.argv) == 4, usage_str

  key_file = sys.argv[1]
  model_name = sys.argv[2]
  data_file = sys.argv[3]

  scopes = ['https://www.googleapis.com/auth/cloud-platform']
  credentials = ServiceAccountCredentials.from_json_keyfile_name(key_file,
scopes=scopes)
  ml_service = discovery.build('ml', 'v1beta1', credentials=credentials)
  with open(key_file) as ff:
    project = json.load(ff)['project_id']


  with open(data_file) as ff:
    for ii, instance in enumerate(ff):
      get_mnist_prediction(ml_service, project, model_name, instance)
EOD

从Cloud ML示例的mnist/deployable文件夹中我们称之为代码...

python key_pair_cloud_ml_serve.py private_key.json \
  $MODEL_NAME data/predict_sample.tensor.json


{u'predictions': [{u'prediction': 5, u'key': 0, u'scores': [0.04025577753782272, 0.00042669562390074134, 0.005919951014220715, 0.4221051335334778, 2.2986243493505754e-05, 0.5084351897239685, 0.0007824163185432553, 0.01125132292509079, 0.008616944774985313, 0.0021835025399923325]}]}

瞧!我们使用私钥,从不需要使用gcloud进行身份验证或查询我们的预测模型!