通过 Python 实现 Google 云机器学习引擎的在线预测

Question

我想从 App Engine 向 ML Engine 的模型发送请求。

我在本地机器上构建了用于测试的示例代码。但是，我收到错误 'UNAUTHENTICATED'：

WARNING:root:No ssl package found. urlfetch will not be able to validate SSL certificates.
91bnQuY34Oh8cJyF=....
{
  "error": {
    "code": 401,
    "message": "Request had invalid authentication credentials. Expected OAuth 2 access token, login cookie or other valid authentication credential. See https://developers.google.com/identity/sign-in/web/devconsole-project.",
    "status": "UNAUTHENTICATED"
  }
}

我的示例代码：

from google.appengine.api import urlfetch
import urllib
import time
import jwt

data_to_post = {"instances":[{"text":[185,15,14,124,370,832,112,120,...]}]}
encoded_data = urllib.urlencode(data_to_post)

model_url = 'https://ml.googleapis.com/v1/projects/myproject/models/analyizesentiment:predict'

from google.appengine.api import apiproxy_stub_map 
from google.appengine.api import urlfetch_stub
apiproxy_stub_map.apiproxy = apiproxy_stub_map.APIProxyStubMap() 
apiproxy_stub_map.apiproxy.RegisterStub('urlfetch', urlfetch_stub.URLFetchServiceStub())

PRIVATE_KEY_ID_FROM_JSON = '??????????????????'
PRIVATE_KEY_FROM_JSON = "-----BEGIN PRIVATE KEY-----\n??????????\n-----END PRIVATE KEY-----\n"
iat = time.time()
exp = iat + 3600
payload = {'iss': 'accountname@myprojectname.iam.gserviceaccount.com',
           'sub': 'accountname@myprojectname.iam.gserviceaccount.com',
           'aud': 'https://ml.googleapis.com/v1/',
           'iat': iat,
           'exp': exp}
additional_headers = {'kid': PRIVATE_KEY_ID_FROM_JSON,
                      'alg': "RS256",
                      "typ": "JWT",}
signed_jwt = jwt.encode(payload, PRIVATE_KEY_FROM_JSON, headers=additional_headers, algorithm='RS256')
print(signed_jwt)

result = urlfetch.fetch(model_url, encoded_data, method=urlfetch.POST, headers={'Content-type': 'application/json', 'Authorization': 'Bearer ' + signed_jwt})
print(result.content)

Answer 1

缺的是header和

header名称："Authorization" 值："Bearer $token"

其中令牌是 oauth 令牌。您可以使用

生成令牌

gcloud auth login

或者您可以下载服务帐户的令牌。有用的阅读：https://developers.google.com/api-client-library/python/guide/aaa_oauth

但是，在应用引擎上，您应该能够从标准凭据 api 获取它

您还可以修改您的示例以使用凭据 object 并以编程方式附加该 header。

from googleapiclient import discovery
from oauth2client.client import GoogleCredentials

PROJECT=''
MODEL=''
VERSION=''
credentials = GoogleCredentials.get_application_default()
api = discovery.build('ml', 'v1', credentials=credentials)

# Add your sample instances here.
request_data = {'instances':[] }

parent = 'projects/%s/models/%s/versions/%s' % (PROJECT, MODEL, VERSION)
response = api.projects().predict(body=request_data, name=parent).execute()
print "response={0}".format(response)

如果您想提供不同的超时，上面的 execute() 方法可以使用 http object。你应该能够提供额外的 headers.

通过 Python 实现 Google 云机器学习引擎的在线预测

Implement online predictions of Google Cloud Machine Learning Engine by Python

google-app-engine

google-authentication

google-cloud-ml