Question

我正在处理从Google App Engine到Google Cloud ML的请求（在线预测）（我没有创建模型），并且我不时会遇到异常＆＃34;等待HTTP时超出截止日期来自网址的响应＆＃34; 完全跟踪：

    Deadline exceeded while waiting for HTTP response from URL: https://ml.googleapis.com/v1/projects/project-id/models/my-model/versions/v3:predict?alt=json (/base/data/home/runtimes/python27/python27_lib/versions/third_party/webapp2-2.5.2/webapp2.py:1552)
Traceback (most recent call last):
  File "/base/data/home/runtimes/python27/python27_lib/versions/third_party/webapp2-2.5.2/webapp2.py", line 1535, in __call__
    rv = self.handle_exception(request, response, e)
  File "/base/data/home/runtimes/python27/python27_lib/versions/third_party/webapp2-2.5.2/webapp2.py", line 1529, in __call__
    rv = self.router.dispatch(request, response)
  File "/base/data/home/runtimes/python27/python27_lib/versions/third_party/webapp2-2.5.2/webapp2.py", line 1278, in default_dispatcher
    return route.handler_adapter(request, response)
  File "/base/data/home/runtimes/python27/python27_lib/versions/third_party/webapp2-2.5.2/webapp2.py", line 1102, in __call__
    return handler.dispatch()
  File "/base/data/home/runtimes/python27/python27_lib/versions/third_party/webapp2-2.5.2/webapp2.py", line 572, in dispatch
    return self.handle_exception(e, self.app.debug)
  File "/base/data/home/runtimes/python27/python27_lib/versions/third_party/webapp2-2.5.2/webapp2.py", line 570, in dispatch
    return method(*args, **kwargs)
  File "/base/data/home/apps/s~project-id/1.402312581449917691/main.py", line 90, in post
    response = predict(batch_obj=batch_data_obj)
  File "/base/data/home/apps/s~project-id/1.402312581449917691/run_cloud_predict.py", line 88, in predict
    response = request.execute()
  File "/base/data/home/apps/s~project-id/1.402312581449917691/lib/oauth2client/util.py", line 135, in positional_wrapper
    return wrapped(*args, **kwargs)
  File "/base/data/home/apps/s~project-id/1.402312581449917691/lib/googleapiclient/http.py", line 835, in execute
    method=str(self.method), body=self.body, headers=self.headers)
  File "/base/data/home/apps/s~project-id/1.402312581449917691/lib/googleapiclient/http.py", line 162, in _retry_request
    resp, content = http.request(uri, method, *args, **kwargs)
  File "/base/data/home/apps/s~project-id/1.402312581449917691/lib/oauth2client/client.py", line 631, in new_request
    redirections, connection_type)
  File "/base/data/home/apps/s~project-id/1.402312581449917691/lib/httplib2/__init__.py", line 1659, in request
    (response, content) = self._request(conn, authority, uri, request_uri, method, body, headers, redirections, cachekey)
  File "/base/data/home/apps/s~project-id/1.402312581449917691/lib/httplib2/__init__.py", line 1399, in _request
    (response, content) = self._conn_request(conn, request_uri, method, body, headers)
  File "/base/data/home/apps/s~project-id/1.402312581449917691/lib/httplib2/__init__.py", line 1355, in _conn_request
    response = conn.getresponse()
  File "/base/data/home/runtimes/python27/python27_dist/lib/python2.7/gae_override/httplib.py", line 526, in getresponse
    raise HTTPException(str(e))
HTTPException: Deadline exceeded while waiting for HTTP response from URL: https://ml.googleapis.com/v1/projects/project-id/models/my-model/versions/v3:predict?alt=json

现在我知道Google App Engine的响应限制为60秒，这就是我使用taskqueue进行请求的原因。我也试过以下事情：

URLFETCH_DEADLINE = 3600
urlfetch.set_default_fetch_deadline(URLFETCH_DEADLINE)
socket.setdefaulttimeout(URLFETCH_DEADLINE)

我正在构建像这样的api客户端

import httplib2
from googleapiclient import discovery
from oauth2client import service_account

credentials = service_account.ServiceAccountCredentials.from_json_keyfile_name('credentials-file', scopes)
http = httplib2.Http(timeout=36000)
http = credentials.authorize(http)

ml = discovery.build('ml', 'v1', http=http)
request = ml.projects().predict(name=predict_ver_name, body=request_data)

有趣的是，有时超时发生在70s左右（69.9,70,70.1等），有时大约120s（119.8,120.1等），这告诉我这可能需要做一些内部的Cloud ML处理。我通过taskqueue并行执行几十个请求。成功的响应时间从几秒到大约110秒如果有人有相似的经历，或者可以给我建议如何解决这个问题，即导致截止日期的原因，我只是好奇。

Answer 1

感谢您发布您的体验。 - 有一些启动成本，并且根据请求的速率，可能需要启动多个服务器来满足需求。 - 您要预测的模型大小是多少？较大的型号往往具有较大的启动成本。

感谢。

Answer 2

您可以使用下面的代码在api客户端上轻松设置超时时间。

import socket
timeout_in_sec = 60*3 # 3 minutes timeout limit
socket.setdefaulttimeout(timeout_in_sec)

然后，您可以像往常一样创建ML服务对象，它将具有扩展的超时限制。

ml_service = discovery.build('ml', 'v1')

Google Cloud ML超时请求

2 个答案: