如何在google ml api python客户端中设置请求超时？

Question

我正在使用 google api python 客户端和在 google cloud 为我托管的模型在 google cloud machine learning API 上运行在线预测。当我预测发送一张图像时，服务器（包括所有流量）大约需要 40 秒。当我发送两个图像时，一段时间后，我收到消息：

timeout: The read operation timed out

我想将超时设置为其他值，但我没有找到方法。

这是我的代码：

import base64
import io
import time
from PIL import Image    

from oauth2client.service_account import ServiceAccountCredentials
from googleapiclient import discovery

SCOPES = ['https://www.googleapis.com/auth/cloud-platform']
SERVICE_ACCOUNT_FILE = 'mycredentialsfile.json'

credentials = ServiceAccountCredentials.from_json_keyfile_name(
        SERVICE_ACCOUNT_FILE, scopes=SCOPES)

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

projectID = 'projects/{}'.format('projectID') + '/models/{}'.format('modelID')

width = 640
height = 480

instances = []

for image in ["image5.jpg", "image6.jpg"]:
    img = Image.open(image)
    img = img.resize((width, height), Image.ANTIALIAS)
    output_str = io.BytesIO()
    img.save(output_str, "JPEG")
    instance = {"b64": base64.b64encode(output_str.getvalue()).decode("utf-8") }
    output_str.close()
    instances.append(instance)  

input_json = {"instances": instances }

request = ml.projects().predict(body=input_json, name=projectID)

print("Starting prediction")
start_time = time.time()
response = request.execute()

print("%s seconds" % (time.time() - start_time))

Answer 1

我找到了一种从 github 上的 google api python 客户端研究样本并尝试相同更改的方法。

使用 httplib2 进行身份验证，您可以设置超时。

按照修改后的代码：

import base64
import io
import time
from PIL import Image

# Need: pip install google-api-python-client

import httplib2
from oauth2client.service_account import ServiceAccountCredentials
from googleapiclient import discovery

SCOPES = ['https://www.googleapis.com/auth/cloud-platform']
# API & Services -> Credentials -> Create Credential -> service account key
SERVICE_ACCOUNT_FILE = 'mycredentialsfile.json'

credentials = ServiceAccountCredentials.from_json_keyfile_name(
        SERVICE_ACCOUNT_FILE, scopes=SCOPES)

http = httplib2.Http(timeout=200)
http = credentials.authorize(http)

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

projectID = 'projects/{}'.format('projectID ') + '/models/{}'.format('modelID')

width = 640
height = 480

instances = []

for image in ["image5.jpg", "image6.jpg"]:
    img = Image.open(image)
    img = img.resize((width, height), Image.ANTIALIAS)
    output_str = io.BytesIO()
    img.save(output_str, "JPEG")
    instance = {"b64": base64.b64encode(output_str.getvalue()).decode("utf-8") }
    output_str.close()
    instances.append(instance)  

input_json = {"instances": instances }

request = ml.projects().predict(body=input_json, name=projectID)

print("Starting prediction")
start_time = time.time()
response = request.execute()

print("%s seconds" % (time.time() - start_time))

我认为通过一些修改，您可以使用它为 python 客户端中的几乎所有谷歌云 API 设置超时。

我希望这有帮助。

Answer 2

是的。我同意上面 Shohei 的回答。我花了一段时间才找到这个简单而优雅的解决方案。您只需要在代码中添加以下内容

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

# then you could create your ML service object as usually, and it will have the extended timeout limit.
ml_service = discovery.build('ml', 'v1')