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Ibm cloud 502坏网关-调用计分api终结点

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Ibm cloud 502坏网关-调用计分api终结点,ibm-cloud,watson-ml,Ibm Cloud,Watson Ml,我正在使用社区笔记本中的以下代码使用IBM Watson Machine Learning预测户外设备购买量： ... <code omitted for brevity> ... import urllib3, requests, json ​ headers = urllib3.util.make_headers(basic_auth='{}:{}'.format(username, password)) url = '{}/v2/identity/token'.format(

我正在使用社区笔记本中的以下代码使用IBM Watson Machine Learning预测户外设备购买量：

...
<code omitted for brevity>
...

import urllib3, requests, json
​
headers = urllib3.util.make_headers(basic_auth='{}:{}'.format(username, password))
url = '{}/v2/identity/token'.format(service_path)
response = requests.get(url, headers=headers)
mltoken = json.loads(response.text).get('token')

endpoint_online = service_path + "/v2/online/deployments/"
header_online = {'Content-Type': 'application/json', 'Authorization': mltoken}
payload_online = {"artifactVersionHref": saved_model.meta.prop("modelVersionHref"), "name": "Product Line Prediction"}
​
response_online = requests.post(endpoint_online, json=payload_online, headers=header_online)
​
print response_online
print response_online.text

scoring_href = json.loads(response_online.text).get('entity').get('scoringHref')
print scoring_href
答复如下：

<html>
<head><title>502 Bad Gateway</title></head>
<body bgcolor="white">
<center><h1>502 Bad Gateway</h1></center>
<hr><center>nginx/1.10.1</center>
</body>
</html>


502坏网关
502坏网关

nginx/1.10.1
几分钟后我重试了呼叫，呼叫成功：

{
 "result":{
      "PROFESSION_IX":6.0,
      "GENDER_IX":0.0,
      "MARITAL_STATUS_IX":1.0,
      "GENDER":"M",
      "features":{
         "values":[
            0.0,
            23.0,
            1.0,
            6.0
         ]
      },
      "predictedLabel":"Personal Accessories",
      "prediction":1.0,
      ...
}

<html>
<head><title>502 Bad Gateway</title></head>
<body bgcolor="white">
<center><h1>502 Bad Gateway</h1></center>
<hr><center>nginx/1.10.1</center>
</body>
</html>

{
 "result":{
      "PROFESSION_IX":6.0,
      "GENDER_IX":0.0,
      "MARITAL_STATUS_IX":1.0,
      "GENDER":"M",
      "features":{
         "values":[
            0.0,
            23.0,
            1.0,
            6.0
         ]
      },
      "predictedLabel":"Personal Accessories",
      "prediction":1.0,
      ...
}