人工智能平台 PAI：Python SDK使用说明

安装方法

pip install -U eas-prediction --user

接口列表

程序示例

• 字符串输入输出示例

  对于使用自定义Processor部署服务的用户而言，通常采用字符串进行服务调用（例如，PMML模型服务的调用），具体的Demo程序如下。

  #!/usr/bin/env python
  
  from eas_prediction import PredictClient
  from eas_prediction import StringRequest
  
  if __name__ == '__main__':
      client = PredictClient('http://182848887922****.cn-shanghai.pai-eas.aliyuncs.com', 'scorecard_pmml_example')
      client.set_token('YWFlMDYyZDNmNTc3M2I3MzMwYmY0MmYwM2Y2MTYxMTY4NzBkNzdj****')
      client.init()
  
      request = StringRequest('[{"fea1": 1, "fea2": 2}]')
      for x in range(0, 1000000):
          resp = client.predict(request)
          print(resp)

• TensorFlow输入输出示例

  使用TensorFlow的用户，需要将TFRequest和TFResponse分别作为输入和输出数据格式，具体Demo示例如下。

  #!/usr/bin/env python
  
  from eas_prediction import PredictClient
  from eas_prediction import StringRequest
  from eas_prediction import TFRequest
  
  if __name__ == '__main__':
      client = PredictClient('http://182848887922****.cn-shanghai.pai-eas.aliyuncs.com', 'mnist_saved_model_example')
      client.set_token('YTg2ZjE0ZjM4ZmE3OTc0NzYxZDMyNmYzMTJjZTQ1YmU0N2FjMTAy****')
      client.init()
  
      #request = StringRequest('[{}]')
      req = TFRequest('predict_images')
      req.add_feed('images', [1, 784], TFRequest.DT_FLOAT, [1] * 784)
      for x in range(0, 1000000):
          resp = client.predict(req)
          print(resp)

• 通过VPC网络直连方式调用服务的示例

  通过网络直连方式，您只能访问部署在EAS专属资源组的服务，且需要为该资源组与用户指定的vSwitch连通网络后才能使用。关于如何购买EAS专属资源组和连通网络，请参见使用专属资源组和配置网络连通。该调用方式与普通调用方式相比，仅需增加一行代码client.set_endpoint_type(ENDPOINT_TYPE_DIRECT)即可，特别适合大流量高并发的服务，具体示例如下。

  #!/usr/bin/env python
  
  from eas_prediction import PredictClient
  from eas_prediction import StringRequest
  from eas_prediction import TFRequest
  from eas_prediction import ENDPOINT_TYPE_DIRECT
  
  if __name__ == '__main__':
      client = PredictClient('http://pai-eas-vpc.cn-hangzhou.aliyuncs.com', 'mnist_saved_model_example')
      client.set_token('M2FhNjJlZDBmMzBmMzE4NjFiNzZhMmUxY2IxZjkyMDczNzAzYjFi****')
      client.set_endpoint_type(ENDPOINT_TYPE_DIRECT)
      client.init()
  
      request = TFRequest('predict_images')
      request.add_feed('images', [1, 784], TFRequest.DT_FLOAT, [1] * 784)
      for x in range(0, 1000000):
          resp = client.predict(request)
          print(resp)

• PyTorch输入输出示例

  使用PyTorch的用户，需要将TorchRequest和TorchResponse分别作为输入和输出数据格式，具体Demo示例如下。

  #!/usr/bin/env python
  
  from eas_prediction import PredictClient
  from eas_prediction import TorchRequest
  
  if __name__ == '__main__':
      client = PredictClient('http://182848887922****.cn-shanghai.pai-eas.aliyuncs.com', 'pytorch_gpu_wl')
      client.init()
  
      req = TorchRequest()
      req.add_feed(0, [1, 3, 224, 224], TorchRequest.DT_FLOAT, [1] * 150528)
      # req.add_fetch(0)
      import time
      st = time.time()
      timer = 0
      for x in range(0, 10):
          resp = client.predict(req)
          timer += (time.time() - st)
          st = time.time()
          print(resp.get_tensor_shape(0))
          # print(resp)
      print("average response time: %s s" % (timer / 10) )

• BladeProcessor输入输出示例

  使用BladeProcessor的用户，需要将BladeRequest和BladeResponse分别作为输入和输出数据格式，具体Demo示例如下。

  #!/usr/bin/env python
  
  from eas_prediction import PredictClient
  from eas_prediction import BladeRequest 
  
  if __name__ == '__main__':
      client = PredictClient('http://182848887922****.cn-shanghai.pai-eas.aliyuncs.com', 'nlp_model_example')
      client.init()
  
      req = BladeRequest()
  
      req.add_feed('input_data', 1, [1, 360, 128], BladeRequest.DT_FLOAT, [0.8] * 85680)
      req.add_feed('input_length', 1, [1], BladeRequest.DT_INT32, [187])
      req.add_feed('start_token', 1, [1], BladeRequest.DT_INT32, [104])
      req.add_fetch('output', BladeRequest.DT_FLOAT)
      import time
      st = time.time()
      timer = 0
      for x in range(0, 10):
          resp = client.predict(req)
          timer += (time.time() - st)
          st = time.time()
          # print(resp)
          # print(resp.get_values('output'))
          print(resp.get_tensor_shape('output'))
      print("average response time: %s s" % (timer / 10) )

• 兼容EAS默认TensorFlow接口的BladeProcessor输入输出示例

  BladeProcessor用户可以使用兼容EAS默认TensorFlow接口的TFRequest与TFResponse作为数据的输入输出格式，具体Demo示例如下。

  #!/usr/bin/env python
  
  from eas_prediction import PredictClient
  from eas_prediction.blade_tf_request import TFRequest # Need Importing blade TFRequest 
  
  if __name__ == '__main__':
      client = PredictClient('http://182848887922****.cn-shanghai.pai-eas.aliyuncs.com', 'nlp_model_example')
      client.init()
  
      req = TFRequest(signature_name='predict_words')
  
      req.add_feed('input_data', [1, 360, 128], TFRequest.DT_FLOAT, [0.8] * 85680)
      req.add_feed('input_length', [1], TFRequest.DT_INT32, [187])
      req.add_feed('start_token', [1], TFRequest.DT_INT32, [104])
      req.add_fetch('output')
      import time
      st = time.time()
      timer = 0
      for x in range(0, 10):
          resp = client.predict(req)
          timer += (time.time() - st)
          st = time.time()
          # print(resp)
          # print(resp.get_values('output'))
          print(resp.get_tensor_shape('output'))
      print("average response time: %s s" % (timer / 10) )

• 队列服务发送、订阅数据示例

  通过QueueClient可向队列服务中发送数据、查询数据、查询队列服务的状态以及订阅队列服务中的数据推送。以下方的Demo为例，介绍一个线程向队列服务中推送数据，另外一个线程通过Watcher订阅队列服务中推送过来的数据。

  #!/usr/bin/env python
  
  from eas_prediction import QueueClient
  import threading
  
  if __name__ == '__main__':
      endpoint = '182848887922****.cn-shanghai.pai-eas.aliyuncs.com'
      queue_name = 'test_group.qservice/sink'
      token = 'YmE3NDkyMzdiMzNmMGM3ZmE4ZmNjZDk0M2NiMDA3OTZmNzc1MTUx****'
  
      queue = QueueClient(endpoint, queue_name)
      queue.set_token(token)
      queue.init()
      queue.set_timeout(30000)
  
      # truncate all messages in the queue
      attributes = queue.attributes()
      if 'stream.lastEntry' in attributes:
          queue.truncate(int(attributes['stream.lastEntry']) + 1)
  
      count = 100
      # create a thread to send messages to the queue
      def send_thread():
          for i in range(count):
              index, request_id = queue.put('[{}]')
              print('send: ', i, index, request_id)
  
      # create a thread to watch messages from the queue
      def watch_thread():
          watcher = queue.watch(0, 5, auto_commit=True)
          i = 0
          for x in watcher.run():
              print('recv: ', i, x.index, x.tags['requestId'])
              i += 1
              if i == count:
                  break
          watcher.close()
  
      thread1 = threading.Thread(target=watch_thread)
      thread2 = threading.Thread(target=send_thread)
  
      thread1.start()
      thread2.start()
  
      thread1.join()
      thread2.join()