Platform For AI：Python SDK使用說明

安裝方法

pip install -U eas-prediction --user

介面列表

程式樣本

• 字串輸入輸出樣本

  對於使用自訂Processor部署服務的使用者而言，通常採用字串進行服務調用（例如，PMML模型服務的調用），具體的Demo程式如下。

  #!/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輸入輸出樣本

  使用TensorFlow的使用者，需要將TFRequest和TFResponse分別作為輸入和輸出資料格式，具體Demo樣本如下。

  #!/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)

• 通過VPC網路直連方式調用服務的樣本

  通過網路直連方式，您只能訪問部署在EAS專屬資源群組的服務，且需要為該資源群組與使用者指定的vSwitch連通網路後才能使用。關於如何購買EAS專屬資源群組和連通網路，請參見使用專屬資源群組和配置網路連通。該調用方式與普通調用方式相比，僅需增加一行代碼client.set_endpoint_type(ENDPOINT_TYPE_DIRECT)即可，特別適合大流量高並發的服務，具體樣本如下。

  #!/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輸入輸出樣本

  使用PyTorch的使用者，需要將TorchRequest和TorchResponse分別作為輸入和輸出資料格式，具體Demo樣本如下。

  #!/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輸入輸出樣本

  使用BladeProcessor的使用者，需要將BladeRequest和BladeResponse分別作為輸入和輸出資料格式，具體Demo樣本如下。

  #!/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) )

• 相容EAS預設TensorFlow介面的BladeProcessor輸入輸出樣本

  BladeProcessor使用者可以使用相容EAS預設TensorFlow介面的TFRequest與TFResponse作為資料的輸入輸出格式，具體Demo樣本如下。

  #!/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) )

• 佇列服務發送、訂閱資料樣本

  通過QueueClient可向佇列服務中發送資料、查詢資料、查詢佇列服務的狀態以及訂閱佇列服務中的資料推送。以下方的Demo為例，介紹一個線程向佇列服務中推送資料，另外一個線程通過Watcher訂閱佇列服務中推送過來的資料。

  #!/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()