class_docstr: Usage:
      Run the iris_classifier service in production mode:
  
          bentoml serve-http iris_classifier:latest
  
      Start locust load testing client with:
  
          locust --class-picker -H http://localhost:3000
  
      Open browser at http://0.0.0.0:8089, adjust desired number of users and spawn
      rate for the load test from the Web UI and start swarming.
class_docstr_tok: ["Usage",":","Run","the","iris_classifier","service","in","production","mode",":","bentoml","serve-http","iris_classifier",":","latest","Start","locust","load","testing","client","with",":","locust","--","class-picker","-H","http",":","\/\/localhost:3000","Open","browser","at","http",":","\/\/0.0.0.0:8089",",","adjust","desired","number","of","users","and","spawn","rate","for","the","load","test","from","the","Web","UI","and","start","swarming","."]
code: def classify(self):
      start = np.random.choice(num_of_rows - max_batch_size)
      end = start + np.random.choice(max_batch_size) + 1
  
      input_data = test_data[start:end]
      self.client.post("/classify", json=input_data.tolist())
  
file_path: BentoML/examples/quickstart/locustfile.py
imports: import time
  import grpc
  import numpy
  from locust import HttpUser
  from locust import User
  from locust import between
  from locust import task
  from sklearn import datasets
  from bentoml.grpc.v1 import service_pb2
  from bentoml.grpc.v1 import service_pb2_grpc
label_desc: Use image node_id 1 for calling the IrisHttpUser obj's underlying member method code with example usage: obj.classify() without return types