text: # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #    http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  
  import os
  import json
  from kubernetes import client
  
  from kserve import KServeClient
  from kserve import constants
  from kserve import V1beta1PredictorSpec
  from kserve import V1beta1Batcher
  from kserve import V1beta1SKLearnSpec
  from kserve import V1beta1InferenceServiceSpec
  from kserve import V1beta1InferenceService
  
  from kubernetes.client import V1ResourceRequirements
  from ..common.utils import predict_str
  from ..common.utils import KSERVE_TEST_NAMESPACE
  from concurrent import futures
  
  kserve_client = KServeClient(config_file=os.environ.get("KUBECONFIG", "~/.kube/config"))
  
  
  input_file = open('./data/iris_batch_input.json')
  json_array = json.load(input_file)
  
  
  def test_batcher():
      service_name = 'isvc-sklearn-batcher'
  
      predictor = V1beta1PredictorSpec(
          batcher=V1beta1Batcher(
              max_batch_size=32,
              max_latency=5000,
          ),
          min_replicas=1,
          sklearn=V1beta1SKLearnSpec(
              storage_uri="gs://kfserving-examples/models/sklearn/1.0/model",
              resources=V1ResourceRequirements(
                  requests={"cpu": "50m", "memory": "128Mi"},
                  limits={"cpu": "100m", "memory": "256Mi"},
              ),
          ),
      )
  
      isvc = V1beta1InferenceService(api_version=constants.KSERVE_V1BETA1,
                                     kind=constants.KSERVE_KIND,
                                     metadata=client.V1ObjectMeta(
                                         name=service_name, namespace=KSERVE_TEST_NAMESPACE
                                     ), spec=V1beta1InferenceServiceSpec(predictor=predictor),
                                     )
      kserve_client.create(isvc)
      try:
          kserve_client.wait_isvc_ready(service_name, namespace=KSERVE_TEST_NAMESPACE)
      except RuntimeError as e:
          print(kserve_client.api_instance.get_namespaced_custom_object("serving.knative.dev", "v1",
                                                                        KSERVE_TEST_NAMESPACE,
                                                                        "services", service_name + "-predictor-default"))
          pods = kserve_client.core_api.list_namespaced_pod(KSERVE_TEST_NAMESPACE,
                                                            label_selector='serving.kserve.io/inferenceservice={}'.
                                                            format(service_name))
          for pod in pods.items:
              print(pod)
          raise e
      with futures.ThreadPoolExecutor(max_workers=4) as executor:
          future_res = [
              executor.submit(lambda: predict_str(service_name, json.dumps(item))) for item in json_array
          ]
      results = [
          f.result()["batchId"] for f in future_res
      ]
      assert (all(x == results[0] for x in results))
      kserve_client.delete(service_name, KSERVE_TEST_NAMESPACE)
  