content: #  Copyright (c) ZenML GmbH 2020. All Rights Reserved.
  #
  #  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:
  #
  #       https://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.
  from sklearn.svm import SVC
  from steps.evaluator_step import evaluator
  from steps.loader_step import importer
  from steps.normalizer_step import normalizer
  from steps.trainer_step import trainer
  
  from zenml import pipeline
  from zenml.config import DockerSettings
  from zenml.integrations.constants import MLFLOW, SKLEARN
  
  docker_settings = DockerSettings(
      required_integrations=[MLFLOW, SKLEARN], requirements=["scikit-image"]
  )
  
  
  @pipeline(enable_cache=False, settings={"docker": docker_settings})
  def mlflow_tracking_pipeline() -> SVC:
      X_train, X_test, y_train, y_test = importer()
      X_trained_normed, X_test_normed = normalizer(
          X_train=X_train, X_test=X_test
      )
      model = trainer(X_train=X_trained_normed, y_train=y_train)
      evaluator(X_test=X_test_normed, y_test=y_test, model=model)
      return model
  