text: # Copyright 2017-2018 Amazon.com, Inc. or its affiliates. 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. A copy of
  # the License is located at
  #
  #     http://aws.amazon.com/apache2.0/
  #
  # or in the "license" file accompanying this file. This file 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 __future__ import absolute_import
  
  import os
  import time
  
  import numpy
  import pytest
  
  import tests.integ
  from sagemaker.mxnet.estimator import MXNet
  from sagemaker.mxnet.model import MXNetModel
  from sagemaker.utils import sagemaker_timestamp
  from tests.integ import DATA_DIR, PYTHON_VERSION, TRAINING_DEFAULT_TIMEOUT_MINUTES
  from tests.integ.timeout import timeout, timeout_and_delete_endpoint_by_name
  
  
  @pytest.fixture(scope='module')
  def mxnet_training_job(sagemaker_session, mxnet_full_version):
      with timeout(minutes=TRAINING_DEFAULT_TIMEOUT_MINUTES):
          script_path = os.path.join(DATA_DIR, 'mxnet_mnist', 'mnist.py')
          data_path = os.path.join(DATA_DIR, 'mxnet_mnist')
  
          mx = MXNet(entry_point=script_path, role='SageMakerRole', framework_version=mxnet_full_version,
                     py_version=PYTHON_VERSION, train_instance_count=1, train_instance_type='ml.c4.xlarge',
                     sagemaker_session=sagemaker_session)
  
          train_input = mx.sagemaker_session.upload_data(path=os.path.join(data_path, 'train'),
                                                         key_prefix='integ-test-data/mxnet_mnist/train')
          test_input = mx.sagemaker_session.upload_data(path=os.path.join(data_path, 'test'),
                                                        key_prefix='integ-test-data/mxnet_mnist/test')
  
          mx.fit({'train': train_input, 'test': test_input})
          return mx.latest_training_job.name
  
  
  @pytest.mark.canary_quick
  @pytest.mark.regional_testing
  def test_attach_deploy(mxnet_training_job, sagemaker_session):
      endpoint_name = 'test-mxnet-attach-deploy-{}'.format(sagemaker_timestamp())
  
      with timeout_and_delete_endpoint_by_name(endpoint_name, sagemaker_session):
          estimator = MXNet.attach(mxnet_training_job, sagemaker_session=sagemaker_session)
          predictor = estimator.deploy(1, 'ml.m4.xlarge', endpoint_name=endpoint_name)
          data = numpy.zeros(shape=(1, 1, 28, 28))
          predictor.predict(data)
  
  
  def test_deploy_model(mxnet_training_job, sagemaker_session, mxnet_full_version):
      endpoint_name = 'test-mxnet-deploy-model-{}'.format(sagemaker_timestamp())
  
      with timeout_and_delete_endpoint_by_name(endpoint_name, sagemaker_session):
          desc = sagemaker_session.sagemaker_client.describe_training_job(TrainingJobName=mxnet_training_job)
          model_data = desc['ModelArtifacts']['S3ModelArtifacts']
          script_path = os.path.join(DATA_DIR, 'mxnet_mnist', 'mnist.py')
          model = MXNetModel(model_data, 'SageMakerRole', entry_point=script_path,
                             py_version=PYTHON_VERSION, sagemaker_session=sagemaker_session,
                             framework_version=mxnet_full_version)
          predictor = model.deploy(1, 'ml.m4.xlarge', endpoint_name=endpoint_name)
  
          data = numpy.zeros(shape=(1, 1, 28, 28))
          predictor.predict(data)
  
      predictor.delete_model()
      with pytest.raises(Exception) as exception:
          sagemaker_session.sagemaker_client.describe_model(ModelName=model.name)
          assert 'Could not find model' in str(exception.value)
  
  
  def test_deploy_model_with_update_endpoint(mxnet_training_job, sagemaker_session, mxnet_full_version):
      endpoint_name = 'test-mxnet-deploy-model-{}'.format(sagemaker_timestamp())
  
      with timeout_and_delete_endpoint_by_name(endpoint_name, sagemaker_session):
          desc = sagemaker_session.sagemaker_client.describe_training_job(TrainingJobName=mxnet_training_job)
          model_data = desc['ModelArtifacts']['S3ModelArtifacts']
          script_path = os.path.join(DATA_DIR, 'mxnet_mnist', 'mnist.py')
          model = MXNetModel(model_data, 'SageMakerRole', entry_point=script_path,
                             py_version=PYTHON_VERSION, sagemaker_session=sagemaker_session,
                             framework_version=mxnet_full_version)
          model.deploy(1, 'ml.t2.medium', endpoint_name=endpoint_name)
          old_endpoint = sagemaker_session.describe_endpoint(EndpointName=endpoint_name)
          old_config_name = old_endpoint['EndpointConfigName']
  
          model.deploy(1, 'ml.m4.xlarge', update_endpoint=True, endpoint_name=endpoint_name)
          new_endpoint = sagemaker_session.describe_endpoint(EndpointName=endpoint_name)['ProductionVariants']
          new_production_variants = new_endpoint['ProductionVariants']
          new_config_name = new_endpoint['EndpointConfigName']
  
          assert old_config_name != new_config_name
          assert new_production_variants['InstanceType'] == 'ml.m4.xlarge'
          assert new_production_variants['InitialInstanceCount'] == 1
          assert new_production_variants['AcceleratorType'] is None
  
  
  def test_deploy_model_with_update_non_existing_endpoint(mxnet_training_job, sagemaker_session, mxnet_full_version):
      endpoint_name = 'test-mxnet-deploy-model-{}'.format(sagemaker_timestamp())
      expected_error_message = 'Endpoint with name "{}" does not exist; ' \
                               'please use an existing endpoint name'.format(endpoint_name)
  
      with timeout_and_delete_endpoint_by_name(endpoint_name, sagemaker_session):
          desc = sagemaker_session.sagemaker_client.describe_training_job(TrainingJobName=mxnet_training_job)
          model_data = desc['ModelArtifacts']['S3ModelArtifacts']
          script_path = os.path.join(DATA_DIR, 'mxnet_mnist', 'mnist.py')
          model = MXNetModel(model_data, 'SageMakerRole', entry_point=script_path,
                             py_version=PYTHON_VERSION, sagemaker_session=sagemaker_session,
                             framework_version=mxnet_full_version)
          model.deploy(1, 'ml.t2.medium', endpoint_name=endpoint_name)
          sagemaker_session.describe_endpoint(EndpointName=endpoint_name)
  
          with pytest.raises(ValueError, message=expected_error_message):
              model.deploy(1, 'ml.m4.xlarge', update_endpoint=True, endpoint_name='non-existing-endpoint')
  
  
  @pytest.mark.canary_quick
  @pytest.mark.regional_testing
  @pytest.mark.skipif(tests.integ.test_region() not in tests.integ.EI_SUPPORTED_REGIONS,
                      reason="EI isn't supported in that specific region.")
  def test_deploy_model_with_accelerator(mxnet_training_job, sagemaker_session, ei_mxnet_full_version):
      endpoint_name = 'test-mxnet-deploy-model-ei-{}'.format(sagemaker_timestamp())
  
      with timeout_and_delete_endpoint_by_name(endpoint_name, sagemaker_session):
          desc = sagemaker_session.sagemaker_client.describe_training_job(TrainingJobName=mxnet_training_job)
          model_data = desc['ModelArtifacts']['S3ModelArtifacts']
          script_path = os.path.join(DATA_DIR, 'mxnet_mnist', 'mnist.py')
          model = MXNetModel(model_data, 'SageMakerRole', entry_point=script_path,
                             framework_version=ei_mxnet_full_version, py_version=PYTHON_VERSION,
                             sagemaker_session=sagemaker_session)
          predictor = model.deploy(1, 'ml.m4.xlarge', endpoint_name=endpoint_name, accelerator_type='ml.eia1.medium')
  
          data = numpy.zeros(shape=(1, 1, 28, 28))
          predictor.predict(data)
  
  
  def test_async_fit(sagemaker_session, mxnet_full_version):
      endpoint_name = 'test-mxnet-attach-deploy-{}'.format(sagemaker_timestamp())
  
      with timeout(minutes=5):
          script_path = os.path.join(DATA_DIR, 'mxnet_mnist', 'mnist.py')
          data_path = os.path.join(DATA_DIR, 'mxnet_mnist')
  
          mx = MXNet(entry_point=script_path, role='SageMakerRole', py_version=PYTHON_VERSION,
                     train_instance_count=1, train_instance_type='ml.c4.xlarge',
                     sagemaker_session=sagemaker_session, framework_version=mxnet_full_version,
                     distributions={'parameter_server': {'enabled': True}})
  
          train_input = mx.sagemaker_session.upload_data(path=os.path.join(data_path, 'train'),
                                                         key_prefix='integ-test-data/mxnet_mnist/train')
          test_input = mx.sagemaker_session.upload_data(path=os.path.join(data_path, 'test'),
                                                        key_prefix='integ-test-data/mxnet_mnist/test')
  
          mx.fit({'train': train_input, 'test': test_input}, wait=False)
          training_job_name = mx.latest_training_job.name
  
          print("Waiting to re-attach to the training job: %s" % training_job_name)
          time.sleep(20)
  
      with timeout_and_delete_endpoint_by_name(endpoint_name, sagemaker_session):
          print("Re-attaching now to: %s" % training_job_name)
          estimator = MXNet.attach(training_job_name=training_job_name, sagemaker_session=sagemaker_session)
          predictor = estimator.deploy(1, 'ml.m4.xlarge', endpoint_name=endpoint_name)
          data = numpy.zeros(shape=(1, 1, 28, 28))
          predictor.predict(data)
  
  
  def test_failed_training_job(sagemaker_session, mxnet_full_version):
      with timeout():
          script_path = os.path.join(DATA_DIR, 'mxnet_mnist', 'failure_script.py')
  
          mx = MXNet(entry_point=script_path, role='SageMakerRole', framework_version=mxnet_full_version,
                     py_version=PYTHON_VERSION, train_instance_count=1, train_instance_type='ml.c4.xlarge',
                     sagemaker_session=sagemaker_session)
  
          with pytest.raises(ValueError) as e:
              mx.fit()
          assert 'ExecuteUserScriptError' in str(e.value)
  