title: Sagemaker model deployment failing due to custom endpoint name
body: <p>AWS Sagemaker model deployment is failing when endpoint_name argument is specified. Any thoughts?</p>
  
  <p>Without endpoint_name argument in deploy, model deployment works successfully.
  Model training and saving into S3 location is successful either way.</p>
  
  <pre><code>import boto3
  import os
  import sagemaker
  from sagemaker import get_execution_role
  from sagemaker.predictor import csv_serializer
  from sagemaker.amazon.amazon_estimator import get_image_uri
  
  bucket = 'Y'
  prefix = 'Z'
  
  role = get_execution_role()
  
      train_data, validation_data, test_data = np.split(df.sample(frac=1, random_state=100), [int(0.5 * len(df)), int(0.8 * len(df))])
  
      train_data.to_csv('train.csv', index=False, header=False)
      validation_data.to_csv('validation.csv', index=False, header=False)
      test_data.to_csv('test.csv', index=False)
      boto3.Session().resource('s3').Bucket(bucket).Object(os.path.join(prefix, 'train/X/train.csv')).upload_file('train.csv')
      boto3.Session().resource('s3').Bucket(bucket).Object(os.path.join(prefix, 'validation/X/validation.csv')).upload_file('validation.csv')
  
      container = get_image_uri(boto3.Session().region_name, 'xgboost')
      #print(container)
  
      s3_input_train = sagemaker.s3_input(s3_data='s3://{}/{}/train/{}'.format(bucket, prefix, suffix), content_type='csv')
      s3_input_validation = sagemaker.s3_input(s3_data='s3://{}/{}/validation/{}/'.format(bucket, prefix, suffix), content_type='csv')
  
      sess = sagemaker.Session()
  
      output_loc = 's3://{}/{}/output'.format(bucket, prefix)
      xgb = sagemaker.estimator.Estimator(container,
                                          role, 
                                          train_instance_count=1, 
                                          train_instance_type='ml.m4.xlarge',
                                          output_path=output_loc,
                                          sagemaker_session=sess,
                                          base_job_name='X')
      #print('Model output to: {}'.format(output_location))
  
      xgb.set_hyperparameters(eta=0.5,
                              objective='reg:linear',
                              eval_metric='rmse',
                              max_depth=3,
                              min_child_weight=1,
                              gamma=0,
                              early_stopping_rounds=10,
                              subsample=0.8,
                              colsample_bytree=0.8,
                              num_round=1000)
  
      #Model fitting
      xgb.fit({'train': s3_input_train, 'validation': s3_input_validation})
  
      #Deploy model with automatic endpoint created
      xgb_predictor_X = xgb.deploy(initial_instance_count=1, instance_type='ml.m4.xlarge', endpoint_name='X')
  
      xgb_predictor_X.content_type = 'text/csv'
      xgb_predictor_X.serializer = csv_serializer
      xgb_predictor_X.deserializer = None
  </code></pre>
  
  <p>INFO:sagemaker:Creating endpoint with name delaymins
  ClientError: An error occurred (ValidationException) when calling the CreateEndpoint operation: Could not find model "arn:aws:sagemaker:us-west-2::model/X-2019-01-08-18-17-42-158".</p>