title: Unable to open pickled Sagemaker XGBoost model
body: <p>I'm trying to open a pickled XGBoost model I created in AWS Sagemaker to look at feature importances in the model. I'm trying to follow the answers in <a href="https://stackoverflow.com/questions/55621967/feature-importance-for-xgboost-in-sagemaker">this post</a>. However, I get an the error shown below. When I try to call <code>Booster.save_model</code>, I get an error saying <code>'Estimator' object has no attribute 'save_model'</code>. How can I resolve this? </p>
  
  <pre><code># Build initial model
  sess = sagemaker.Session()
  s3_input_train = sagemaker.s3_input(s3_data='s3://{}/{}/train/'.format(bucket, prefix), content_type='csv')
  xgb_cont = get_image_uri(region, 'xgboost', repo_version='0.90-1')
  xgb = sagemaker.estimator.Estimator(xgb_cont, role, train_instance_count=1, train_instance_type='ml.m4.4xlarge',
                                      output_path='s3://{}/{}'.format(bucket, prefix), sagemaker_session=sess)
  xgb.set_hyperparameters(eval_metric='rmse', objective='reg:squarederror', num_round=100)
  ts = strftime("%Y-%m-%d-%H-%M-%S", gmtime())
  xgb_name = 'xgb-initial-' + ts
  xgb.set_hyperparameters(eta=0.1, alpha=0.5, max_depth=10)
  xgb.fit({'train': s3_input_train}, job_name=xgb_name)
  
  # Load model to get feature importances
  model_path = 's3://{}/{}//output/model.tar.gz'.format(bucket, prefix, xgb_name)
  fs = s3fs.S3FileSystem()
  with fs.open(model_path, 'rb') as f:
      with tarfile.open(fileobj=f, mode='r') as tar_f:
          with tar_f.extractfile('xgboost-model') as extracted_f:
              model = pickle.load(extracted_f)
  
  XGBoostError: [19:16:42] /workspace/src/learner.cc:682: Check failed: header == serialisation_header_: 
  
    If you are loading a serialized model (like pickle in Python) generated by older
    XGBoost, please export the model by calling `Booster.save_model` from that version
    first, then load it back in current version.  There's a simple script for helping
    the process. See:
  
      https://xgboost.readthedocs.io/en/latest/tutorials/saving_model.html
  
    for reference to the script, and more details about differences between saving model and
    serializing.
  </code></pre>