fix errors

Author: laurenyu

doc/amazon_sagemaker_operators_for_kubernetes.rst

@@ -19,4 +19,4 @@ Analytics
 .. autoclass:: sagemaker.analytics.ExperimentAnalytics
     :members:
     :undoc-members:
-    :show-inheritance:
+    :show-inheritance:

doc/analytics.rst

@@ -6,4 +6,4 @@ Make real-time predictions against SageMaker endpoints with Python objects
 .. autoclass:: sagemaker.predictor.RealTimePredictor
     :members:
     :undoc-members:
-    :show-inheritance:
+    :show-inheritance:

doc/predictors.rst

@@ -17,7 +17,7 @@ The XGBoost open source algorithm provides the following benefits over the built
 * Latest version - The open source XGBoost algorithm typically supports a more recent version of XGBoost.
   To see the XGBoost version that is currently supported,
   see `XGBoost SageMaker Estimators and Models <https://github.com/aws/sagemaker-python-sdk/tree/master/src/sagemaker/xgboost#xgboost-sagemaker-estimators-and-models>`__.
-* Flexibility - Take advantage of the full range of XGBoost functionality, such as cross-validation support. 
+* Flexibility - Take advantage of the full range of XGBoost functionality, such as cross-validation support.
   You can add custom pre- and post-processing logic and run additional code after training.
 * Scalability - The XGBoost open source algorithm has a more efficient implementation of distributed training,
   which enables it to scale out to more instances and reduce out-of-memory errors.
@@ -100,14 +100,14 @@ such as the location of input data and location where we want to save the model.
         parser.add_argument('--max_depth', type=int, default=5)
         parser.add_argument('--eta', type=float, default=0.2)
         parser.add_argument('--objective', type=str, default='reg:squarederror')
-    
+
         # SageMaker specific arguments. Defaults are set in the environment variables.
         parser.add_argument('--model_dir', type=str, default=os.environ.get('SM_MODEL_DIR'))
         parser.add_argument('--train', type=str, default=os.environ['SM_CHANNEL_TRAIN'])
         parser.add_argument('--validation', type=str, default=os.environ['SM_CHANNEL_VALIDATION'])
-    
+
         args = parser.parse_args()
-    
+
         train_hp = {
             'max_depth': args.max_depth,
             'eta': args.eta,
@@ -117,15 +117,15 @@ such as the location of input data and location where we want to save the model.
             'silent': args.silent,
             'objective': args.objective
         }
-    
+
         dtrain = xgb.DMatrix(args.train)
         dval = xgb.DMatrix(args.validation)
         watchlist = [(dtrain, 'train'), (dval, 'validation')] if dval is not None else [(dtrain, 'train')]
 
         callbacks = []
         prev_checkpoint, n_iterations_prev_run = add_checkpointing(callbacks)
         # If checkpoint is found then we reduce num_boost_round by previously run number of iterations
-    
+
         bst = xgb.train(
             params=train_hp,
             dtrain=dtrain,
@@ -134,7 +134,7 @@ such as the location of input data and location where we want to save the model.
             xgb_model=prev_checkpoint,
             callbacks=callbacks
         )
-    
+
         # Save the model to the location specified by ``model_dir``
         model_location = args.model_dir + '/xgboost-model'
         pkl.dump(bst, open(model_location, 'wb'))
@@ -154,7 +154,7 @@ and a dictionary of the hyperparameters to pass to the training script.
     xgb_estimator = XGBoost(
         entry_point="abalone.py",
         hyperparameters=hyperparameters,
-        role=role, 
+        role=role,
         train_instance_count=1,
         train_instance_type="ml.m5.2xlarge",
         framework_version="0.90-1",
@@ -210,6 +210,3 @@ SageMaker XGBoost Docker Containers
 ***********************************
 
 For information about SageMaker XGBoost Docker container and its dependencies, see `SageMaker XGBoost Container <https://github.com/aws/sagemaker-xgboost-container>`_.
-
-
-

doc/using_xgboost.rst

@@ -15,4 +15,4 @@ The Amazon SageMaker XGBoost open source framework algorithm.
 .. autoclass:: sagemaker.xgboost.model.XGBoostPredictor
     :members:
     :undoc-members:
-    :show-inheritance:
+    :show-inheritance:

doc/xgboost.rst

@@ -11,7 +11,7 @@ You can visit the Chainer repository at https://github.com/chainer/chainer.
 For information about using Chainer with the SageMaker Python SDK, see https://sagemaker.readthedocs.io/en/stable/using_chainer.html.
 
 Chainer Training Examples
-~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~
 
 Amazon provides several example Jupyter notebooks that demonstrate end-to-end training on Amazon SageMaker using Chainer.
 Please refer to:
@@ -22,7 +22,7 @@ These are also available in SageMaker Notebook Instance hosted Jupyter notebooks
 
 
 SageMaker Chainer Docker containers
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 When training and deploying training scripts, SageMaker runs your Python script in a Docker container with several
 libraries installed. When creating the Estimator and calling deploy to create the SageMaker Endpoint, you can control

src/sagemaker/chainer/README.rst

@@ -262,8 +262,7 @@ Specifying the output of a prediction request
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
 The structure of the prediction ``result`` is determined at the end of the training process before SavedModel is created. For example, if
-you are using TensorFlow's Estimator API for training, you control inference outputs using the ``export_outputs`` parameter of the `tf.estimator.EstimatorSpec <https://www.tensorflow.org/api_docs/python/tf/estimator/EstimatorSpec>`_ that you return from
-your ``model_fn`` (see `Example of a complete model_fn`_ for an example of ``export_outputs``).
+you are using TensorFlow's Estimator API for training, you control inference outputs using the ``export_outputs`` parameter of the `tf.estimator.EstimatorSpec <https://www.tensorflow.org/api_docs/python/tf/estimator/EstimatorSpec>`_ that you return from your ``model_fn``.
 
 More information on how to create ``export_outputs`` can be found in `specifying the outputs of a custom model <https://github.com/tensorflow/tensorflow/blob/r1.4/tensorflow/docs_src/programmers_guide/saved_model.md#specifying-the-outputs-of-a-custom-model>`_. You can also
 refer to TensorFlow's `Save and Restore <https://www.tensorflow.org/guide/saved_model>`_ documentation for other ways to control the

src/sagemaker/tensorflow/deploying_tensorflow_serving.rst

@@ -10,4 +10,4 @@ is a platform that enables you to programmatically author, schedule, and monitor
 you can build a workflow for SageMaker training, hyperparameter tuning, batch transform and endpoint deployment.
 You can use any SageMaker deep learning framework or Amazon algorithms to perform above operations in Airflow.
 
-For information about using SageMaker Workflow, see https://sagemaker.readthedocs.io/en/stable/using_workflow.html.
+For information about using SageMaker Workflow, see https://sagemaker.readthedocs.io/en/stable/using_workflow.html.

src/sagemaker/workflow/README.rst

@@ -1 +1 @@
-asset-file-contents
+asset-file-contents

tests/data/tfs/tfs-test-entrypoint-with-handler/123/assets/foo.txt

@@ -1 +1 @@
-asset-file-contents
+asset-file-contents