doc: update using_tensorflow topic

doc/conf.py

@@ -60,6 +60,7 @@ def __getattr__(cls, name):
     "sphinx.ext.coverage",
     "sphinx.ext.autosummary",
     "sphinx.ext.napoleon",
+    "sphinx.ext.autosectionlabel",
 ]
 
 # Add any paths that contain templates here, relative to this directory.
@@ -95,3 +96,6 @@ def __getattr__(cls, name):
 
 # autosummary
 autosummary_generate = True
+
+# autosectionlabel
+autosectionlabel_prefix_document = True

doc/overview.rst

@@ -826,59 +826,6 @@ A new training job channel, named ``code``, will be added with that S3 URI.  Bef
 
 Once the training job begins, the training container will look at the offline input ``code`` channel to install dependencies and run the entry script. This isolates the training container, so no inbound or outbound network calls can be made.
 
-*********
-BYO Model
-*********
-
-You can also create an endpoint from an existing model rather than training one.
-That is, you can bring your own model:
-
-First, package the files for the trained model into a ``.tar.gz`` file, and upload the archive to S3.
-
-Next, create a ``Model`` object that corresponds to the framework that you are using: `MXNetModel <https://sagemaker.readthedocs.io/en/stable/sagemaker.mxnet.html#mxnet-model>`__ or `TensorFlowModel <https://sagemaker.readthedocs.io/en/stable/sagemaker.tensorflow.html#tensorflow-model>`__.
-
-Example code using ``MXNetModel``:
-
-.. code:: python
-
-   from sagemaker.mxnet.model import MXNetModel
-
-   sagemaker_model = MXNetModel(model_data='s3://path/to/model.tar.gz',
-                                role='arn:aws:iam::accid:sagemaker-role',
-                                entry_point='entry_point.py')
-
-After that, invoke the ``deploy()`` method on the ``Model``:
-
-.. code:: python
-
-   predictor = sagemaker_model.deploy(initial_instance_count=1,
-                                      instance_type='ml.m4.xlarge')
-
-This returns a predictor the same way an ``Estimator`` does when ``deploy()`` is called. You can now get inferences just like with any other model deployed on Amazon SageMaker.
-
-Git support is also available when you bring your own model, through which you can use inference scripts stored in your
-Git repositories. The process is similar to using Git support for training jobs. You can simply provide ``git_config``
-when create the ``Model`` object, and let ``entry_point``, ``source_dir`` and ``dependencies`` (if needed) be relative
-paths inside the Git repository:
-
-.. code:: python
-
-    git_config = {'repo': 'https://github.com/username/repo-with-training-scripts.git',
-                  'branch': 'branch1',
-                  'commit': '4893e528afa4a790331e1b5286954f073b0f14a2'}
-
-    sagemaker_model = MXNetModel(model_data='s3://path/to/model.tar.gz',
-                                role='arn:aws:iam::accid:sagemaker-role',
-                                entry_point='inference.py',
-                                source_dir='mxnet',
-                                git_config=git_config)
-
-A full example is available in the `Amazon SageMaker examples repository <https://github.com/awslabs/amazon-sagemaker-examples/tree/master/advanced_functionality/mxnet_mnist_byom>`__.
-
-You can also find this notebook in the **Advanced Functionality** section of the **SageMaker Examples** section in a notebook instance.
-For information about using sample notebooks in a SageMaker notebook instance, see `Use Example Notebooks <https://docs.aws.amazon.com/sagemaker/latest/dg/howitworks-nbexamples.html>`__
-in the AWS documentation.
-
 *******************
 Inference Pipelines
 *******************

doc/using_mxnet.rst

@@ -411,8 +411,8 @@ Calling fit
 
 You start your training script by calling ``fit`` on an ``MXNet`` Estimator. ``fit`` takes both required and optional arguments.
 
-Required argument
-'''''''''''''''''
+fit Required argument
+'''''''''''''''''''''
 
 -  ``inputs``: This can take one of the following forms: A string
    S3 URI, for example ``s3://my-bucket/my-training-data``. In this
@@ -430,8 +430,8 @@ For example:
 
 .. optional-arguments-1:
 
-Optional arguments
-''''''''''''''''''
+fit Optional arguments
+''''''''''''''''''''''
 
 -  ``wait``: Defaults to True, whether to block and wait for the
    training script to complete before returning.