Push documentation changes to gh-pages.

Author: Chris Olston

architecture_overview.md

@@ -1,3 +1,6 @@
+---
+---
+
 # Architecture Overview
 
 TensorFlow Serving is a flexible, high-performance serving system for machine
@@ -98,7 +101,7 @@ versions to the Manager, it supercedes the previous list for that servable
 stream. The Manager unloads any previously loaded versions that no longer
 appear in the list.
 
-See the [advanced tutorial](serving_advanced.md) to see how version loading
+See the [advanced tutorial](serving_advanced) to see how version loading
 works in practice.
 
 ### Managers
@@ -198,7 +201,7 @@ easy & fast to create new sources. For example, TensorFlow Serving includes a
 utility to wrap polling behavior around a simple source. Sources are closely
 related to Loaders for specific algorithms and data hosting servables.
 
-See the [Custom Source](custom_source.md) document for more about how to create
+See the [Custom Source](custom_source) document for more about how to create
 a custom Source.
 
 ### Loaders
@@ -209,7 +212,7 @@ new Loader in order to load, provide access to, and unload an instance of a
 new type of servable machine learning model. We anticipate creating Loaders
 for lookup tables and additional algorithms.
 
-See the [Custom Servable](custom_servable.md) document to learn how to create a
+See the [Custom Servable](custom_servable) document to learn how to create a
 custom servable.
 
 ### Batcher
@@ -225,4 +228,4 @@ for more information.
 ## Next Steps
 
 To get started with TensorFlow Serving, try the
-[Basic Tutorial](serving_basic.md).
+[Basic Tutorial](serving_basic).

custom_servable.md

@@ -1,3 +1,6 @@
+---
+---
+
 # Creating a new kind of servable
 
 This document explains how to extend TensorFlow Serving with a new kind of
@@ -25,7 +28,7 @@ define methods for loading, accessing and unloading your type of servable. The
 data from which the servable is loaded can come from anywhere, but it is common
 for it to come from a storage-system path. Let us assume that is the case for
 `YourServable`. Let us further assume you already have a `Source<StoragePath>`
-that you are happy with (if not, see the [Custom Source](custom_source.md)
+that you are happy with (if not, see the [Custom Source](custom_source)
 document).
 
 In addition to your `Loader`, you will need to define a `SourceAdapter` that

custom_source.md

@@ -1,3 +1,6 @@
+---
+---
+
 # Creating a module that discovers new servable paths
 
 This document explains how to extend TensorFlow Serving to monitor different
@@ -24,7 +27,7 @@ Of course, whatever kind of data your source emits (whether it is POSIX paths,
 Google Cloud Storage paths, or RPC handles), there needs to be accompanying
 module(s) that are able to load servables based on that. Such modules are called
 `SourceAdapters`. Creating a custom one is described in the
-[Custom Servable](custom_servable.md) document. TensorFlow Serving
+[Custom Servable](custom_servable) document. TensorFlow Serving
 comes with one for instantiating TensorFlow sessions based on paths
 in file systems that TensorFlow supports. One can add support for
 additional file systems to TensorFlow by extending the `RandomAccessFile`

docker.md

@@ -1,3 +1,6 @@
+---
+---
+
 # Using TensorFlow Serving via Docker
 
 This directory contains Dockerfiles to make it easy to get up and running with

index.md

@@ -1,3 +1,6 @@
+---
+---
+
 # Introduction
 
 TensorFlow Serving is a flexible, high-performance serving system for machine
@@ -9,9 +12,9 @@ types of models and data.
 
 To get started with TensorFlow Serving:
 
-* Read the [overview](architecture_overview.md)
-* [Set up](setup.md) your environment
-* Do the [basic tutorial](serving_basic.md)
+* Read the [overview](architecture_overview)
+* [Set up](setup) your environment
+* Do the [basic tutorial](serving_basic)
 
 
 

serving_advanced.md

@@ -1,10 +1,13 @@
+---
+---
+
 # Building Standard TensorFlow ModelServer
 
 This tutorial shows you how to use TensorFlow Serving components to build the
 standard TensorFlow ModelServer that dynamically discovers and serves new
 versions of a trained TensorFlow model. If you just want to use the standard
 server to serve your models, see
-[TensorFlow Serving basic tutorial](serving_basic.md).
+[TensorFlow Serving basic tutorial](serving_basic).
 
 This tutorial uses the simple Softmax Regression model introduced in the
 TensorFlow tutorial for handwritten image (MNIST data) classification. If you
@@ -32,11 +35,11 @@ This tutorial steps through the following tasks:
 5.  Run and test the service.
 
 Before getting started, please complete the
-[prerequisites](setup.md#prerequisites).
+[prerequisites](setup#prerequisites).
 
 Note: All `bazel build` commands below use the standard `-c opt` flag. To
 further optimize the build, refer to the
-[instructions here](setup.md#optimized-build).
+[instructions here](setup#optimized-build).
 
 ## Train And Export TensorFlow Model
 
@@ -60,7 +63,7 @@ $>bazel-bin/tensorflow_serving/example/mnist_saved_model --training_iteration=20
 ```
 
 As you can see in `mnist_saved_model.py`, the training and exporting is done the
-same way it is in the [TensorFlow Serving basic tutorial](serving_basic.md). For
+same way it is in the [TensorFlow Serving basic tutorial](serving_basic). For
 demonstration purposes, you're intentionally dialing down the training
 iterations for the first run and exporting it as v1, while training it normally
 for the second run and exporting it as v2 to the same parent directory -- as we
@@ -165,8 +168,8 @@ that monitors cloud storage instead of local storage, or you could build a
 version policy plugin that does version transition in a different way -- in
 fact, you could even build a custom model plugin that serves non-TensorFlow
 models. These topics are out of scope for this tutorial. However, you can refer
-to the [custom source](custom_source.md) and [custom servable]
-(custom_servable.md) tutorials for more information.
+to the [custom source](custom_source) and [custom servable]
+(custom_servable) tutorials for more information.
 
 ## Batching
 

serving_basic.md

@@ -1,10 +1,13 @@
+---
+---
+
 # Serving a TensorFlow Model
 
 This tutorial shows you how to use TensorFlow Serving components to export a
 trained TensorFlow model and use the standard tensorflow_model_server to serve
 it. If you are already familiar with TensorFlow Serving, and you want to know
 more about how the server internals work, see the
-[TensorFlow Serving advanced tutorial](serving_advanced.md).
+[TensorFlow Serving advanced tutorial](serving_advanced).
 
 This tutorial uses the simple Softmax Regression model introduced in the
 TensorFlow tutorial for handwritten image (MNIST data) classification. If you
@@ -25,11 +28,11 @@ The code for this tutorial consists of two parts:
     [gRPC](http://www.grpc.io) service for serving them.
 
 Before getting started, please complete the
-[prerequisites](setup.md#prerequisites).
+[prerequisites](setup#prerequisites).
 
 Note: All `bazel build` commands below use the standard `-c opt` flag. To
 further optimize the build, refer to the
-[instructions here](setup.md#optimized-build).
+[instructions here](setup#optimized-build).
 
 ## Train And Export TensorFlow Model
 
@@ -141,7 +144,7 @@ allows the user to refer to these tensors with their logical names when
 running inference.
 
 Note: In addition to the description above, documentation related to signature
-def structure and how to set up them up can be found [here](signature_defs.md).
+def structure and how to set up them up can be found [here](signature_defs).
 
 Let's run it!
 
@@ -154,7 +157,7 @@ $>rm -rf /tmp/mnist_model
 If you would like to install the `tensorflow` and `tensorflow-serving-api` PIP
 packages, you can run all Python code (export and client) using a simple
 `python` command. To install the PIP package, follow the
-[instructions here](setup.md#tensorflow-serving-python-api-pip-package).
+[instructions here](setup#tensorflow-serving-python-api-pip-package).
 It's also possible to
 use Bazel to build the necessary dependencies and run all code without
 installing those packages. The rest of the codelab will have instructions for
@@ -216,7 +219,7 @@ $>bazel-bin/tensorflow_serving/model_servers/tensorflow_model_server --port=9000
 ```
 
 If you'd prefer to skip compilation and install using apt-get, follow the
-[instructions here](setup.md#installing-using-apt-get). Then run the server with
+[instructions here](setup#installing-using-apt-get). Then run the server with
 the following command:
 
 ```shell

serving_inception.md

@@ -1,12 +1,15 @@
+---
+---
+
 # Serving Inception Model with TensorFlow Serving and Kubernetes
 
 This tutorial shows how to use TensorFlow Serving components running in Docker
 containers to serve the TensorFlow Inception model and how to deploy the
 serving cluster with Kubernetes.
 
 To learn more about TensorFlow Serving, we recommend
-[TensorFlow Serving basic tutorial](serving_basic.md) and
-[TensorFlow Serving advanced tutorial](serving_advanced.md).
+[TensorFlow Serving basic tutorial](serving_basic) and
+[TensorFlow Serving advanced tutorial](serving_advanced).
 
 To learn more about TensorFlow Inception model, we recommend
 [Inception in TensorFlow](https://github.com/tensorflow/models/tree/master/inception).
@@ -19,7 +22,7 @@ To learn more about TensorFlow Inception model, we recommend
 
 ## Part 0: Create a Docker image
 
-Please refer to [Using TensorFlow Serving via Docker](docker.md) for details
+Please refer to [Using TensorFlow Serving via Docker](docker) for details
 about building a TensorFlow Serving Docker image.
 
 ### Run container
@@ -37,7 +40,7 @@ $ docker run --name=inception_container -it $USER/tensorflow-serving-devel
 
 Note: All `bazel build` commands below use the standard `-c opt` flag. To
 further optimize the build, refer to the
-[instructions here](setup.md#optimized-build).
+[instructions here](setup#optimized-build).
 
 In the running container, we clone, configure and build TensorFlow Serving
 example code.
@@ -51,7 +54,7 @@ root@c97d8e820ced:/serving# bazel build -c opt tensorflow_serving/example/...
 ```
 
 Next we can either install a TensorFlow ModelServer with apt-get using the
-[instructions here](setup.md#installing-using-apt-get), or build a ModelServer
+[instructions here](setup#installing-using-apt-get), or build a ModelServer
 binary using:
 
 ```shell

setup.md

@@ -1,3 +1,6 @@
+---
+---
+
 # Installation
 
 ## Prerequisites
@@ -170,7 +173,7 @@ To test your installation, execute:
 bazel test -c opt tensorflow_serving/...
 ```
 
-See the [basic tutorial](serving_basic.md) and [advanced tutorial](serving_advanced.md)
+See the [basic tutorial](serving_basic) and [advanced tutorial](serving_advanced)
 for more in-depth examples of running TensorFlow Serving.
 
 ### Optimized build

signature_defs.md

@@ -1,3 +1,6 @@
+---
+---
+
 # SignatureDefs in SavedModel for TensorFlow Serving
 
 ## Objective