Fix some documentation.

README.md

@@ -1,7 +1,7 @@
 #TensorFlow Serving
 
 TensorFlow Serving is an open-source software library for serving
-machine-learned models. It deals with the *inference* aspect of machine
+machine learning models. It deals with the *inference* aspect of machine
 learning, taking models after *training* and managing their lifetimes, providing
 clients with versioned access via a high-performance, reference-counted lookup
 table.
@@ -23,7 +23,7 @@ but at its core it manages arbitrary versioned items (*servables*) with
 pass-through to their native APIs. In addition to trained TensorFlow models,
 servables can include other assets needed for inference such as embeddings,
 vocabularies and feature transformation configs, or even non-TensorFlow-based
-machine-learned models.
+machine learning models.
 
 The architecture is highly modular. You can use some parts individually (e.g.
 batch scheduling) or use all the parts together. There are numerous plug-in
@@ -41,6 +41,11 @@ tracking requests and bugs.
 
 See [install instructions](tensorflow_serving/g3doc/setup.md).
 
+##Tutorials
+
+* [Basic tutorial](tensorflow_serving/g3doc/serving_basic.md)
+* [Advanced tutorial](tensorflow_serving/g3doc/serving_advanced.md)
+
 ##For more information
 
 * [Serving architecture overview](tensorflow_serving/g3doc/architecture_overview.md)

tensorflow_serving/g3doc/architecture_overview.md

@@ -1,7 +1,3 @@
----
----
-<style>hr{display:none;}</style>
-
 # Architecture Overview
 
 TensorFlow Serving is a flexible, high-performance serving system for machine
@@ -102,7 +98,7 @@ 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) to see how version loading
+See the [advanced tutorial](serving_advanced.md) to see how version loading
 works in practice.
 
 ### Managers
@@ -200,7 +196,7 @@ 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) document for more about how to create
+See the [Custom Source](custom_source.md) document for more about how to create
 a custom Source.
 
 ### Loaders
@@ -211,7 +207,7 @@ 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) document to learn how to create a
+See the [Custom Servable](custom_servable.md) document to learn how to create a
 custom servable.
 
 ### Batcher
@@ -226,4 +222,4 @@ process.
 ## Next Steps
 
 To get started with TensorFlow Serving, try the
-[Basic Tutorial](serving_basic).
+[Basic Tutorial](serving_basic.md).

tensorflow_serving/g3doc/custom_servable.md

@@ -1,7 +1,3 @@
----
----
-<style>hr{display:none;}</style>
-
 # Creating a new kind of servable
 
 This document explains how to extend TensorFlow Serving with a new kind of
@@ -26,7 +22,7 @@ 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's assume that's the case for
 `YourServable`. Let's further assume you already have a `Source<StoragePath>`
-that you are happy with (if not, see the [Custom Source](custom_source)
+that you are happy with (if not, see the [Custom Source](custom_source.md)
 document). In addition to your Loader, you will need to define a `SourceAdapter`
 that instantiates a `Loader` from a given storage path. Most simple use-cases
 can specify the two objects concisely via the `SimpleLoaderSourceAdapter` class

tensorflow_serving/g3doc/custom_source.md

@@ -1,7 +1,3 @@
----
----
-<style>hr{display:none;}</style>
-
 # Creating a module that discovers new servable paths
 
 This document explains how to extend TensorFlow Serving to monitor different

tensorflow_serving/g3doc/index.md

@@ -1,7 +1,3 @@
----
----
-<style>hr{display:none;}</style>
-
 TensorFlow Serving is a flexible, high-performance serving system for machine
 learning models, designed for production environments. TensorFlow Serving
 makes it easy to deploy new algorithms and experiments, while keeping the same
@@ -11,10 +7,10 @@ types of models and data.
 
 To get started with TensorFlow Serving:
 
-* Read the [overview](architecture_overview)
-* [Set up](setup) your environment
-* Do the [basic tutorial](serving_basic)
+* Read the [overview](architecture_overview.md)
+* [Set up](setup.md) your environment
+* Do the [basic tutorial](serving_basic.md)
 
 
 
-![TensorFlow Serving Diagram](images/tf_diagram.svg){: width="500"}
+![TensorFlow Serving Diagram](images/tf_diagram.svg){: width="500"}

tensorflow_serving/g3doc/serving_advanced.md

@@ -53,7 +53,7 @@ $>bazel-bin/tensorflow_serving/example/mnist_export --training_iteration=100 --e
 Train (with 2000 iterations) and export the second version of model:
 
 ~~~
-$>bazel-bin/tensorflow_serving/example:mnist_export --training_iteration=2000 --export_version=2 /tmp/mnist_model
+$>bazel-bin/tensorflow_serving/example/mnist_export --training_iteration=2000 --export_version=2 /tmp/mnist_model
 ~~~
 
 As you can see in `mnist_export.py`, the training and exporting is done the

tensorflow_serving/g3doc/serving_basic.md

@@ -1,7 +1,3 @@
----
----
-<style>hr{display:none;}</style>
-
 # Serving a TensorFlow Model
 
 This tutorial shows you how to use TensorFlow Serving components to export a
@@ -12,7 +8,7 @@ requests), and it calculates an aggregate inference error rate. If you're
 already familiar with TensorFlow Serving, and you want to create a more complex
 server that handles batched inference requests, and discovers and serves new
 versions of a TensorFlow model that is being dynamically updated, see the
-[TensorFlow Serving advanced tutorial](serving_advanced).
+[TensorFlow Serving advanced tutorial](serving_advanced.md).
 
 This tutorial uses the simple Softmax Regression model introduced in the
 TensorFlow tutorial for handwritten image (MNIST data) classification. If you
@@ -26,7 +22,7 @@ trains and exports the model, and a C++ file
 ([mnist_inference.cc](https://github.com/tensorflow/serving/tree/master/tensorflow_serving/example/mnist_inference.cc)) that loads the
 exported model and runs a [gRPC](http://www.grpc.io) service to serve it.
 
-Before getting started, please complete the [prerequisites](setup#prerequisites).
+Before getting started, please complete the [prerequisites](setup.md#prerequisites).
 
 ## Train And Export TensorFlow Model
 

tensorflow_serving/g3doc/setup.md

@@ -1,7 +1,3 @@
----
----
-<style>hr{display:none;}</style>
-
 # Installation
 
 ## Prerequisites
@@ -36,7 +32,7 @@ following steps:
 
 Our tutorials use [gRPC](http://www.grpc.io) (0.13 or higher) as our RPC
 framework. You can find the installation instructions
-[here](https://github.com/grpc/grpc/issues/5111).
+[here](https://github.com/grpc/grpc/tree/master/src/python/grpcio).
 
 ### Packages
 
@@ -114,5 +110,5 @@ To test your installation, execute:
 bazel test tensorflow_serving/...
 ~~~
 
-See the [basic tutorial](serving_basic) and [advanced tutorial](serving_advanced)
+See the [basic tutorial](serving_basic.md) and [advanced tutorial](serving_advanced.md)
 for more in-depth examples of running TensorFlow Serving.