Rename programmers_guide/ directory to guide/.

Update references in source files and docs in tensorflow and related projects.

PiperOrigin-RevId: 201766994

58 files changed, 119 insertions, 110 deletions

diff --git a/README.md b/README.md
index 6fb4486d0d..4e4d139bd1 100644
--- a/README.md
+++ b/README.md
@@ -14,7 +14,7 @@ data flow graphs.  The graph nodes represent mathematical operations, while
 the graph edges represent the multidimensional data arrays (tensors) that flow
 between them.  This flexible architecture enables you to deploy computation to one
 or more CPUs or GPUs in a desktop, server, or mobile device without rewriting
-code.  TensorFlow also includes [TensorBoard](https://www.tensorflow.org/programmers_guide/summaries_and_tensorboard), a data visualization toolkit.
+code.  TensorFlow also includes [TensorBoard](https://www.tensorflow.org/guide/summaries_and_tensorboard), a data visualization toolkit.
 
 TensorFlow was originally developed by researchers and engineers
 working on the Google Brain team within Google's Machine Intelligence Research
diff --git a/RELEASE.md b/RELEASE.md
index 510eca5467..5fec61af7e 100644
--- a/RELEASE.md
+++ b/RELEASE.md
@@ -467,7 +467,7 @@ answered questions, and were part of inspiring discussions.
 
 ## Major Features And Improvements
 * `tf.keras` is now part of the core TensorFlow API.
-* [`tf.data`](http://tensorflow.org/programmers_guide/datasets) is now part of
+* [`tf.data`](http://tensorflow.org/guide/datasets) is now part of
   the core TensorFlow API.
   * The API is now subject to backwards compatibility guarantees.
 
@@ -495,7 +495,7 @@ answered questions, and were part of inspiring discussions.
 * TensorFlow Debugger (tfdbg):
   * Add `eval` command to allow evaluation of arbitrary Python/numpy expressions
     in tfdbg command-line interface. See
-    [Debugging TensorFlow Programs](https://www.tensorflow.org/programmers_guide/debugger)
+    [Debugging TensorFlow Programs](https://www.tensorflow.org/guide/debugger)
     for more details.
   * Usability improvement: The frequently used tensor filter `has_inf_or_nan` is
     now added to `Session` wrappers and hooks by default. So there is no need
@@ -782,7 +782,7 @@ answered questions, and were part of inspiring discussions.
 * Support client-provided ClusterSpec's and propagate them to all workers to enable the creation of dynamic TensorFlow clusters.
 * TensorFlow C library now available for Windows.
 * We released a new open-source version of TensorBoard.
-* [`SavedModel CLI`](https://www.tensorflow.org/versions/master/programmers_guide/saved_model_cli) tool available to inspect and execute MetaGraph in SavedModel
+* [`SavedModel CLI`](https://www.tensorflow.org/versions/master/guide/saved_model_cli) tool available to inspect and execute MetaGraph in SavedModel
 * Android releases of TensorFlow are now pushed to jcenter for easier
   integration into apps. See
   https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/android/README.md
diff --git a/tensorflow/contrib/autograph/README.md b/tensorflow/contrib/autograph/README.md
index 674859bed4..47b1d4a99a 100644
--- a/tensorflow/contrib/autograph/README.md
+++ b/tensorflow/contrib/autograph/README.md
@@ -4,7 +4,7 @@ IMPORTANT: AutoGraph is alpha software, and under active development. Expect rou
 
 AutoGraph is a Python to TensorFlow compiler.
 
-With AutoGraph, you can write [Eager style](https://www.tensorflow.org/programmers_guide/eager) code in a concise manner, and run it as a TensorFlow graph. AutoGraph uses source code transformation and partial evaluation to generate Python code that builds an equivalent TensorFlow subgraph. The result is code that behaves like ops and can be freely combined with other TensorFlow ops.
+With AutoGraph, you can write [Eager style](https://www.tensorflow.org/guide/eager) code in a concise manner, and run it as a TensorFlow graph. AutoGraph uses source code transformation and partial evaluation to generate Python code that builds an equivalent TensorFlow subgraph. The result is code that behaves like ops and can be freely combined with other TensorFlow ops.
 
 For example, this Python function:
 
diff --git a/tensorflow/contrib/data/__init__.py b/tensorflow/contrib/data/__init__.py
index 9c6a13333e..3510e7b1ad 100644
--- a/tensorflow/contrib/data/__init__.py
+++ b/tensorflow/contrib/data/__init__.py
@@ -20,7 +20,7 @@ be used in conjunction with the @{tf.data.Dataset} API. Note that the
 guarantees as `tf.data`, but we will provide deprecation advice in advance of
 removing existing functionality.
 
-See the @{$datasets$Importing Data} Programmer's Guide for an overview.
+See @{$guide/datasets$Importing Data} for an overview.
 
 @@Counter
 @@CheckpointInputPipelineHook
diff --git a/tensorflow/contrib/eager/README.md b/tensorflow/contrib/eager/README.md
index 4384431e7b..86d203452e 100644
--- a/tensorflow/contrib/eager/README.md
+++ b/tensorflow/contrib/eager/README.md
@@ -44,7 +44,7 @@ Installation instructions at https://www.tensorflow.org/install/
 
 For an introduction to eager execution in TensorFlow, see:
 
-- [User Guide](https://www.tensorflow.org/programmers_guide/eager) ([source](../../docs_src/programmers_guide/eager.md))
+- [User Guide](https://www.tensorflow.org/guide/eager) ([source](../../docs_src/guide/eager.md))
 - Notebook: [Basic Usage](python/examples/notebooks/1_basics.ipynb)
 - Notebook: [Gradients](python/examples/notebooks/2_gradients.ipynb)
 - Notebook: [Importing Data](python/examples/notebooks/3_datasets.ipynb)
diff --git a/tensorflow/contrib/eager/python/examples/notebooks/3_datasets.ipynb b/tensorflow/contrib/eager/python/examples/notebooks/3_datasets.ipynb
index bfcc7feb07..d268cbcd91 100644
--- a/tensorflow/contrib/eager/python/examples/notebooks/3_datasets.ipynb
+++ b/tensorflow/contrib/eager/python/examples/notebooks/3_datasets.ipynb
@@ -9,7 +9,7 @@
       "source": [
         "# Eager Execution Tutorial: Importing Data\n",
         "\n",
-        "This notebook demonstrates the use of the [`tf.data.Dataset` API](https://www.tensorflow.org/programmers_guide/datasets) to build pipelines to feed data to your program. It covers:\n",
+        "This notebook demonstrates the use of the [`tf.data.Dataset` API](https://www.tensorflow.org/guide/datasets) to build pipelines to feed data to your program. It covers:\n",
         "\n",
         "* Creating a `Dataset`.\n",
         "* Iteration over a `Dataset` with eager execution enabled.\n",
@@ -18,7 +18,7 @@
         "\n",
         "If you're familiar with TensorFlow graphs, the API for constructing the `Dataset` object remains exactly the same when eager execution is enabled, but the process of iterating over elements of the dataset is slightly simpler.\n",
         "You can use Python iteration over the `tf.data.Dataset` object and do not need to explicitly create an `tf.data.Iterator` object.\n",
-        "As a result, the discussion on iterators in the [Programmer's Guide](https://www.tensorflow.org/programmers_guide/datasets) is not relevant when eager execution is enabled."
+        "As a result, the discussion on iterators in the [TensorFlow Guide](https://www.tensorflow.org/guide/datasets) is not relevant when eager execution is enabled."
       ]
     },
     {
@@ -63,7 +63,7 @@
       "source": [
         "# Step 1: Create a source `Dataset`\n",
         "\n",
-        "Create a _source_ dataset using one of the factory functions like [`Dataset.from_tensors`](https://www.tensorflow.org/api_docs/python/tf/data/Dataset#from_tensors), [`Dataset.from_tensor_slices`](https://www.tensorflow.org/api_docs/python/tf/data/Dataset#from_tensor_slices) or using objects that read from files like [`TextLineDataset`](https://www.tensorflow.org/api_docs/python/tf/data/TextLineDataset) or [`TFRecordDataset`](https://www.tensorflow.org/api_docs/python/tf/data/TFRecordDataset). See the [Programmer's Guide](https://www.google.com/url?sa=D\u0026q=https%3A%2F%2Fwww.tensorflow.org%2Fprogrammers_guide%2Fdatasets%23reading_input_data) for more information."
+        "Create a _source_ dataset using one of the factory functions like [`Dataset.from_tensors`](https://www.tensorflow.org/api_docs/python/tf/data/Dataset#from_tensors), [`Dataset.from_tensor_slices`](https://www.tensorflow.org/api_docs/python/tf/data/Dataset#from_tensor_slices) or using objects that read from files like [`TextLineDataset`](https://www.tensorflow.org/api_docs/python/tf/data/TextLineDataset) or [`TFRecordDataset`](https://www.tensorflow.org/api_docs/python/tf/data/TFRecordDataset). See the [TensorFlow Guide](https://www.tensorflow.org/guide/datasets#reading_input_data) for more information."
       ]
     },
     {
diff --git a/tensorflow/contrib/eager/python/g3doc/guide.md b/tensorflow/contrib/eager/python/g3doc/guide.md
index 2d2aba6908..23f33d0230 100644
--- a/tensorflow/contrib/eager/python/g3doc/guide.md
+++ b/tensorflow/contrib/eager/python/g3doc/guide.md
@@ -4,8 +4,8 @@ Eager execution is a feature that makes TensorFlow execute operations
 immediately: concrete values are returned, instead of creating a computational
 graph that is executed later.
 
-A user guide is available: https://www.tensorflow.org/programmers_guide/eager
-([source file](../../../../docs_src/programmers_guide/eager.md))
+A user guide is available: https://www.tensorflow.org/guide/eager
+([source file](../../../../docs_src/guide/eager.md))
 
 We welcome feedback through [GitHub issues](https://github.com/tensorflow/tensorflow/labels/comp:eager).
 
diff --git a/tensorflow/contrib/lite/toco/README.md b/tensorflow/contrib/lite/toco/README.md
index 522e260ad2..ee83c7a6e3 100644
--- a/tensorflow/contrib/lite/toco/README.md
+++ b/tensorflow/contrib/lite/toco/README.md
@@ -17,7 +17,7 @@ Usage information is given in these documents:
 Once an application developer has a trained TensorFlow model, TOCO will accept
 that model and generate a TensorFlow Lite
 [FlatBuffer](https://google.github.io/flatbuffers/) file. TOCO currently supports
-[SavedModels](https://www.tensorflow.org/programmers_guide/saved_model#using_savedmodel_with_estimators)
+[SavedModels](https://www.tensorflow.org/guide/saved_model#using_savedmodel_with_estimators)
 and frozen graphs (models generated via
 [freeze_graph.py](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/tools/freeze_graph.py)).
 The TensorFlow Lite FlatBuffer file can be shipped to client devices, generally
diff --git a/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py b/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py
index 7c770912b4..c57acd0a2d 100644
--- a/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py
+++ b/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py
@@ -1103,7 +1103,7 @@ class _InputPipeline(object):
       err_msg = ('Input pipeline contains one or more QueueRunners. '
                  'It could be slow and not scalable. Please consider '
                  'converting your input pipeline to use `tf.data` instead (see '
-                 'https://www.tensorflow.org/programmers_guide/datasets for '
+                 'https://www.tensorflow.org/guide/datasets for '
                  'instructions.')
       if _WRAP_INPUT_FN_INTO_WHILE_LOOP:
         raise RuntimeError(err_msg)
diff --git a/tensorflow/core/protobuf/config.proto b/tensorflow/core/protobuf/config.proto
index 9a48f43a63..d83215d5c2 100644
--- a/tensorflow/core/protobuf/config.proto
+++ b/tensorflow/core/protobuf/config.proto
@@ -147,7 +147,7 @@ message GPUOptions {
 
   // Everything inside experimental is subject to change and is not subject
   // to API stability guarantees in
-  // https://www.tensorflow.org/programmers_guide/version_compat.
+  // https://www.tensorflow.org/guide/version_compat.
   Experimental experimental = 9;
 };
 
@@ -381,7 +381,7 @@ message ConfigProto {
 
   // Everything inside Experimental is subject to change and is not subject
   // to API stability guarantees in
-  // https://www.tensorflow.org/programmers_guide/version_compat.
+  // https://www.tensorflow.org/guide/version_compat.
   message Experimental {
     // Task name for group resolution.
     string collective_group_leader = 1;
@@ -426,7 +426,7 @@ message RunOptions {
 
   // Everything inside Experimental is subject to change and is not subject
   // to API stability guarantees in
-  // https://www.tensorflow.org/programmers_guide/version_compat.
+  // https://www.tensorflow.org/guide/version_compat.
   message Experimental {
     // If non-zero, declares that this graph is going to use collective
     // ops and must synchronize step_ids with any other graph with this
diff --git a/tensorflow/docs_src/api_guides/python/client.md b/tensorflow/docs_src/api_guides/python/client.md
index eef23696db..27fc8610bf 100644
--- a/tensorflow/docs_src/api_guides/python/client.md
+++ b/tensorflow/docs_src/api_guides/python/client.md
@@ -3,7 +3,7 @@
 
 This library contains classes for launching graphs and executing operations.
 
-@{$programmers_guide/low_level_intro$This guide} has examples of how a graph
+@{$guide/low_level_intro$This guide} has examples of how a graph
 is launched in a @{tf.Session}.
 
 ## Session management
diff --git a/tensorflow/docs_src/api_guides/python/input_dataset.md b/tensorflow/docs_src/api_guides/python/input_dataset.md
index a6e2fc48e0..a6612d1bf7 100644
--- a/tensorflow/docs_src/api_guides/python/input_dataset.md
+++ b/tensorflow/docs_src/api_guides/python/input_dataset.md
@@ -2,8 +2,7 @@
 [TOC]
 
 @{tf.data.Dataset} allows you to build complex input pipelines. See the
-@{$datasets$programmer's guide} for an in-depth explanation of how to use this
-API.
+@{$guide/datasets} for an in-depth explanation of how to use this API.
 
 ## Reader classes
 
diff --git a/tensorflow/docs_src/api_guides/python/reading_data.md b/tensorflow/docs_src/api_guides/python/reading_data.md
index 5bbbfd3216..d7d0904ae2 100644
--- a/tensorflow/docs_src/api_guides/python/reading_data.md
+++ b/tensorflow/docs_src/api_guides/python/reading_data.md
@@ -16,8 +16,8 @@ There are four methods of getting data into a TensorFlow program:
 
 ## `tf.data` API
 
-See the @{$datasets$programmer's guide} for an in-depth explanation of
-@{tf.data.Dataset}. The `tf.data` API enables you to extract and preprocess data
+See the @{$guide/datasets} for an in-depth explanation of @{tf.data.Dataset}.
+The `tf.data` API enables you to extract and preprocess data
 from different input/file formats, and apply transformations such as batching,
 shuffling, and mapping functions over the dataset. This is an improved version
 of the old input methods---feeding and `QueueRunner`---which are described
@@ -511,8 +511,8 @@ You can have the train and eval in the same graph in the same process, and share
 their trained variables or layers. See @{$variables$the shared variables tutorial}.
 
 To support the single-graph approach
-@{$programmers_guide/datasets$`tf.data`} also supplies
-@{$programmers_guide/datasets#creating_an_iterator$advanced iterator types} that
+@{$guide/datasets$`tf.data`} also supplies
+@{$guide/datasets#creating_an_iterator$advanced iterator types} that
 that allow the user to change the input pipeline without rebuilding the graph or
 session.
 
diff --git a/tensorflow/docs_src/deploy/distributed.md b/tensorflow/docs_src/deploy/distributed.md
index d7ed6b1deb..8e2c818e39 100644
--- a/tensorflow/docs_src/deploy/distributed.md
+++ b/tensorflow/docs_src/deploy/distributed.md
@@ -2,7 +2,7 @@
 
 This document shows how to create a cluster of TensorFlow servers, and how to
 distribute a computation graph across that cluster. We assume that you are
-familiar with the @{$programmers_guide/low_level_intro$basic concepts} of
+familiar with the @{$guide/low_level_intro$basic concepts} of
 writing low level TensorFlow programs.
 
 ## Hello distributed TensorFlow!
diff --git a/tensorflow/docs_src/extend/architecture.md b/tensorflow/docs_src/extend/architecture.md
index c8f522a03a..84435a57f2 100644
--- a/tensorflow/docs_src/extend/architecture.md
+++ b/tensorflow/docs_src/extend/architecture.md
@@ -7,9 +7,8 @@ learning models and system-level optimizations.
 This document describes the system architecture that makes this
 combination of scale and flexibility possible. It assumes that you have basic familiarity
 with TensorFlow programming concepts such as the computation graph, operations,
-and sessions. See @{$programmers_guide/low_level_intro$this document}
-for an introduction to these topics. Some familiarity
-with @{$distributed$distributed TensorFlow}
+and sessions. See @{$guide/low_level_intro$this document} for an introduction to
+these topics. Some familiarity with @{$distributed$distributed TensorFlow}
 will also be helpful.
 
 This document is for developers who want to extend TensorFlow in some way not
diff --git a/tensorflow/docs_src/get_started/_index.yaml b/tensorflow/docs_src/get_started/_index.yaml
index af255a482d..277fc852fb 100644
--- a/tensorflow/docs_src/get_started/_index.yaml
+++ b/tensorflow/docs_src/get_started/_index.yaml
@@ -74,7 +74,7 @@ landing_page:
               The high-level Keras API provides building blocks to create and
               train deep learning models. Start with these beginner-friendly
               notebook examples, then read the
-              <a href="/programmers_guide/keras">TensorFlow Keras guide</a>.
+              <a href="/guide/keras">TensorFlow Keras guide</a>.
             </p>
             <ol style="padding-left:20px;">
               <li><a href="/get_started/basic_classification">Basic classification</a></li>
@@ -85,7 +85,7 @@ landing_page:
             </ol>
           </div>
           <div class="devsite-landing-row-item-buttons" style="margin-top:0;">
-            <a class="button button-primary tfo-button-primary" href="/programmers_guide/keras">Read the Keras guide</a>
+            <a class="button button-primary tfo-button-primary" href="/guide/keras">Read the Keras guide</a>
           </div>
         </div>
     - classname: tfo-landing-row-item-code-block
@@ -123,7 +123,7 @@ landing_page:
           <div class="devsite-landing-row-item-description-content">
             <p>
               Eager execution provides an imperative, define-by-run interface for advanced operations. Write custom layers, forward passes, and training loops with auto‑differentiation. Start with
-              these notebooks, then read the <a href="/programmers_guide/eager">eager execution guide</a>.
+              these notebooks, then read the <a href="/guide/eager">eager execution guide</a>.
             </p>
             <ol style="padding-left:20px;">
               <li>
@@ -165,7 +165,7 @@ landing_page:
             </ol>
           </div>
           <div class="devsite-landing-row-item-buttons">
-            <a class="button button-primary tfo-button-primary" href="/programmers_guide/eager">Read the eager execution guide</a>
+            <a class="button button-primary tfo-button-primary" href="/guide/eager">Read the eager execution guide</a>
           </div>
         </div>
     - custom_html: >
@@ -177,7 +177,7 @@ landing_page:
             <p>
               Estimators can train large models on multiple machines in a
               production environment. Try the examples below and read the
-              <a href="/programmers_guide/estimators">Estimators guide</a>.
+              <a href="/guide/estimators">Estimators guide</a>.
             </p>
             <ol style="padding-left: 20px;">
               <li><a href="/tutorials/text_classification_with_tf_hub">How to build a simple text classifier with TF-Hub</a></li>
@@ -186,7 +186,7 @@ landing_page:
             </ol>
           </div>
           <div class="devsite-landing-row-item-buttons">
-            <a class="button button-primary tfo-button-primary" href="/programmers_guide/estimators">Read the Estimators guide</a>
+            <a class="button button-primary tfo-button-primary" href="/guide/estimators">Read the Estimators guide</a>
           </div>
         </div>
 
diff --git a/tensorflow/docs_src/get_started/next_steps.md b/tensorflow/docs_src/get_started/next_steps.md
index 79c0ef3346..6318a39c6c 100644
--- a/tensorflow/docs_src/get_started/next_steps.md
+++ b/tensorflow/docs_src/get_started/next_steps.md
@@ -2,9 +2,9 @@
 
 ## Learn more about TensorFlow
 
-* The [TensorFlow Guide](/programmers_guide) includes usage guides for the
+* The [TensorFlow Guide](/guide) includes usage guides for the
   high-level APIs, as well as advanced TensorFlow operations.
-* [Premade Estimators](/programmers_guide/premade_estimators) are designed to
+* [Premade Estimators](/guide/premade_estimators) are designed to
   get results out of the box. Use TensorFlow without building your own models.
 * [TensorFlow.js](https://js.tensorflow.org/) allows web developers to train and
   deploy ML models in the browser and using Node.js.
diff --git a/tensorflow/docs_src/programmers_guide/checkpoints.md b/tensorflow/docs_src/guide/checkpoints.md
index 8dfd91e3c8..dfb2626b86 100644
--- a/tensorflow/docs_src/programmers_guide/checkpoints.md
+++ b/tensorflow/docs_src/guide/checkpoints.md
@@ -8,9 +8,8 @@ Estimators. TensorFlow provides two model formats:
 *   SavedModel, which is a format independent of the code that created
     the model.
 
-This document focuses on checkpoints. For details on SavedModel, see the
-@{$saved_model$Saving and Restoring} chapter of the
-*TensorFlow Programmer's Guide*.
+This document focuses on checkpoints. For details on `SavedModel`, see the
+@{$saved_model$Saving and Restoring} guide.
 
 
 ## Sample code
@@ -232,8 +231,7 @@ This separation will keep your checkpoints recoverable.
 Checkpoints provide an easy automatic mechanism for saving and restoring
 models created by Estimators.
 
-See the @{$saved_model$Saving and Restoring}
-chapter of the *TensorFlow Programmer's Guide* for details on:
+See the @{$saved_model$Saving and Restoring} guide for details about:
 
 *   Saving and restoring models using low-level TensorFlow APIs.
 *   Exporting and importing models in the SavedModel format, which is a
diff --git a/tensorflow/docs_src/programmers_guide/custom_estimators.md b/tensorflow/docs_src/guide/custom_estimators.md
index fb20b35c12..fb20b35c12 100644
--- a/tensorflow/docs_src/programmers_guide/custom_estimators.md
+++ b/tensorflow/docs_src/guide/custom_estimators.md
diff --git a/tensorflow/docs_src/programmers_guide/datasets.md b/tensorflow/docs_src/guide/datasets.md
index 8b69860a68..8b69860a68 100644
--- a/tensorflow/docs_src/programmers_guide/datasets.md
+++ b/tensorflow/docs_src/guide/datasets.md
diff --git a/tensorflow/docs_src/programmers_guide/datasets_for_estimators.md b/tensorflow/docs_src/guide/datasets_for_estimators.md
index 345a31b985..b04af78cd8 100644
--- a/tensorflow/docs_src/programmers_guide/datasets_for_estimators.md
+++ b/tensorflow/docs_src/guide/datasets_for_estimators.md
@@ -91,8 +91,8 @@ print(mnist_ds)
 ```
 
 This will print the following line, showing the
-@{$programmers_guide/tensors#shapes$shapes} and
-@{$programmers_guide/tensors#data_types$types} of the items in
+@{$guide/tensors#shapes$shapes} and
+@{$guide/tensors#data_types$types} of the items in
 the dataset. Note that a `Dataset` does not know how many items it contains.
 
 ``` None
@@ -128,7 +128,7 @@ print(dataset)
 
 Here we see that when a `Dataset` contains structured elements, the `shapes`
 and `types` of the `Dataset` take on the same structure. This dataset contains
-dictionaries of @{$programmers_guide/tensors#rank$scalars}, all of type
+dictionaries of @{$guide/tensors#rank$scalars}, all of type
 `tf.float64`.
 
 The first line of the iris `train_input_fn` uses the same functionality, but
@@ -382,6 +382,6 @@ Estimator. Consider the following documents next:
 * The @{$low_level_intro#datasets$Low Level Introduction}, which demonstrates
   how to experiment directly with `tf.data.Datasets` using TensorFlow's low
   level APIs.
-* @{$programmers_guide/datasets} which goes into great detail about additional
+* @{$guide/datasets} which goes into great detail about additional
   functionality of `Datasets`.
 
diff --git a/tensorflow/docs_src/programmers_guide/debugger.md b/tensorflow/docs_src/guide/debugger.md
index 6bd941886d..6bd941886d 100644
--- a/tensorflow/docs_src/programmers_guide/debugger.md
+++ b/tensorflow/docs_src/guide/debugger.md
diff --git a/tensorflow/docs_src/programmers_guide/eager.md b/tensorflow/docs_src/guide/eager.md
index 00d02b4455..00d02b4455 100644
--- a/tensorflow/docs_src/programmers_guide/eager.md
+++ b/tensorflow/docs_src/guide/eager.md
diff --git a/tensorflow/docs_src/programmers_guide/embedding.md b/tensorflow/docs_src/guide/embedding.md
index 8a98367dfb..8a98367dfb 100644
--- a/tensorflow/docs_src/programmers_guide/embedding.md
+++ b/tensorflow/docs_src/guide/embedding.md
diff --git a/tensorflow/docs_src/programmers_guide/estimators.md b/tensorflow/docs_src/guide/estimators.md
index b13b47184d..78b30c3040 100644
--- a/tensorflow/docs_src/programmers_guide/estimators.md
+++ b/tensorflow/docs_src/guide/estimators.md
@@ -81,7 +81,7 @@ of the following four steps:
            ...  # manipulate dataset, extracting the feature dict and the label
            return feature_dict, label
 
-    (See @{$programmers_guide/datasets} for full details.)
+    (See @{$guide/datasets} for full details.)
 
 2.  **Define the feature columns.** Each @{tf.feature_column}
     identifies a feature name, its type, and any input pre-processing.
diff --git a/tensorflow/docs_src/programmers_guide/faq.md b/tensorflow/docs_src/guide/faq.md
index b6291a9ffa..b6291a9ffa 100644
--- a/tensorflow/docs_src/programmers_guide/faq.md
+++ b/tensorflow/docs_src/guide/faq.md
diff --git a/tensorflow/docs_src/programmers_guide/feature_columns.md b/tensorflow/docs_src/guide/feature_columns.md
index 90f5c53a17..1013ec910c 100644
--- a/tensorflow/docs_src/programmers_guide/feature_columns.md
+++ b/tensorflow/docs_src/guide/feature_columns.md
@@ -534,7 +534,7 @@ embedding_column = tf.feature_column.embedding_column(
     dimension=embedding_dimensions)
 ```
 
-@{$programmers_guide/embedding$Embeddings} is a significant topic within machine
+@{$guide/embedding$Embeddings} is a significant topic within machine
 learning. This information was just to get you started using them as feature
 columns.
 
diff --git a/tensorflow/docs_src/programmers_guide/graph_viz.md b/tensorflow/docs_src/guide/graph_viz.md
index f581ae56da..f581ae56da 100644
--- a/tensorflow/docs_src/programmers_guide/graph_viz.md
+++ b/tensorflow/docs_src/guide/graph_viz.md
diff --git a/tensorflow/docs_src/programmers_guide/graphs.md b/tensorflow/docs_src/guide/graphs.md
index f0dd8def17..e6246ef148 100644
--- a/tensorflow/docs_src/programmers_guide/graphs.md
+++ b/tensorflow/docs_src/guide/graphs.md
@@ -93,7 +93,7 @@ to all API functions in the same context.  For example:
   stored value. The @{tf.Variable} object also has methods such as
   @{tf.Variable.assign$`assign`} and @{tf.Variable.assign_add$`assign_add`} that
   create @{tf.Operation} objects that, when executed, update the stored value.
-  (See @{$programmers_guide/variables} for more information about variables.)
+  (See @{$guide/variables} for more information about variables.)
 
 * Calling @{tf.train.Optimizer.minimize} will add operations and tensors to the
   default graph that calculates gradients, and return a @{tf.Operation} that,
diff --git a/tensorflow/docs_src/programmers_guide/index.md b/tensorflow/docs_src/guide/index.md
index 9c58a3b45e..eefdb9ceae 100644
--- a/tensorflow/docs_src/programmers_guide/index.md
+++ b/tensorflow/docs_src/guide/index.md
@@ -1,17 +1,17 @@
-# Programmer's Guide
+# TensorFlow Guide
 
 The documents in this unit dive into the details of how TensorFlow
 works. The units are as follows:
 
 ## High Level APIs
 
-  * @{$programmers_guide/keras}, TensorFlow's high-level API for building and
+  * @{$guide/keras}, TensorFlow's high-level API for building and
     training deep learning models.
-  * @{$programmers_guide/eager}, an API for writing TensorFlow code
+  * @{$guide/eager}, an API for writing TensorFlow code
     imperatively, like you would use Numpy.
-  * @{$programmers_guide/estimators}, a high-level API that provides
+  * @{$guide/estimators}, a high-level API that provides
     fully-packaged models ready for large-scale training and production.
-  * @{$programmers_guide/datasets}, easy input pipelines to bring your data into
+  * @{$guide/datasets}, easy input pipelines to bring your data into
     your TensorFlow program.
 
 ## Estimators
@@ -34,13 +34,13 @@ works. The units are as follows:
 
 ## Low Level APIs
 
-  * @{$programmers_guide/low_level_intro}, which introduces the
+  * @{$guide/low_level_intro}, which introduces the
     basics of how you can use TensorFlow outside of the high Level APIs.
-  * @{$programmers_guide/tensors}, which explains how to create,
+  * @{$guide/tensors}, which explains how to create,
     manipulate, and access Tensors--the fundamental object in TensorFlow.
-  * @{$programmers_guide/variables}, which details how
+  * @{$guide/variables}, which details how
     to represent shared, persistent state in your program.
-  * @{$programmers_guide/graphs}, which explains:
+  * @{$guide/graphs}, which explains:
       * dataflow graphs, which are TensorFlow's representation of computations
         as dependencies between operations.
       * sessions, which are TensorFlow's mechanism for running dataflow graphs
@@ -50,19 +50,19 @@ works. The units are as follows:
     such as Estimators or Keras, the high-level API creates and manages
     graphs and sessions for you, but understanding graphs and sessions
     can still be helpful.
-  * @{$programmers_guide/saved_model}, which
+  * @{$guide/saved_model}, which
     explains how to save and restore variables and models.
 
 ## ML Concepts
 
-  * @{$programmers_guide/embedding}, which introduces the concept
+  * @{$guide/embedding}, which introduces the concept
     of embeddings, provides a simple example of training an embedding in
     TensorFlow, and explains how to view embeddings with the TensorBoard
     Embedding Projector.
 
 ## Debugging
 
-  * @{$programmers_guide/debugger}, which
+  * @{$guide/debugger}, which
     explains how to use the TensorFlow debugger (tfdbg).
 
 ## TensorBoard
@@ -70,17 +70,17 @@ works. The units are as follows:
 TensorBoard is a utility to visualize different aspects of machine learning.
 The following guides explain how to use TensorBoard:
 
-  * @{$programmers_guide/summaries_and_tensorboard},
+  * @{$guide/summaries_and_tensorboard},
     which introduces TensorBoard.
-  * @{$programmers_guide/graph_viz}, which
+  * @{$guide/graph_viz}, which
     explains how to visualize the computational graph.
-  * @{$programmers_guide/tensorboard_histograms} which demonstrates the how to
+  * @{$guide/tensorboard_histograms} which demonstrates the how to
     use TensorBoard's histogram dashboard.
 
 
 ## Misc
 
-  * @{$programmers_guide/version_compat},
+  * @{$guide/version_compat},
     which explains backward compatibility guarantees and non-guarantees.
-  * @{$programmers_guide/faq}, which contains frequently asked
+  * @{$guide/faq}, which contains frequently asked
     questions about TensorFlow.
diff --git a/tensorflow/docs_src/programmers_guide/keras.md b/tensorflow/docs_src/guide/keras.md
index c6aca7ebf4..83172dab7f 100644
--- a/tensorflow/docs_src/programmers_guide/keras.md
+++ b/tensorflow/docs_src/guide/keras.md
@@ -19,7 +19,7 @@ fast prototyping, advanced research, and production, with three key advantages:
 [Keras API specification](https://keras.io){:.external}. This is a high-level
 API to build and train models that includes first-class support for
 TensorFlow-specific functionality, such as [eager execution](#eager_execution),
-`tf.data` pipelines, and [Estimators](/programmers_guide/estimators).
+`tf.data` pipelines, and [Estimators](./estimators.md).
 `tf.keras` makes TensorFlow easier to use without sacrificing flexibility and
 performance.
 
@@ -35,8 +35,8 @@ from tensorflow import keras
 * The `tf.keras` version in the latest TensorFlow release might not be the same
   as the latest `keras` version from PyPI. Check `tf.keras.__version__`.
 * When [saving a model's weights](#weights_only), `tf.keras` defaults to the
-  [checkpoint format](/get_started/checkpoints). Pass `save_format='h5'` to use
-  HDF5.
+  [checkpoint format](../get_started/checkpoints.md). Pass `save_format='h5'` to
+  use HDF5.
 
 ## Build a simple model
 
@@ -179,7 +179,7 @@ model.fit(data, labels, epochs=10, batch_size=32,
 
 ### Input tf.data datasets
 
-Use the [Datasets API](/programmers_guide/datasets) to scale to large datasets
+Use the [Datasets API](./datasets.md) to scale to large datasets
 or multi-device training. Pass a `tf.data.Dataset` instance to the `fit`
 method:
 
@@ -285,7 +285,7 @@ your own forward pass. Create layers in the `__init__` method and set them as
 attributes of the class instance. Define the forward pass in the `call` method.
 
 Model subclassing is particularly useful when
-[eager execution](/programmers_guide/eager) is enabled since the forward pass
+[eager execution](./eager.md) is enabled since the forward pass
 can be written imperatively.
 
 Key Point: Use the right API for the job. While model subclassing offers
@@ -410,7 +410,7 @@ during training. You can write your own custom callback, or use the built-in
 * `tf.keras.callbacks.EarlyStopping`: Interrupt training when validation
   performance has stopped improving.
 * `tf.keras.callbacks.TensorBoard`: Monitor the model's behavior using
-  [TensorBoard](/programmers_guide/summaries_and_tensorboard).
+  [TensorBoard](./summaries_and_tensorboard.md).
 
 To use a `tf.keras.callbacks.Callback`, pass it to the model's `fit` method:
 
@@ -442,8 +442,8 @@ model.load_weights('my_model')
 ```
 
 By default, this saves the model's weights in the
-[TensorFlow checkpoint](/get_started/checkpoints) file format. Weights can also
-be saved to the Keras HDF5 format (the default for the multi-backend
+[TensorFlow checkpoint](../get_started/checkpoints.md) file format. Weights can
+also be saved to the Keras HDF5 format (the default for the multi-backend
 implementation of Keras):
 
 ```python
@@ -509,7 +509,7 @@ model = keras.models.load_model('my_model.h5')
 
 ## Eager execution
 
-[Eager execution](/programmers_guide/eager) is an imperative programming
+[Eager execution](./eager.md) is an imperative programming
 environment that evaluates operations immediately. This is not required for
 Keras, but is supported by `tf.keras` and useful for inspecting your program and
 debugging.
@@ -520,7 +520,7 @@ especially benefits *model subclassing* and building *custom layers*—the APIs
 that require you to write the forward pass as code (instead of the APIs that
 create models by assembling existing layers).
 
-See the [eager execution guide](/programmers_guide/eager#build_a_model) for
+See the [eager execution guide](./eager.md#build_a_model) for
 examples of using Keras models with custom training loops and `tf.GradientTape`.
 
 
@@ -528,14 +528,14 @@ examples of using Keras models with custom training loops and `tf.GradientTape`.
 
 ### Estimators
 
-The [Estimators](/programmers_guide/estimators) API is used for training models
+The [Estimators](./estimators.md) API is used for training models
 for distributed environments. This targets industry use cases such as
 distributed training on large datasets that can export a model for production.
 
 A `tf.keras.Model` can be trained with the `tf.estimator` API by converting the
 model to an `tf.estimator.Estimator` object with
 `tf.keras.estimator.model_to_estimator`. See
-[Creating Estimators from Keras models](/programmers_guide/estimators#creating_estimators_from_keras_models).
+[Creating Estimators from Keras models](./estimators.md#creating_estimators_from_keras_models).
 
 ```python
 model = keras.Sequential([layers.Dense(10,activation='softmax'),
@@ -548,8 +548,8 @@ model.compile(optimizer=tf.train.RMSPropOptimizer(0.001),
 estimator = keras.estimator.model_to_estimator(model)
 ```
 
-Note: Enable [eager execution](/programmers_guide/eager) for debugging
-[Estimator input functions](/programmers_guide/premade_estimators#create_input_functions)
+Note: Enable [eager execution](./eager.md) for debugging
+[Estimator input functions](./premade_estimators.md#create_input_functions)
 and inspecting data.
 
 ### Multiple GPUs
diff --git a/tensorflow/docs_src/programmers_guide/leftnav_files b/tensorflow/docs_src/guide/leftnav_files
index 357a2a1cb9..357a2a1cb9 100644
--- a/tensorflow/docs_src/programmers_guide/leftnav_files
+++ b/tensorflow/docs_src/guide/leftnav_files
diff --git a/tensorflow/docs_src/programmers_guide/low_level_intro.md b/tensorflow/docs_src/guide/low_level_intro.md
index 478e2bb70b..665a5568b4 100644
--- a/tensorflow/docs_src/programmers_guide/low_level_intro.md
+++ b/tensorflow/docs_src/guide/low_level_intro.md
@@ -303,7 +303,7 @@ while True:
     break
 ```
 
-For more details on Datasets and Iterators see: @{$programmers_guide/datasets}.
+For more details on Datasets and Iterators see: @{$guide/datasets}.
 
 ## Layers
 
diff --git a/tensorflow/docs_src/programmers_guide/premade_estimators.md b/tensorflow/docs_src/guide/premade_estimators.md
index 02e2caf64b..3e910c1fe2 100644
--- a/tensorflow/docs_src/programmers_guide/premade_estimators.md
+++ b/tensorflow/docs_src/guide/premade_estimators.md
@@ -78,10 +78,10 @@ provides a programming stack consisting of multiple API layers:
 
 We strongly recommend writing TensorFlow programs with the following APIs:
 
-* @{$programmers_guide/estimators$Estimators}, which represent a complete model.
+* @{$guide/estimators$Estimators}, which represent a complete model.
   The Estimator API provides methods to train the model, to judge the model's
   accuracy, and to generate predictions.
-* @{$programmers_guide/datasets_for_estimators}, which build a data input
+* @{$guide/datasets_for_estimators}, which build a data input
   pipeline. The Dataset API has methods to load and manipulate data, and feed
   it into your model. The Dataset API meshes well with the Estimators API.
 
@@ -173,7 +173,7 @@ example is an Iris Versicolor.
 An Estimator is TensorFlow's high-level representation of a complete model. It
 handles the details of initialization, logging, saving and restoring, and many
 other features so you can concentrate on your model. For more details see
-@{$programmers_guide/estimators}.
+@{$guide/estimators}.
 
 An Estimator is any class derived from @{tf.estimator.Estimator}. TensorFlow
 provides a collection of
@@ -424,7 +424,7 @@ Now that you've gotten started writing TensorFlow programs, consider the
 following material:
 
 * @{$checkpoints$Checkpoints} to learn how to save and restore models.
-* @{$programmers_guide/datasets_for_estimators} to learn more about importing
+* @{$guide/datasets_for_estimators} to learn more about importing
   data into your model.
 * @{$custom_estimators$Creating Custom Estimators} to learn how to
   write your own Estimator, customized for a particular problem.
diff --git a/tensorflow/docs_src/programmers_guide/saved_model.md b/tensorflow/docs_src/guide/saved_model.md
index c6ef87c54a..27ef7bb0da 100644
--- a/tensorflow/docs_src/programmers_guide/saved_model.md
+++ b/tensorflow/docs_src/guide/saved_model.md
@@ -3,7 +3,7 @@
 The @{tf.train.Saver} class provides methods to save and restore models. The
 @{tf.saved_model.simple_save} function is an easy way to build a
 @{tf.saved_model$saved model} suitable for serving.
-[Estimators](@{$programmers_guide/estimators}) automatically save and restore
+[Estimators](@{$guide/estimators}) automatically save and restore
 variables in the `model_dir`.
 
 ## Save and restore variables
@@ -299,7 +299,7 @@ following:
    added attributes with defaults don't cause older model consumers to fail
    loading models regenerated with newer training binaries.
 
-See [compatibility guidance](https://www.tensorflow.org/programmers_guide/version_compat)
+See [compatibility guidance](./version_compat.md)
 for more information.
 
 ### Loading a SavedModel in Python
diff --git a/tensorflow/docs_src/programmers_guide/summaries_and_tensorboard.md b/tensorflow/docs_src/guide/summaries_and_tensorboard.md
index fadfa03e78..fadfa03e78 100644
--- a/tensorflow/docs_src/programmers_guide/summaries_and_tensorboard.md
+++ b/tensorflow/docs_src/guide/summaries_and_tensorboard.md
diff --git a/tensorflow/docs_src/programmers_guide/tensorboard_histograms.md b/tensorflow/docs_src/guide/tensorboard_histograms.md
index 918deda190..918deda190 100644
--- a/tensorflow/docs_src/programmers_guide/tensorboard_histograms.md
+++ b/tensorflow/docs_src/guide/tensorboard_histograms.md
diff --git a/tensorflow/docs_src/programmers_guide/tensors.md b/tensorflow/docs_src/guide/tensors.md
index 1248c3cabe..7227260f1a 100644
--- a/tensorflow/docs_src/programmers_guide/tensors.md
+++ b/tensorflow/docs_src/guide/tensors.md
@@ -26,7 +26,7 @@ some cases it's only possible to find the shape of a tensor at graph execution
 time.
 
 Some types of tensors are special, and these will be covered in other
-units of the Programmer's guide. The main ones are:
+units of the TensorFlow guide. The main ones are:
 
   * `tf.Variable`
   * `tf.constant`
@@ -230,7 +230,7 @@ yet_another = tf.reshape(matrixAlt, [13, 2, -1])  # ERROR!
 ## Data types
 
 In addition to dimensionality, Tensors have a data type. Refer to the
-`tf.DataType` page in the programmer's guide for a full list of the data types.
+`tf.DType` page for a complete list of the data types.
 
 It is not possible to have a `tf.Tensor` with more than one data type. It is
 possible, however, to serialize arbitrary data structures as `string`s and store
diff --git a/tensorflow/docs_src/programmers_guide/using_gpu.md b/tensorflow/docs_src/guide/using_gpu.md
index c429ca4750..c429ca4750 100644
--- a/tensorflow/docs_src/programmers_guide/using_gpu.md
+++ b/tensorflow/docs_src/guide/using_gpu.md
diff --git a/tensorflow/docs_src/programmers_guide/using_tpu.md b/tensorflow/docs_src/guide/using_tpu.md
index 44aabf0557..41d80d9d60 100644
--- a/tensorflow/docs_src/programmers_guide/using_tpu.md
+++ b/tensorflow/docs_src/guide/using_tpu.md
@@ -171,7 +171,7 @@ This section details the changes you must make to the model function
 During regular usage TensorFlow attempts to determine the shapes of each
 `tf.Tensor` during graph construction. During execution any unknown shape
 dimensions are determined dynamically,
-see @{$programmers_guide/tensors#shape$Tensor Shapes} for more details.
+see @{$guide/tensors#shape$Tensor Shapes} for more details.
 
 To run on Cloud TPUs TensorFlow models are compiled using @{$xla$XLA}.
 XLA uses a similar system for determining shapes at compile time. XLA requires
@@ -195,7 +195,7 @@ TPU.
 
 Build your evaluation metrics dictionary in a stand-alone `metric_fn`.
 
-<!-- TODO(markdaoust) link to programmers_guide/metrics when it exists -->
+<!-- TODO(markdaoust) link to guide/metrics when it exists -->
 
 Evaluation metrics are an essential part of training a model. These are fully
 supported on Cloud TPUs, but with a slightly different syntax.
diff --git a/tensorflow/docs_src/programmers_guide/variables.md b/tensorflow/docs_src/guide/variables.md
index cd8c4b5b9a..cd8c4b5b9a 100644
--- a/tensorflow/docs_src/programmers_guide/variables.md
+++ b/tensorflow/docs_src/guide/variables.md
diff --git a/tensorflow/docs_src/programmers_guide/version_compat.md b/tensorflow/docs_src/guide/version_compat.md
index 72e427c5f8..72e427c5f8 100644
--- a/tensorflow/docs_src/programmers_guide/version_compat.md
+++ b/tensorflow/docs_src/guide/version_compat.md
diff --git a/tensorflow/docs_src/install/install_go.md b/tensorflow/docs_src/install/install_go.md
index 1c03dd223e..5451e1b319 100644
--- a/tensorflow/docs_src/install/install_go.md
+++ b/tensorflow/docs_src/install/install_go.md
@@ -6,7 +6,7 @@ a Go application. This guide explains how to install and set up the
 [TensorFlow Go package](https://godoc.org/github.com/tensorflow/tensorflow/tensorflow/go).
 
 Warning: The TensorFlow Go API is *not* covered by the TensorFlow
-[API stability guarantees](https://www.tensorflow.org/programmers_guide/version_semantics).
+[API stability guarantees](../guide/version_semantics.md).
 
 
 ## Supported Platforms
diff --git a/tensorflow/docs_src/install/install_java.md b/tensorflow/docs_src/install/install_java.md
index c73e2f4281..ad3544b595 100644
--- a/tensorflow/docs_src/install/install_java.md
+++ b/tensorflow/docs_src/install/install_java.md
@@ -7,7 +7,7 @@ Java application. This guide explains how to install
 and use it in a Java application.
 
 Warning: The TensorFlow Java API is *not* covered by the TensorFlow
-[API stability guarantees](https://www.tensorflow.org/programmers_guide/version_semantics).
+[API stability guarantees](../guide/version_semantics.md).
 
 
 ## Supported Platforms
diff --git a/tensorflow/docs_src/tutorials/deep_cnn.md b/tensorflow/docs_src/tutorials/deep_cnn.md
index 6a4c9a9b07..44a32d9d1d 100644
--- a/tensorflow/docs_src/tutorials/deep_cnn.md
+++ b/tensorflow/docs_src/tutorials/deep_cnn.md
@@ -268,7 +268,7 @@ in `cifar10_input.py`.
 
 `cifar10_train.py` periodically @{tf.train.Saver$saves}
 all model parameters in
-@{$programmers_guide/saved_model$checkpoint files}
+@{$guide/saved_model$checkpoint files}
 but it does *not* evaluate the model. The checkpoint file
 will be used by `cifar10_eval.py` to measure the predictive
 performance (see [Evaluating a Model](#evaluating-a-model) below).
diff --git a/tensorflow/docs_src/tutorials/layers.md b/tensorflow/docs_src/tutorials/layers.md
index 0f17899dae..212e337637 100644
--- a/tensorflow/docs_src/tutorials/layers.md
+++ b/tensorflow/docs_src/tutorials/layers.md
@@ -627,7 +627,7 @@ operation earlier when we generated the probabilities in `cnn_model_fn`.
 > argument, TensorFlow will assign a default name. A couple easy ways to
 > discover the names applied to operations are to visualize your graph on
 > @{$graph_viz$TensorBoard}) or to enable the
-> @{$programmers_guide/debugger$TensorFlow Debugger (tfdbg)}.
+> @{$guide/debugger$TensorFlow Debugger (tfdbg)}.
 
 Next, we create the `LoggingTensorHook`, passing `tensors_to_log` to the
 `tensors` argument. We set `every_n_iter=50`, which specifies that probabilities
diff --git a/tensorflow/examples/how_tos/reading_data/fully_connected_reader.py b/tensorflow/examples/how_tos/reading_data/fully_connected_reader.py
index 307eede5c0..7402247448 100644
--- a/tensorflow/examples/how_tos/reading_data/fully_connected_reader.py
+++ b/tensorflow/examples/how_tos/reading_data/fully_connected_reader.py
@@ -17,7 +17,7 @@
 This version is like fully_connected_feed.py but uses data converted
 to a TFRecords file containing tf.train.Example protocol buffers.
 See:
-https://www.tensorflow.org/programmers_guide/reading_data#reading_from_files
+https://www.tensorflow.org/guide/reading_data#reading_from_files
 for context.
 
 YOU MUST run convert_to_records before running this (but you only need to
diff --git a/tensorflow/java/README.md b/tensorflow/java/README.md
index 2f1ce253b2..c7382ff231 100644
--- a/tensorflow/java/README.md
+++ b/tensorflow/java/README.md
@@ -1,7 +1,7 @@
 # TensorFlow for Java
 
 > *WARNING*: The TensorFlow Java API is not currently covered by the TensorFlow
-> [API stability guarantees](https://www.tensorflow.org/programmers_guide/version_semantics).
+> [API stability guarantees](https://www.tensorflow.org/guide/version_semantics).
 >
 > For using TensorFlow on Android refer instead to
 > [contrib/android](https://www.tensorflow.org/code/tensorflow/contrib/android),
@@ -23,8 +23,7 @@ native libraries will need to be built from source.
 
 2.  Setup the environment to build TensorFlow from source code
     ([Linux](https://www.tensorflow.org/install/install_sources#PrepareLinux)
-    or [Mac OS
-    X](https://www.tensorflow.org/install/install_sources#PrepareMac)).
+    or [macOS](https://www.tensorflow.org/install/install_sources#PrepareMac)).
     If you'd like to skip reading those details and do not care about GPU
     support, try the following:
 
diff --git a/tensorflow/java/src/main/java/org/tensorflow/package-info.java b/tensorflow/java/src/main/java/org/tensorflow/package-info.java
index 521c5c610c..f353ee3145 100644
--- a/tensorflow/java/src/main/java/org/tensorflow/package-info.java
+++ b/tensorflow/java/src/main/java/org/tensorflow/package-info.java
@@ -17,7 +17,7 @@ limitations under the License.
  * Defines classes to build, save, load and execute TensorFlow models.
  *
  * <p><b>WARNING</b>: The API is currently experimental and is not covered by TensorFlow <a
- * href="https://www.tensorflow.org/programmers_guide/version_semantics">API stability
+ * href="https://www.tensorflow.org/guide/version_semantics">API stability
  * guarantees</a>. See <a
  * href="https://www.tensorflow.org/code/tensorflow/java/README.md">README.md</a> for installation
  * instructions.
diff --git a/tensorflow/python/data/__init__.py b/tensorflow/python/data/__init__.py
index 7efe0948e7..3b9bf2469e 100644
--- a/tensorflow/python/data/__init__.py
+++ b/tensorflow/python/data/__init__.py
@@ -14,7 +14,7 @@
 # ==============================================================================
 """`tf.data.Dataset` API for input pipelines.
 
-See the @{$datasets$Importing Data} Programmer's Guide for an overview.
+See @{$guide/datasets$Importing Data} for an overview.
 """
 
 from __future__ import absolute_import
diff --git a/tensorflow/python/data/ops/dataset_ops.py b/tensorflow/python/data/ops/dataset_ops.py
index 6f9b12b123..0e020d86d0 100644
--- a/tensorflow/python/data/ops/dataset_ops.py
+++ b/tensorflow/python/data/ops/dataset_ops.py
@@ -212,6 +212,13 @@ class Dataset(object):
   def from_tensors(tensors):
     """Creates a `Dataset` with a single element, comprising the given tensors.
 
+    Note that if `tensors` contains a NumPy array, and eager execution is not
+    enabled, the values will be embedded in the graph as one or more
+    @{tf.constant} operations. For large datasets (> 1 GB), this can waste
+    memory and run into byte limits of graph serialization.  If tensors contains
+    one or more large NumPy arrays, consider the alternative described in
+    @{$guide/datasets#consuming_numpy_arrays$this guide}.
+
     Args:
       tensors: A nested structure of tensors.
 
@@ -224,6 +231,13 @@ class Dataset(object):
   def from_tensor_slices(tensors):
     """Creates a `Dataset` whose elements are slices of the given tensors.
 
+    Note that if `tensors` contains a NumPy array, and eager execution is not
+    enabled, the values will be embedded in the graph as one or more
+    @{tf.constant} operations. For large datasets (> 1 GB), this can waste
+    memory and run into byte limits of graph serialization.  If tensors contains
+    one or more large NumPy arrays, consider the alternative described in
+    @{$guide/datasets#consuming_numpy_arrays$this guide}.
+
     Args:
       tensors: A nested structure of tensors, each having the same size in the
         0th dimension.
diff --git a/tensorflow/python/debug/BUILD b/tensorflow/python/debug/BUILD
index 09062abd74..2d261f9be7 100644
--- a/tensorflow/python/debug/BUILD
+++ b/tensorflow/python/debug/BUILD
@@ -5,7 +5,7 @@
 #
 # ":debug_py": Public Python methods and classes of tfdbg.
 #   For API documentation, see https://www.tensorflow.org/api_docs/python/tfdbg
-#   For a user interface walkthrough, see https://www.tensorflow.org/programmers_guide/debugger
+#   For a user interface walkthrough, see https://www.tensorflow.org/guide/debugger
 # ":grpc_debug_server": Server interface for grpc:// debug URLs.
 
 package(
diff --git a/tensorflow/python/debug/README.md b/tensorflow/python/debug/README.md
index 269bbb19bd..9c16af4d79 100644
--- a/tensorflow/python/debug/README.md
+++ b/tensorflow/python/debug/README.md
@@ -28,7 +28,7 @@ models:
 
 * Easy access through session wrappers
 * Easy integration with common high-level APIs, such as
-  [TensorFlow Estimators](https://www.tensorflow.org/programmers_guide/estimators) and
+  [TensorFlow Estimators](https://www.tensorflow.org/guide/estimators) and
   [Keras](https://keras.io/)
 * Inspection of runtime tensor values and node connections
 * Conditional breaking after runs that generate tensors satisfying given
@@ -43,7 +43,7 @@ models:
 
 ## How to use TFDBG?
 
-* For a walkthrough of TFDBG command-line interface, see https://www.tensorflow.org/programmers_guide/debugger.
+* For a walkthrough of TFDBG command-line interface, see https://www.tensorflow.org/guide/debugger.
 * For information on the web GUI of TFDBG (TensorBoard Debugger Plugin), see
   [this README](https://github.com/tensorflow/tensorboard/blob/master/tensorboard/plugins/debugger/README.md).
 * For programmatic use of the API of TFDBG, see https://www.tensorflow.org/api_docs/python/tfdbg.
diff --git a/tensorflow/python/debug/examples/README.md b/tensorflow/python/debug/examples/README.md
index cb4d484092..3b431e04dc 100644
--- a/tensorflow/python/debug/examples/README.md
+++ b/tensorflow/python/debug/examples/README.md
@@ -3,7 +3,7 @@ Hi, there!
 The documentation of **TensorFlow Debugger (tfdbg)** has moved.
 
 See the source version at
-[this new location](../../../docs_src/programmers_guide/debugger.md).
+[this new location](../../../docs_src/guide/debugger.md).
 
 See the public website version at
-[https://www.tensorflow.org/programmers_guide/debugger](https://www.tensorflow.org/programmers_guide/debugger).
+[https://www.tensorflow.org/guide/debugger](https://www.tensorflow.org/guide/debugger).
diff --git a/tensorflow/python/estimator/keras.py b/tensorflow/python/estimator/keras.py
index 2f439f765e..6856b8b5a9 100644
--- a/tensorflow/python/estimator/keras.py
+++ b/tensorflow/python/estimator/keras.py
@@ -455,7 +455,7 @@ def model_to_estimator(keras_model=None,
   """Constructs an `Estimator` instance from given keras model.
 
   For usage example, please see
-  @{$programmers_guide/estimators$creating_estimators_from_keras_models}.
+  @{$guide/estimators$creating_estimators_from_keras_models}.
 
   Args:
     keras_model: A compiled Keras model object. This argument is mutually
diff --git a/tensorflow/python/ops/script_ops.py b/tensorflow/python/ops/script_ops.py
index 16c73213d5..f8df9b2c78 100644
--- a/tensorflow/python/ops/script_ops.py
+++ b/tensorflow/python/ops/script_ops.py
@@ -267,7 +267,7 @@ def eager_py_func(func, inp, Tout, name=None):
   or print statements as desired, and wrap those functions in
   `tf.contrib.eager.py_func`.
 
-  For more information on eager execution, see @{$programmers_guide/eager}.
+  For more information on eager execution, see @{$guide/eager}.
 
   `tf.contrib.eager.py_func` is similar in spirit to @{tf.py_func}, but unlike
   the latter, the former lets you use TensorFlow operations in the wrapped
diff --git a/tensorflow/python/tools/saved_model_cli.py b/tensorflow/python/tools/saved_model_cli.py
index 5b9d25d449..38fed5335e 100644
--- a/tensorflow/python/tools/saved_model_cli.py
+++ b/tensorflow/python/tools/saved_model_cli.py
@@ -15,7 +15,7 @@
 """Command-line interface to inspect and execute a graph in a SavedModel.
 
 For detailed usages and examples, please refer to:
-https://www.tensorflow.org/programmers_guide/saved_model_cli
+https://www.tensorflow.org/guide/saved_model_cli
 
 """
 
@@ -720,7 +720,7 @@ def create_parser():
              '\'input4_key=[{"id":[26],"weights":[0.5, 0.5]}]\' \\\n'
              '   --outdir=/out\n\n'
              'For more information about input file format, please see:\n'
-             'https://www.tensorflow.org/programmers_guide/saved_model_cli\n')
+             'https://www.tensorflow.org/guide/saved_model_cli\n')
   parser_run = subparsers.add_parser(
       'run', description=run_msg, formatter_class=argparse.RawTextHelpFormatter)
   parser_run.add_argument(
diff --git a/third_party/examples/eager/spinn/README.md b/third_party/examples/eager/spinn/README.md
index fbb1fde837..e2fd8009a0 100644
--- a/third_party/examples/eager/spinn/README.md
+++ b/third_party/examples/eager/spinn/README.md
@@ -22,7 +22,7 @@ Other eager execution examples can be found under [tensorflow/contrib/eager/pyth
 - [`data.py`](../../../../tensorflow/contrib/eager/python/examples/spinn/data.py): Pipeline for loading and preprocessing the
    [SNLI](https://nlp.stanford.edu/projects/snli/) data and
    [GloVe](https://nlp.stanford.edu/projects/glove/) word embedding, written
-   using the [`tf.data`](https://www.tensorflow.org/programmers_guide/datasets)
+   using the [`tf.data`](https://www.tensorflow.org/guide/datasets)
    API.
 - [`spinn.py`](./spinn.py): Model definition and training routines.
   This example illustrates how one might perform the following actions with