Rename programmers_guide/ directory to guide/.

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

PiperOrigin-RevId: 201766994

Author: lamberta

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

RELEASE.md

@@ -465,7 +465,7 @@ 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.
   * For a guide to migrating from the `tf.contrib.data` API, see the
@@ -485,7 +485,7 @@ 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
@@ -772,7 +772,7 @@ 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

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:
 

tensorflow/contrib/data/__init__.py

@@ -20,7 +20,7 @@
 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

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)

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."
       ]
     },
     {

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).
 

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

tensorflow/contrib/lite/toco/g3doc/cmdline_examples.md

@@ -81,19 +81,19 @@ tflite_convert \
   --saved_model_dir=/tmp/saved_model
 ```
 
-[SavedModel](https://www.tensorflow.org/programmers_guide/saved_model#using_savedmodel_with_estimators)
+[SavedModel](https://www.tensorflow.org/guide/saved_model#using_savedmodel_with_estimators)
 has fewer required flags than frozen graphs due to access to additional data
 contained within the SavedModel. The values for `--input_arrays` and
 `--output_arrays` are an aggregated, alphabetized list of the inputs and outputs
 in the [SignatureDefs](https://www.tensorflow.org/serving/signature_defs) within
 the
-[MetaGraphDef](https://www.tensorflow.org/programmers_guide/saved_model#apis_to_build_and_load_a_savedmodel)
+[MetaGraphDef](https://www.tensorflow.org/guide/saved_model#apis_to_build_and_load_a_savedmodel)
 specified by `--saved_model_tag_set`. As with the GraphDef, the value for
 `input_shapes` is automatically determined whenever possible.
 
 There is currently no support for MetaGraphDefs without a SignatureDef or for
 MetaGraphDefs that use the [`assets/`
-directory](https://www.tensorflow.org/programmers_guide/saved_model#structure_of_a_savedmodel_directory).
+directory](https://www.tensorflow.org/guide/saved_model#structure_of_a_savedmodel_directory).
 
 ## Quantization
 

tensorflow/contrib/tpu/python/tpu/tpu_estimator.py

@@ -1136,7 +1136,7 @@ def _validate_input_pipeline(self):
       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)

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

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

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
 

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.
 

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!

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

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>
 

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.

tensorflow/docs_src/programmers_guide/checkpoints.md renamed to 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

tensorflow/docs_src/programmers_guide/custom_estimators.md renamed to tensorflow/docs_src/guide/custom_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`.