Moves stacks from tutorials to guides. (#644)

- Modifies guides.rst to add stacks subdir
- Replaces all internal refernces in docs from tutorials to guides
- Cleans up cruft left from merge conflict on kernel-modules-dkms.rst

Signed-off-by: Michael Vincerra <michael.vincerra@intel.com>

Closes #579

Signed-off-by: Michael Vincerra <michael.vincerra@intel.com>

Revise for rebase updates.

Signed-off-by: Michael Vincerra <michael.vincerra@intel.com>

source/clear-linux/guides/guides.rst

@@ -38,4 +38,15 @@ Network
    :maxdepth: 1
    :glob:
 
-   network/*
+   network/*
+
+Stacks
+======
+
+.. toctree::
+   :maxdepth: 1
+   :glob:
+
+   stacks/*
+   stacks/dlrs/*
+

source/clear-linux/guides/stacks/dars.rst

@@ -3,7 +3,7 @@
 Data Analytics Reference Stack
 ##############################
 
-This tutorial shows you how to use the Data Analytics Reference Stack
+This guide shows you how to use the Data Analytics Reference Stack
 (DARS), and to optionally build your own images with the baseline Dockerfiles
 provided in the `DARS repository`_. Our assumption is that |CL-ATTR| is the
 host. However, any system that supports Docker\* containers can be used to
@@ -30,15 +30,16 @@ not be the latest released by |CL|.
 
 .. note::
 
-   The Data Analytics Reference Stack is a collective work, and each piece of software within the work has its own license.  Please see the `terms of use`_ for more details about licensing and usage of the Data Analytics Reference Stack.
-
-
+   The Data Analytics Reference Stack is a collective work, and each piece
+   of software within the work has its own license.  Please see the `terms
+   of use`_ for more details about licensing and usage of the Data Analytics
+   Reference Stack.
 
 Using the Docker Images
 ***********************
 
 To immediately start using the latest stable DARS images, pull directly
-from `Docker Hub`_. For this tutorial we'll use the `Dars with MKL`_ version of the stack.
+from `Docker Hub`_. For this guide we'll use the `Dars with MKL`_ version of the stack.
 
 Once you have downloaded the image, you can run it with
 
@@ -99,7 +100,6 @@ You can use any of the resulting images to launch fully functional
 containers.  If you need to customize the containers, you can edit the
 provided :file:`Dockerfile`.
 
-
 .. _DARS repository:  https://github.com/clearlinux/dockerfiles/tree/master/stacks/dars
 .. _Docker Hub: https://hub.docker.com/
 .. _OpenBLAS: http://www.openblas.net/

source/clear-linux/guides/stacks/dlrs/dlrs.rst

@@ -3,7 +3,7 @@
 Deep Learning Reference Stack
 #############################
 
-This tutorial describes how to run benchmarking workloads for TensorFlow\*,
+This guide describes how to run benchmarking workloads for TensorFlow\*,
 PyTorch\*, and Kubeflow in |CL-ATTR| using the Deep Learning Reference Stack.
 
 .. contents::
@@ -13,11 +13,12 @@ PyTorch\*, and Kubeflow in |CL-ATTR| using the Deep Learning Reference Stack.
 Overview
 ********
 
-We created the Deep Learning Reference Stack to help AI developers deliver the
-best experience on Intel® Architecture. This stack reduces complexity common
-with deep learning software components, provides flexibility for customized
-solutions, and enables you to quickly prototype and deploy Deep Learning
-workloads. Use this tutorial to run benchmarking workloads on your solution.
+We created the Deep Learning Reference Stack to help AI developers deliver
+the best experience on Intel® Architecture. This stack reduces complexity
+common with deep learning software components, provides flexibility for
+customized solutions, and enables you to quickly prototype and deploy Deep
+Learning workloads. Use this guide to run benchmarking workloads on your
+solution.
 
 The Deep Learning Reference Stack is available in the following versions:
 
@@ -103,7 +104,7 @@ We validated these steps against the following software package versions:
 
 .. note::
 
-   The Deep Learning Reference Stack was developed to provide the best user experience when executed on a |CL| host.  However, as the stack runs in a container environment, you should be able to complete the following sections of this tutorial on other Linux* distributions, provided they comply with the Docker*, Kubernetes* and Go* package versions listed above. Look for your distribution documentation on how to update packages and manage Docker services.
+   The Deep Learning Reference Stack was developed to provide the best user experience when executed on a |CL| host.  However, as the stack runs in a container environment, you should be able to complete the following sections of this guide on other Linux* distributions, provided they comply with the Docker*, Kubernetes* and Go* package versions listed above. Look for your distribution documentation on how to update packages and manage Docker services.
 
 TensorFlow single and multi-node benchmarks
 *******************************************
@@ -115,9 +116,7 @@ TensorFlow.
 
 .. note::
 
-   Performance test results for the Deep Learning Reference Stack and for this tutorial were
-   obtained using `runc` as the runtime.
-
+   Performance test results for the Deep Learning Reference Stack and for this guide were obtained using `runc` as the runtime.
 
 #. Download either the `Eigen`_ or the `Intel MKL-DNN`_ Docker image
    from `Docker Hub`_.
@@ -194,12 +193,12 @@ single node.
 Kubeflow multi-node benchmarks
 ******************************
 
-The benchmark workload runs in a Kubernetes cluster. The tutorial uses
+The benchmark workload runs in a Kubernetes cluster. The guide uses
 `Kubeflow`_ for the Machine Learning workload deployment on three nodes.
 
 .. warning::
 
-   If you choose the Intel® MKL-DNN or Intel® MKL-DNN-VNNI image, your platform must support the Intel® AVX-512 instruction set. Otherwise, an *illegal instruction* error may appear, and you won’t be able to complete this tutorial.
+   If you choose the Intel® MKL-DNN or Intel® MKL-DNN-VNNI image, your platform must support the Intel® AVX-512 instruction set. Otherwise, an *illegal instruction* error may appear, and you won’t be able to complete this guide.
 
 
 Kubernetes setup
@@ -255,7 +254,7 @@ Images
 
 You must add `launcher.py`_ to the Docker image to include the Deep
 Learning Reference Stack and put the benchmarks repo in the correct
-location. Note that this tutorial uses Kubeflow v0.4.0, and cannot guarantee results if you use a different version.
+location. Note that this guide uses Kubeflow v0.4.0, and cannot guarantee results if you use a different version.
 
 From the Docker image, run the following:
 
@@ -362,8 +361,8 @@ Run a TFJob
 
 This replicates and deploys three test setups in your Kubernetes cluster.
 
-Results of running this tutorial
-================================
+Results of running this guide
+=============================
 
 You must parse the logs of the Kubernetes pod to retrieve performance
 data. The pods will still exist post-completion and will be in

source/clear-linux/guides/stacks/greengrass.rst

@@ -6,36 +6,36 @@ Enable AWS Greengrass\* and OpenVINO™ toolkit
 Hardware accelerated Function-as-a-Service (FaaS) enables cloud developers to
 deploy inference functionalities [1] on Intel® IoT edge devices with
 accelerators (CPU, Integrated GPU, Intel® FPGA, and Intel® Movidius™
-technology). These functions provide a great developer experience and seamless
-migration of visual analytics from cloud to edge in a secure manner using a
-containerized environment. Hardware-accelerated FaaS provides the best-in-class
-performance by accessing optimized deep learning libraries on Intel® IoT
-edge devices with accelerators.
+technology). These functions provide a great developer experience and
+seamless migration of visual analytics from cloud to edge in a secure manner
+using a containerized environment. Hardware-accelerated FaaS provides the
+best-in-class performance by accessing optimized deep learning libraries on
+Intel® IoT edge devices with accelerators.
 
-This tutorial demonstrates how to:
+This guide demonstrates how to:
 
 * Set up the Intel® edge device with |CL-ATTR|
 * Install the OpenVINO™ toolkit and Amazon Web Services\* (AWS\*)
   Greengrass\* software stacks
-* Use AWS Greengrass\* and AWS Lambda\* to deploy the FaaS samples from the cloud
+* Use AWS Greengrass\* and AWS Lambda\* to deploy the FaaS samples from
+  the cloud
 
 Refer to the following topics:
 
 .. contents:: :local:
    :depth: 1
 
-
 Supported platforms
 *******************
 
 *	Operating System: |CL| latest release
-*	Hardware:	Intel® core platforms (This tutorial supports inference on CPU only.)
+*	Hardware:	Intel® core platforms (that support inference on CPU only)
 
 Sample description
 ==================
 
 The AWS Greengrass samples are located at `Edge-Analytics-FaaS`_. This
-tutorial uses the 1.0 version of the source code.
+guide uses the 1.0 version of the source code.
 
 |CL| provides the following AWS Greengrass samples:
 
@@ -117,9 +117,9 @@ Locate sample models
 --------------------
 
 There are two types of provided models that can be used in conjunction with
-AWS Greengrass for this tutorial: classification or object detection.
+AWS Greengrass for this guide: classification or object detection.
 
-To complete this tutorial using an image classification model,
+To complete this guide using an image classification model,
 download the BVLC AlexNet model files `bvlc_alexnet.caffemodel`_ and `deploy.prototxt`_
 to the default model_location at :file:`/usr/share/openvino/models`.
 Any custom pre-trained classification models can be used with the
@@ -203,7 +203,7 @@ cloud and edge.
 Create and package Lambda function
 **********************************
 
-#. Complete steps 1-4 of the AWS Greengrass tutorial at `Create and Package a Lambda Function`_.
+#. Complete steps 1-4 of the AWS Greengrass guide at `Create and Package a Lambda Function`_.
 
    .. note::
 
@@ -271,7 +271,7 @@ configuring the Lambda function for AWS Greengrass.
       * - PARAM_MODEL_XML
         - <MODEL_DIR>/<IR.xml>, where <MODEL_DIR> is user specified and
           contains IR.xml, the Intermediate Representation file from Intel® Model Optimizer.
-          For this tutorial, <MODEL_DIR> should be set to '/usr/share/openvino/models'
+          For this guide, <MODEL_DIR> should be set to '/usr/share/openvino/models'
           or one of its subdirectories.
       * - PARAM_INPUT_SOURCE
         - <DATA_DIR>/input.webm to be specified by user. Holds both input and
@@ -292,8 +292,7 @@ configuring the Lambda function for AWS Greengrass.
 
    .. note::
 
-      The optional topic filter field is the topic mentioned inside the Lambda
-      function. In this tutorial, sample topics include the following:
+      The optional topic filter field is the topic mentioned inside the Lambda function. In this guide, sample topics include the following:
       :command:`openvino/ssd` or :command:`openvino/classification`
 
 Add local resources

source/clear-linux/tutorials/tutorials.rst

@@ -22,10 +22,7 @@ Explore our tutorials to discover what you can do with |CL|!
    kata
    kata_migration
    kubernetes/kubernetes*
-   greengrass
-   dlrs/dlrs
    yubikey-u2f
    nvidia
-   dars
    redis
    tutorial-proxy