diff --git a/source/clear-linux/guides/guides.rst b/source/clear-linux/guides/guides.rst index d00fd5da..7f7d0b07 100644 --- a/source/clear-linux/guides/guides.rst +++ b/source/clear-linux/guides/guides.rst @@ -38,4 +38,15 @@ Network :maxdepth: 1 :glob: - network/* \ No newline at end of file + network/* + +Stacks +====== + +.. toctree:: + :maxdepth: 1 + :glob: + + stacks/* + stacks/dlrs/* + diff --git a/source/clear-linux/tutorials/dars.rst b/source/clear-linux/guides/stacks/dars.rst similarity index 92% rename from source/clear-linux/tutorials/dars.rst rename to source/clear-linux/guides/stacks/dars.rst index a134c938..8e1a6848 100644 --- a/source/clear-linux/tutorials/dars.rst +++ b/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/ diff --git a/source/clear-linux/tutorials/dlrs/dlrs.rst b/source/clear-linux/guides/stacks/dlrs/dlrs.rst similarity index 94% rename from source/clear-linux/tutorials/dlrs/dlrs.rst rename to source/clear-linux/guides/stacks/dlrs/dlrs.rst index 49dd3930..ea09e94f 100644 --- a/source/clear-linux/tutorials/dlrs/dlrs.rst +++ b/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 diff --git a/source/clear-linux/tutorials/dlrs/figures/dlrs-fig-1.png b/source/clear-linux/guides/stacks/dlrs/figures/dlrs-fig-1.png similarity index 100% rename from source/clear-linux/tutorials/dlrs/figures/dlrs-fig-1.png rename to source/clear-linux/guides/stacks/dlrs/figures/dlrs-fig-1.png diff --git a/source/clear-linux/tutorials/dlrs/figures/dlrs-fig-2.png b/source/clear-linux/guides/stacks/dlrs/figures/dlrs-fig-2.png similarity index 100% rename from source/clear-linux/tutorials/dlrs/figures/dlrs-fig-2.png rename to source/clear-linux/guides/stacks/dlrs/figures/dlrs-fig-2.png diff --git a/source/clear-linux/tutorials/dlrs/figures/dlrs-fig-3.png b/source/clear-linux/guides/stacks/dlrs/figures/dlrs-fig-3.png similarity index 100% rename from source/clear-linux/tutorials/dlrs/figures/dlrs-fig-3.png rename to source/clear-linux/guides/stacks/dlrs/figures/dlrs-fig-3.png diff --git a/source/clear-linux/tutorials/dlrs/figures/dlrs-fig-4.png b/source/clear-linux/guides/stacks/dlrs/figures/dlrs-fig-4.png similarity index 100% rename from source/clear-linux/tutorials/dlrs/figures/dlrs-fig-4.png rename to source/clear-linux/guides/stacks/dlrs/figures/dlrs-fig-4.png diff --git a/source/clear-linux/tutorials/dlrs/figures/dlrs-fig-5.png b/source/clear-linux/guides/stacks/dlrs/figures/dlrs-fig-5.png similarity index 100% rename from source/clear-linux/tutorials/dlrs/figures/dlrs-fig-5.png rename to source/clear-linux/guides/stacks/dlrs/figures/dlrs-fig-5.png diff --git a/source/clear-linux/tutorials/greengrass.rst b/source/clear-linux/guides/stacks/greengrass.rst similarity index 94% rename from source/clear-linux/tutorials/greengrass.rst rename to source/clear-linux/guides/stacks/greengrass.rst index 311a905b..4d780f28 100644 --- a/source/clear-linux/tutorials/greengrass.rst +++ b/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 - /, where is user specified and contains IR.xml, the Intermediate Representation file from Intel® Model Optimizer. - For this tutorial, should be set to '/usr/share/openvino/models' + For this guide, should be set to '/usr/share/openvino/models' or one of its subdirectories. * - PARAM_INPUT_SOURCE - /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 diff --git a/source/clear-linux/tutorials/tutorials.rst b/source/clear-linux/tutorials/tutorials.rst index 0d5300e9..b7d67ebc 100644 --- a/source/clear-linux/tutorials/tutorials.rst +++ b/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