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Merge pull request #319 from psfoley/pf-greengrass-cleanup
Greengrass tutorial: Tutorial structure change, link updates, phrasing changes for clarity
This commit is contained in:
michael vincerra
@@ -5,14 +5,14 @@ Enable AWS Greengrass* and OpenVINO™ on |CL-ATTR|
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Hardware accelerated Function-as-a-Service (FaaS) enables cloud developers
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to deploy inference functionalities [1] on Intel® IoT edge devices with
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accelerators (Integrated GPU, Intel® FPGA, and Intel® Movidius™). These
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accelerators (CPU, Integrated GPU, Intel® FPGA, and Intel® Movidius™). These
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functions provide a great developer experience and seamless migration of
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visual analytics from cloud to edge in a secure manner using a containerized
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environment. Hardware-accelerated FaaS provides the best-in-class
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performance by accessing optimized deep learning libraries on Intel® IoT
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edge devices with accelerators.
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This tutorial shows how to:
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This tutorial will demonstrate how to:
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* Set up the Intel® edge device with |CL-ATTR|
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* Install the OpenVINO™ and AWS Greengrass* software stacks
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@@ -31,13 +31,13 @@ The AWS Greengrass samples are located at the `Edge-Analytics-FaaS`_.
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We provide the following AWS Greengrass samples:
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* :file:`greengrass_classification_sample.py`
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* `greengrass_classification_sample.py`_
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This AWS Greengrass sample classifies a video stream using classification
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networks such as AlexNet and GoogLeNet and publishes top-10 results on AWS*
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IoT Cloud every second.
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* :file:`greengrass_object_detection_sample_ssd.py`
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* `greengrass_object_detection_sample_ssd.py`_
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This AWS Greengrass sample detects objects in a video stream and
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classifies them using single-shot multi-box detection (SSD) networks such
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@@ -45,61 +45,6 @@ We provide the following AWS Greengrass samples:
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detection outputs such as class label, class confidence, and bounding box
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coordinates on AWS IoT Cloud every second.
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Converting Deep Learning Models
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*******************************
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Sample Models
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=============
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For classification, `download the BVLC Alexnet model`_ as an example.
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Any custom pre-trained classification models can be used with the
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classification sample.
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For object detection, the sample models optimized for Intel® edge platforms
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are present at :file:`/usr/share/openvino/models`. These models are provided
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as an example; however, any custom pre-trained SSD models can be used with
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the object detection sample.
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Running Model Optimizer
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=======================
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Follow these instructions for `converting deep learning models to Intermediate Representation using Model Optimizer`_. For example, use the
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following commands.
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For classification using BVLC Alexnet model:
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.. code-block:: bash
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python3 mo.py --framework caffe --input_model <
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model_location>/bvlc_alexnet.caffemodel --input_proto <
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model_location>/deploy.prototxt --data_type <data_type> --output_dir <
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output_dir> --input_shape [1,3,227,227]
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For object detection using SqueezeNetSSD-5Class model:
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.. code-block:: bash
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python3 mo.py --framework caffe --input_model
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SqueezeNetSSD-5Class.caffemodel --input_proto
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SqueezeNetSSD-5Class.prototxt
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--data_type <data_type> --output_dir <output_dir>
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In these examples:
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* ``<model_location>`` is :file:`/usr/share/openvino/models`
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* ``<data_type>`` is FP32 or FP16, depending on target device.
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* ``<output_dir>`` is the directory where the user wants to store the
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Intermediate Representation (IR). IR contains .xml format corresponding
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to the network structure and .bin format corresponding to weights. This
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.xml file should be passed to <PARAM_MODEL_XML>.
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* In the BVLC Alexnet model, the prototxt defines the input shape with
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batch size 10 by default. In order to use any other batch size, the
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entire input shape needs to be provided as an argument to the model
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optimizer. For example, to use batch size 1, you can provide
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“--input_shape [1,3,227,227]”.
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Installing |CL| on the edge device
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**********************************
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@@ -129,13 +74,6 @@ services to use (see Greengrass user below).
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usermod -G wheel -a <userid>
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#. Create the user and group account for the Greengrass daemon:
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.. code-block:: console
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useradd ggc_user
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groupadd ggc_group
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#. Create a :file:`/etc/fstab` file.
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.. code-block:: bash
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@@ -151,8 +89,8 @@ services to use (see Greengrass user below).
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Add required bundles
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====================
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Use the ``swupd`` software updater utility to add the following bundles to
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enable the OpenVINO software stack:
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Use the ``swupd`` software updater utility to add the prerequisite bundles
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for the OpenVINO software stack:
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.. code-block:: bash
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@@ -165,6 +103,67 @@ enable the OpenVINO software stack:
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The ``computer-vision-basic`` bundle will install the OpenVINO software,
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along with the edge device models needed.
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Converting Deep Learning Models
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===============================
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Locate Sample Models
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--------------------
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There are two types of provided models that can be used in conjunction with AWS Greengrass
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for this tutorial: classification or object detection.
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To complete this tutorial using an image classification model,
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download the BVLC Alexnet model files `bvlc_alexnet.caffemodel`_ and `deploy.prototxt`_
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to the default model_location at :file:`/usr/share/openvino/models`.
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Any custom pre-trained classification models can be used with the
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classification sample.
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For object detection, the sample models optimized for Intel® edge platforms
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are included with the computer-vision-basic bundle installation at :file:`/usr/share/openvino/models`.
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These models are provided as an example; however, you may also use a custom SSD model
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with the Greengrass object detection sample.
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Running Model Optimizer
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-----------------------
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Follow these instructions for `converting deep learning models to Intermediate Representation using Model Optimizer`_. To optimize either of the afformentioned sample models, run one of the following commands.
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For classification using BVLC Alexnet model:
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.. code-block:: bash
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python3 mo.py --framework caffe --input_model
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<model_location>/bvlc_alexnet.caffemodel --input_proto
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<model_location>/deploy.prototxt --data_type <data_type> --output_dir
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<output_dir> --input_shape [1,3,227,227]
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For object detection using SqueezeNetSSD-5Class model:
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.. code-block:: bash
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python3 mo.py --framework caffe --input_model
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<model_location>/'SqueezeNet 5-Class detection'/SqueezeNetSSD-5Class.caffemodel
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--input_proto <model_location>/'SqueezeNet 5-Class detection'/SqueezeNetSSD-5Class.prototxt
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--data_type <data_type> --output_dir <output_dir>
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In these examples:
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* ``<model_location>`` is :file:`/usr/share/openvino/models`
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* ``<data_type>`` is FP32 or FP16, depending on target device.
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* ``<output_dir>`` is the directory where the user wants to store the
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Intermediate Representation (IR). IR contains .xml format corresponding
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to the network structure and .bin format corresponding to weights. This
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.xml file should be passed to <PARAM_MODEL_XML>.
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* In the BVLC Alexnet model, the prototxt defines the input shape with
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batch size 10 by default. In order to use any other batch size, the
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entire input shape needs to be provided as an argument to the model
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optimizer. For example, to use batch size 1, you can provide
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“--input_shape [1,3,227,227]”.
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Configuring an AWS Greengrass group
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===================================
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@@ -176,7 +175,9 @@ cloud and edge.
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`AWS Greengrass developer guide`_
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#. To install and configure AWS Greengrass core on edge platform, follow
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the instructions at `Start AWS Greengrass on the Core Device`_.
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the instructions at `Start AWS Greengrass on the Core Device`_. In
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step 8(b), download the x86_64 Ubuntu configuration of the AWS Greengrass
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core software.
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.. note::
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@@ -184,43 +185,40 @@ cloud and edge.
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of Module 1 of the `AWS Greengrass developer guide`_ because this is
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enabled already in |CL|.
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Creating and Packaging Lambda Functions
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=======================================
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#. Complete the tutorial at `Configure AWS Greengrass on AWS IoT`_ .
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.. note::
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This creates the tarball needed to create the AWS Greengrass
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environment on the edge device.
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#. Assure to download both the security resources and the AWS Greengrass
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#. Be sure to download both the security resources and the AWS Greengrass
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core software.
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.. note::
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Security certificates are linked to your AWS* account.
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#. Replace greengrassHelloWorld.py with Greengrass samples:
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* greengrass_classification_sample.py
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Creating and Packaging Lambda Functions
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=======================================
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* greengrass_object_detection_sample_ssd.py
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#. Complete steps 1-4 of the tutorial at `Create and Package Lambda Function`_ .
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#. Zip these files with extracted Greengrass SDK folders from the previous
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.. note::
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This creates the tarball needed to create the AWS Greengrass
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environment on the edge device.
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#. In step 5, replace greengrassHelloWorld.py with the classification or object detection
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Greengrass sample from `Edge-Analytics-Faas`_:
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* Classification: `greengrass_classification_sample.py`_
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* Object Detection: `greengrass_object_detection_sample_ssd.py`_
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#. Zip the selected Greengrass sample with the extracted Greengrass SDK folders from the previous
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step into :file:`greengrass_sample_python_lambda.zip`.
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The zip should contain:
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* greengrasssdk
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* greengrass sample
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For the sample, choose one of these:
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- greengrass_classification_sample.py
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- greengrass_object_detection_sample_ssd.py
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* greengrass classification or object detection sample
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For example:
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@@ -229,11 +227,11 @@ Creating and Packaging Lambda Functions
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zip -r greengrass_lambda.zip greengrasssdk
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greengrass_object_detection_sample_ssd.py
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#. Follow steps 6-11 to `complete creating lambdas`_.
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#. Return to the AWS Documentation and follow steps 6-11 to `complete creating lambdas`_.
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.. note::
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In the AWS documentation, step 9(a), while uploading the zip file,
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In step 9(a) of the AWS documentation, while uploading the zip file,
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make sure to name the handler as below depending on the AWS Greengrass
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sample you are using:
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@@ -266,12 +264,14 @@ configuring the lambda function for AWS Greengrass.
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- Value
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* - PARAM_MODEL_XML
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- <MODEL_DIR>/<IR.xml>, where <MODEL_DIR> is user specified and
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contains IR.xml, the Intermediate Representation file from Intel® Model Optimizer
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contains IR.xml, the Intermediate Representation file from Intel® Model Optimizer.
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For this tutorial, <MODEL_DIR> should be set to '/usr/share/openvino/models'
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or one of its subdirectories.
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* - PARAM_INPUT_SOURCE
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- <DATA_DIR>/input.webm to be specified by user. Holds both input and
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output data. For webcam, set PARAM_INPUT_SOURCE to ‘/dev/video0’
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* - PARAM_DEVICE
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- For CPU, specify "CPU"
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- "CPU"
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* - PARAM_CPU_EXTENSION_PATH
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- /usr/lib64/libcpu_extension.so
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* - PARAM_OUTPUT_DIRECTORY
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@@ -377,7 +377,13 @@ References
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.. _Edge-Analytics-FaaS: https://github.com/intel/Edge-Analytics-FaaS/tree/master/AWS%20Greengrass
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.. _download the BVLC Alexnet model: https://github.com/BVLC/caffe/tree/master/models/bvlc_alexnet
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.. _bvlc_alexnet.caffemodel: http://dl.caffe.berkeleyvision.org/bvlc_alexnet.caffemodel
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.. _deploy.prototxt: https://github.com/BVLC/caffe/blob/master/models/bvlc_alexnet/deploy.prototxt
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.. _greengrass_classification_sample.py: https://github.com/intel/Edge-Analytics-FaaS/blob/master/AWS%20Greengrass/greengrass_classification_sample.py
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.. _greengrass_object_detection_sample_ssd.py: https://github.com/intel/Edge-Analytics-FaaS/blob/master/AWS%20Greengrass/greengrass_object_detection_sample_ssd.py
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.. _converting deep learning models to Intermediate Representation using Model Optimizer: https://software.intel.com/en-us/articles/OpenVINO-ModelOptimizer
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@@ -401,4 +407,4 @@ References
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.. _this link to add local resources and access privileges: https://docs.aws.amazon.com/greengrass/latest/developerguide/access-local-resources.html
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.. _Configure AWS Greengrass on AWS IoT: https://docs.aws.amazon.com/greengrass/latest/developerguide/gg-config.html
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.. _Create and Package Lambda Function: https://docs.aws.amazon.com/greengrass/latest/developerguide/create-lambda.html
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