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updated incorrect bundle name
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Beth Dean
@@ -3,25 +3,25 @@
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Enable AWS Greengrass* and OpenVINO™ on |CL-ATTR|
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#################################################
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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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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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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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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 shows 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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* Use AWS Greengrass and lambdas to deploy the FaaS samples from the cloud
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* Use AWS Greengrass and lambdas to deploy the FaaS samples from the cloud
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Supported Platforms
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*******************
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* Operating System: |CL-ATTR| latest release
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* Operating System: |CL-ATTR| latest release
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* Hardware: Intel® core platforms (Tutorial supports inference on CPU only)
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Description of Samples
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@@ -32,7 +32,7 @@ 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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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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@@ -41,7 +41,7 @@ We provide the following AWS Greengrass samples:
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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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as SSD Squeezenet, SSD Mobilenet, and SSD300. This sample publishes
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as SSD Squeezenet, SSD Mobilenet, and SSD300. This sample publishes
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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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@@ -51,13 +51,13 @@ Converting Deep Learning Models
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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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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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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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@@ -79,42 +79,42 @@ 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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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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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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In these examples:
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* ``<model_location>`` is :file:`/usr/share/openvino/models`
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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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* ``<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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* ``<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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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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Start with a clean installation of |CL| on a new system, using the
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Start with a clean installation of |CL| on a new system, using the
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:ref:`bare-metal-install`, found in :ref:`get-started`.
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Create user accounts
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====================
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After |CL| is installed, create two user accounts. Create an administrative
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After |CL| is installed, create two user accounts. Create an administrative
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user in |CL|. You will also create a user account for the Greengrass
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services to use (see Greengrass user below).
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services to use (see Greengrass user below).
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#. Create a new user and set a password for that user. Enter the following
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#. Create a new user and set a password for that user. Enter the following
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commands as ``root``:
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.. code-block:: bash
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@@ -122,7 +122,7 @@ services to use (see Greengrass user below).
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useradd <userid>
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passwd <userid>
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#. Next, enable the :command:`sudo` command for your new ``<userid>``. Add
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#. Next, enable the :command:`sudo` command for your new ``<userid>``. Add
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``<userid>`` to the ``wheel`` group:
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.. code-block:: bash
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@@ -136,18 +136,18 @@ services to use (see Greengrass user below).
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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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#. Create a :file:`/etc/fstab` file.
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.. code-block:: bash
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touch /etc/fstab
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.. note::
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By default |CL| does not create an :file:`/etc/fstab` file.
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The Greengrass service needs to have the file created before
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.. note::
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By default |CL| does not create an :file:`/etc/fstab` file.
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The Greengrass service needs to have the file created before
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it will run.
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Add required bundles
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====================
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@@ -156,67 +156,67 @@ enable the OpenVINO software stack:
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.. code-block:: bash
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swupd bundle-add os-clr-on-clear desktop-autostart computer-vision-basic
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swupd bundle-add os-clr-on-clr desktop-autostart computer-vision-basic
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.. note::
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Learn more about how to :ref:`swupd-guide`.
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Learn more about how to :ref:`swupd-guide`.
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The ``computer-vision-basic`` bundle will install the OpenVINO software,
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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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Configuring an AWS Greengrass group
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===================================
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For each Intel® edge platform, we need to create a new AWS Greengrass group
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and install AWS Greengrass core software to establish the connection between
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For each Intel® edge platform, we need to create a new AWS Greengrass group
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and install AWS Greengrass core software to establish the connection between
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cloud and edge.
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#. To create an AWS Greengrass group, follow the
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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`_.
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.. note::
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You will not need to run the ``cgroupfs-mount.sh`` script in step #6
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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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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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#. Complete the tutorial at `Configure AWS Greengrass on AWS IoT`_ .
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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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.. note::
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#. Assure to download both the security resources and the AWS Greengrass
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core software.
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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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.. note::
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#. Assure to download both the security resources and the AWS Greengrass
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core software.
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Security certificates are linked to your AWS* account.
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.. note::
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#. Replace greengrassHelloWorld.py with Greengrass samples:
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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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* greengrass_object_detection_sample_ssd.py
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* greengrass_object_detection_sample_ssd.py
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#. Zip these files with extracted Greengrass SDK folders from the previous
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step into :file:`greengrass_sample_python_lambda.zip`.
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#. Zip these files with 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 sample
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For the sample, choose one of these:
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- greengrass_classification_sample.py
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@@ -229,15 +229,15 @@ 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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.. note::
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#. Follow steps 6-11 to `complete creating lambdas`_.
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In the AWS documentation, step 9(a), 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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.. note::
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In the AWS documentation, step 9(a), 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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* greengrass_object_detection_sample_ssd.function_handler (or)
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* greengrass_object_detection_sample_ssd.function_handler (or)
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* greengrass_classification_sample.function_handler
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Deploying Lambdas
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@@ -246,18 +246,18 @@ Deploying Lambdas
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Configuring the Lambda function
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-------------------------------
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After creating the Greengrass group and the lambda function, start
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configuring the lambda function for AWS Greengrass.
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After creating the Greengrass group and the lambda function, start
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configuring the lambda function for AWS Greengrass.
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#. Follow steps 1-8 in `Configure the Lambda Function`_ of the AWS
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documentation.
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documentation.
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#. In addition to the details mentioned in step 8, change the Memory limit
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to 2048MB to accommodate large input video streams.
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#. Add the following environment variables as key-value pairs when editing
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the lambda configuration and click on update:
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.. list-table:: **Table 1. Environment Variables: Lambda Configuration**
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:widths: 20 80
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:header-rows: 1
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@@ -265,7 +265,7 @@ configuring the lambda function for AWS Greengrass.
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* - Key
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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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- <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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* - PARAM_INPUT_SOURCE
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- <DATA_DIR>/input.webm to be specified by user. Holds both input and
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@@ -281,19 +281,19 @@ configuring the lambda function for AWS Greengrass.
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- User specified for classification sample.
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(e.g. 1 for top-1 result, 5 for top-5 results)
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#. Add subscription to subscribe, or publish messages from AWS Greengrass
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lambda function by following the steps 10-14 in `Configure the Lambda Function`_
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#. Add subscription to subscribe, or publish messages from AWS Greengrass
|
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lambda function by following the steps 10-14 in `Configure the Lambda Function`_
|
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|
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.. note::
|
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|
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The “Optional topic filter” field should be the topic
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.. note::
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|
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The “Optional topic filter” field should be the topic
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mentioned inside the lambda function.
|
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|
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|
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For example, openvino/ssd or openvino/classification
|
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|
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Local Resources
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---------------
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#. Select `this link to add local resources and access privileges`_.
|
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#. Select `this link to add local resources and access privileges`_.
|
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|
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Following are the local resources needed for the CPU:
|
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|
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@@ -301,71 +301,71 @@ Local Resources
|
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:widths: 20, 20, 20, 20
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:header-rows: 1
|
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|
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* - Name
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- Resource type
|
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- Local path
|
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* - Name
|
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- Resource type
|
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- Local path
|
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- Access
|
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|
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* - ModelDir
|
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- Volume
|
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- <MODEL_DIR> to be specified by user
|
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|
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* - ModelDir
|
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- Volume
|
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- <MODEL_DIR> to be specified by user
|
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- Read-Only
|
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|
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* - Webcam
|
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- Device
|
||||
* - Webcam
|
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- Device
|
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- /dev/video0
|
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- Read-Only
|
||||
|
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* - DataDir
|
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- Volume
|
||||
- <DATA_DIR> to be specified by user. Holds both input and output
|
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* - DataDir
|
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- Volume
|
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- <DATA_DIR> to be specified by user. Holds both input and output
|
||||
data.
|
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- Read and Write
|
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|
||||
Deploy
|
||||
------
|
||||
|
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To `deploy the lambda function to AWS Greengrass core device`_, select
|
||||
“Deployments” on group page and follow the instructions.
|
||||
To `deploy the lambda function to AWS Greengrass core device`_, select
|
||||
“Deployments” on group page and follow the instructions.
|
||||
|
||||
Output Consumption
|
||||
------------------
|
||||
|
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There are four options available for output consumption. These options are
|
||||
used to report, stream, upload, or store inference output at an interval
|
||||
There are four options available for output consumption. These options are
|
||||
used to report, stream, upload, or store inference output at an interval
|
||||
defined by the variable ``reporting_interval`` in the AWS Greengrass samples.
|
||||
|
||||
a. IoT Cloud Output:
|
||||
This option is enabled by default in the AWS Greengrass samples using a
|
||||
variable ``enable_iot_cloud_output``. We can use it to verify the lambda
|
||||
running on the edge device. It enables publishing messages to IoT cloud
|
||||
using the subscription topic specified in the lambda (For example,
|
||||
‘openvino/classification’ for classification and ‘openvino/ssd’ for
|
||||
object detection samples). For classification, top-1 result with class
|
||||
label are published to IoT cloud. For SSD object detection, detection
|
||||
results such as bounding box co-ordinates of objects, class label, and
|
||||
class confidence are published.
|
||||
This option is enabled by default in the AWS Greengrass samples using a
|
||||
variable ``enable_iot_cloud_output``. We can use it to verify the lambda
|
||||
running on the edge device. It enables publishing messages to IoT cloud
|
||||
using the subscription topic specified in the lambda (For example,
|
||||
‘openvino/classification’ for classification and ‘openvino/ssd’ for
|
||||
object detection samples). For classification, top-1 result with class
|
||||
label are published to IoT cloud. For SSD object detection, detection
|
||||
results such as bounding box co-ordinates of objects, class label, and
|
||||
class confidence are published.
|
||||
|
||||
Follow the instructions here to `view the output on IoT cloud`_
|
||||
|
||||
|
||||
b. Kinesis Streaming:
|
||||
|
||||
This option enables inference output to be streamed from the edge device
|
||||
to cloud using Kinesis [3] streams when ‘enable_kinesis_output’ is set
|
||||
to True. The edge devices act as data producers and continually push
|
||||
processed data to the cloud. The users need to set up and specify
|
||||
Kinesis stream name, Kinesis shard, and AWS region in the AWS Greengrass
|
||||
|
||||
This option enables inference output to be streamed from the edge device
|
||||
to cloud using Kinesis [3] streams when ‘enable_kinesis_output’ is set
|
||||
to True. The edge devices act as data producers and continually push
|
||||
processed data to the cloud. The users need to set up and specify
|
||||
Kinesis stream name, Kinesis shard, and AWS region in the AWS Greengrass
|
||||
samples.
|
||||
|
||||
c. Cloud Storage using AWS S3 Bucket:
|
||||
|
||||
When the ‘enable_s3_jpeg_output’ variable is set to True, it enables uploading and storing processed frames (in JPEG format) in an AWS S3 bucket. The users need to set up and specify the S3 bucket name in the
|
||||
|
||||
When the ‘enable_s3_jpeg_output’ variable is set to True, it enables uploading and storing processed frames (in JPEG format) in an AWS S3 bucket. The users need to set up and specify the S3 bucket name in the
|
||||
AWS Greengrass samples to store the JPEG images. The images are named using the timestamp and uploaded to S3.
|
||||
|
||||
d. Local Storage:
|
||||
|
||||
When the ‘enable_s3_jpeg_output’ variable is set to True, it enables storing processed frames (in JPEG format) on the edge device. The
|
||||
images are named using the timestamp and stored in a directory specified
|
||||
|
||||
When the ‘enable_s3_jpeg_output’ variable is set to True, it enables storing processed frames (in JPEG format) on the edge device. The
|
||||
images are named using the timestamp and stored in a directory specified
|
||||
by ‘PARAM_OUTPUT_DIRECTORY’.
|
||||
|
||||
References
|
||||
@@ -391,7 +391,7 @@ References
|
||||
|
||||
.. _Configure the Lambda Function: https://docs.aws.amazon.com/greengrass/latest/developerguide/config-lambda.html
|
||||
|
||||
.. _Add local resources and access privileges: https://docs.aws.amazon.com/greengrass/latest/developerguide/access-local-resources.html
|
||||
.. _Add local resources and access privileges: https://docs.aws.amazon.com/greengrass/latest/developerguide/access-local-resources.html
|
||||
|
||||
.. _deploy the lambda function to AWS Greengrass core device: https://docs.aws.amazon.com/greengrass/latest/developerguide/configs-core.html
|
||||
|
||||
@@ -402,5 +402,3 @@ References
|
||||
.. _this link to add local resources and access privileges: https://docs.aws.amazon.com/greengrass/latest/developerguide/access-local-resources.html
|
||||
|
||||
.. _Configure AWS Greengrass on AWS IoT: https://docs.aws.amazon.com/greengrass/latest/developerguide/gg-config.html
|
||||
|
||||
|
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Reference in New Issue
Block a user