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.. _greengrass:
Enable AWS Greengrass\* and OpenVINO™ on |CL-ATTR|
##################################################
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
@@ -19,19 +19,25 @@ This tutorial demonstrates how to:
Greengrass\* software stacks
* Use AWS Greengrass\* and AWS Lambda\* to deploy the FaaS samples from the cloud
Supported Platforms
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.)
Description of Samples
**********************
Sample description
==================
The AWS Greengrass samples are located at the `Edge-Analytics-FaaS`_. This
The AWS Greengrass samples are located at `Edge-Analytics-FaaS`_. This
tutorial uses the 1.0 version of the source code.
We provide the following AWS Greengrass samples:
|CL| provides the following AWS Greengrass samples:
* `greengrass_classification_sample.py`_
@@ -48,8 +54,8 @@ We provide the following AWS Greengrass samples:
coordinates on AWS IoT Cloud every second.
Installing |CL| on the edge device
**********************************
Install the OS on the edge device
*********************************
Start with a clean installation of |CL| on a new system, using the
:ref:`bare-metal-install`, found in :ref:`get-started`.
@@ -70,7 +76,7 @@ Greengrass user below).
passwd <userid>
#. Next, enable the :command:`sudo` command for your new ``<userid>``. Add
``<userid>`` to the ``wheel`` group:
``<userid>`` to the *wheel* group:
.. code-block:: bash
@@ -90,7 +96,7 @@ Greengrass user below).
Add required bundles
====================
Use the ``swupd`` software updater utility to add the prerequisite bundles
Use the :command:`swupd` software updater utility to add the prerequisite bundles
for the OpenVINO software stack:
.. code-block:: bash
@@ -101,20 +107,20 @@ for the OpenVINO software stack:
Learn more about how to :ref:`swupd-guide`.
The ``computer-vision-basic`` bundle installs the OpenVINO™ toolkit,
along with the edge device models needed.
The :command:`computer-vision-basic` bundle installs the OpenVINO™ toolkit,
and the sample models optimized for Intel® edge platforms.
Converting Deep Learning Models
===============================
Convert deep learning models
============================
Locate Sample Models
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.
To complete this tutorial using an image classification model,
download the BVLC Alexnet model files `bvlc_alexnet.caffemodel`_ and `deploy.prototxt`_
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
classification sample.
@@ -124,12 +130,12 @@ are included with the computer-vision-basic bundle installation at :file:`/usr/s
These models are provided as an example; however, you may also use a custom SSD model
with the Greengrass object detection sample.
Running Model Optimizer
-----------------------
Run model optimizer
-------------------
Follow these instructions for `converting deep learning models to Intermediate Representation using Model Optimizer`_. To optimize either of the sample models described above, run one of the following commands.
For classification using BVLC Alexnet model:
For classification using BVLC AlexNet model:
.. code-block:: bash
@@ -156,33 +162,33 @@ In these examples:
* ``<output_dir>`` is the directory where the Intermediate Representation
(IR) is stored. IR contains .xml format corresponding to the network
structure and .bin format corresponding to weights. This .xml file should be
passed to <PARAM_MODEL_XML>.
passed to :command:`<PARAM_MODEL_XML>`.
* In the BVLC Alexnet model, the prototxt defines the input shape with
* In the BVLC AlexNet model, the prototxt defines the input shape with
batch size 10 by default. In order to use any other batch size, the
entire input shape must be provided as an argument to the model
optimizer. For example, to use batch size 1, you must provide
“--input_shape [1,3,227,227]”.
optimizer. For example, to use batch size 1, you must provide:
``--input_shape [1,3,227,227]``
Configuring an AWS Greengrass group
===================================
Configure AWS Greengrass group
******************************
For each Intel® edge platform, you must create a new AWS Greengrass group
and install AWS Greengrass core software to establish the connection between
cloud and edge.
#. To create an AWS Greengrass group, follow the
`AWS Greengrass developer guide`_.
#. To create an AWS Greengrass group, follow the instructions in
`Configure AWS IoT Greengrass on AWS IoT`_.
#. To install and configure AWS Greengrass core on edge platform, follow
the instructions at `Start AWS Greengrass on the Core Device`_. In
step 8(b), download the x86_64 Ubuntu configuration of the AWS Greengrass
the instructions in `Start AWS Greengrass on the Core Device`_. In
step 8(b), download the x86_64 Ubuntu\* configuration of the AWS Greengrass
core software.
.. note::
You do not need to run the ``cgroupfs-mount.sh`` script in step #6
You do not need to run the :file:`cgroupfs-mount.sh` script in step #6
of Module 1 of the `AWS Greengrass developer guide`_ because this is
enabled already in |CL|.
@@ -191,13 +197,13 @@ cloud and edge.
.. note::
Security certificates are linked to your AWS* account.
Security certificates are linked to your AWS account.
Creating and Packaging Lambda Functions
=======================================
Create and package Lambda function
**********************************
#. Complete steps 1-4 of the tutorial at `Create and Package Lambda Function`_.
#. Complete steps 1-4 of the AWS Greengrass tutorial at `Create and Package a Lambda Function`_.
.. note::
@@ -205,7 +211,7 @@ Creating and Packaging Lambda Functions
environment on the edge device.
#. In step 5, replace greengrassHelloWorld.py with the classification or object detection
#. In step 5, replace :file:`greengrassHelloWorld.py` with the classification or object detection
Greengrass sample from `Edge-Analytics-Faas`_:
* Classification: `greengrass_classification_sample.py`_
@@ -228,36 +234,35 @@ Creating and Packaging Lambda Functions
zip -r greengrass_lambda.zip greengrasssdk
greengrass_object_detection_sample_ssd.py
#. Return to the AWS Documentation and follow steps 6-11 to `complete creating lambdas`_.
#. Return to the AWS documentation section called `Create and Package a Lambda Function`_
and complete the procedure.
.. note::
In step 9(a) of the AWS documentation, while uploading the zip file,
make sure to name the handler as below depending on the AWS Greengrass
sample you are using:
make sure to name the handler to one of the following, depending on the
AWS Greengrass sample you are using:
* greengrass_object_detection_sample_ssd.function_handler (or)
* greengrass_object_detection_sample_ssd.function_handler
* greengrass_classification_sample.function_handler
Deploying Lambdas
=================
Configuring the Lambda function
-------------------------------
Configure Lambda function
*************************
After creating the Greengrass group and the lambda function, start
configuring the lambda function for AWS Greengrass.
After creating the Greengrass group and the Lambda function, start
configuring the Lambda function for AWS Greengrass.
#. Follow steps 1-8 in `Configure the Lambda Function`_ of the AWS
#. Follow steps 1-8 in `Configure the Lambda Function for AWS IoT Greengrass`_ in the AWS
documentation.
#. In addition to the details mentioned in step 8, change the Memory limit
to 2048MB to accommodate large input video streams.
to 2048 MB to accommodate large input video streams.
#. Add the following environment variables as key-value pairs when editing
the lambda configuration and click on update:
the Lambda configuration and click on update:
.. list-table:: **Table 1. Environment Variables: Lambda Configuration**
.. list-table:: **Table 1. Environment variables: Lambda configuration**
:widths: 20 80
:header-rows: 1
@@ -283,99 +288,100 @@ configuring the lambda function for AWS Greengrass.
(e.g. 1 for top-1 result, 5 for top-5 results)
#. Add subscription to subscribe, or publish messages from AWS Greengrass
lambda function by following the steps 10-14 in `Configure the Lambda Function`_.
Lambda function by completing the procedure in `Configure the Lambda Function for AWS IoT Greengrass`_.
.. note::
The “Optional topic filter” field should be the topic
mentioned inside the lambda function.
The optional topic filter field is the topic mentioned inside the Lambda
function. In this tutorial, sample topics include the following:
:command:`openvino/ssd` or :command:`openvino/classification`
For example: openvino/ssd or openvino/classification
Add local resources
===================
Local Resources
---------------
#. Select `this link to add local resources and access privileges`_.
Refer to the AWS documentation for details about `local resources and access privileges`_.
The following table describes the local resources needed for the CPU:
The following table describes the local resources needed for the CPU:
.. list-table:: **Local Resources**
:widths: 20, 20, 20, 20
:header-rows: 1
.. list-table:: **Local resources**
:widths: 20, 20, 20, 20
:header-rows: 1
* - Name
- Resource type
- Local path
- Access
* - Name
- Resource type
- Local path
- Access
* - ModelDir
- Volume
- <MODEL_DIR> to be specified by user
- Read-Only
* - ModelDir
- Volume
- <MODEL_DIR> to be specified by user
- Read-Only
* - Webcam
- Device
- /dev/video0
- Read-Only
* - Webcam
- Device
- /dev/video0
- Read-Only
* - DataDir
- Volume
- <DATA_DIR> to be specified by user. Holds both input and output
data.
- Read and Write
* - DataDir
- Volume
- <DATA_DIR> to be specified by user. Holds both input and output
data.
- Read and Write
Deploy
------
Deploy Lambda function
**********************
To `deploy the lambda function to AWS Greengrass core device`_, select
“Deployments” on group page and follow the instructions.
Refer to the AWS documentation for instructions on how to
`deploy the lambda function to AWS Greengrass core device`_. Select
*Deployments* on the group page and follow the instructions.
Output Consumption
------------------
Output consumption
==================
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.
defined by the variable :command:`reporting_interval` in the AWS Greengrass samples.
a. IoT Cloud Output:
a. IoT cloud output:
This option is enabled by default in the AWS Greengrass samples using a
variable ``enable_iot_cloud_output``. You can use it to verify the lambda
This option is enabled by default in the AWS Greengrass samples using the
:command:`enable_iot_cloud_output` variable. You 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, topics
may include ‘openvino/classification’ for classification and ‘openvino/ssd’
may include :command:`openvino/classification` for classification and :command:`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
results such as bounding box coordinates of objects, class label, and
class confidence are published.
Follow the instructions here to `view the output on IoT cloud`_.
b. Kinesis Streaming:
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 cloud using Kinesis [3] streams when :command:`enable_kinesis_output` is set
to True. The edge devices act as data producers and continually push
processed data to the cloud. You must set up and specify
Kinesis stream name, Kinesis shard, and AWS region in the AWS Greengrass
samples.
c. Cloud Storage using AWS S3 Bucket:
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
When the :command:`enable_s3_jpeg_output` variable is set to True, it enables
uploading and storing processed frames (in jpeg format) in an AWS S3
bucket. You must 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:
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
When the :command:`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’.
:command:`PARAM_OUTPUT_DIRECTORY`.
References
-----------
**********
1. AWS Greengrass: https://aws.amazon.com/greengrass/
2. AWS Lambda: https://aws.amazon.com/lambda/
@@ -393,17 +399,15 @@ References
.. _converting deep learning models to Intermediate Representation using Model Optimizer: https://software.intel.com/en-us/articles/OpenVINO-ModelOptimizer
.. _AWS Greengrass developer guide: https://docs.aws.amazon.com/greengrass/latest/developerguide/gg-config.html
.. _AWS Greengrass Developer Guide: https://docs.aws.amazon.com/greengrass/latest/developerguide/what-is-gg.html
.. _Configure AWS IoT Greengrass on AWS IoT: https://docs.aws.amazon.com/greengrass/latest/developerguide/gg-config.html
.. _Start AWS Greengrass on the Core Device: https://docs.aws.amazon.com/greengrass/latest/developerguide/gg-device-start.html
.. _AWS Greengrass Core SDK: https://docs.aws.amazon.com/greengrass/latest/developerguide/create-lambda.html
.. _Configure the Lambda Function for AWS IoT Greengrass: https://docs.aws.amazon.com/greengrass/latest/developerguide/config-lambda.html
.. _complete creating lambdas: https://docs.aws.amazon.com/greengrass/latest/developerguide/create-lambda.html
.. _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
.. _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
@@ -413,4 +417,4 @@ References
.. _this link to add local resources and access privileges: https://docs.aws.amazon.com/greengrass/latest/developerguide/access-local-resources.html
.. _Create and Package Lambda Function: https://docs.aws.amazon.com/greengrass/latest/developerguide/create-lambda.html
.. _Create and Package a Lambda Function: https://docs.aws.amazon.com/greengrass/latest/developerguide/create-lambda.html