Requirements Parameters Input data Output data Usage Example Installing MXNet dependencies Logging and troubleshooting Licenses Changelog See also

Amazon IoT Greengrass Version 1 entered the extended life phase on June 30, 2023. For more information, see the Amazon IoT Greengrass V1 maintenance policy. After this date, Amazon IoT Greengrass V1 won't release updates that provide features, enhancements, bug fixes, or security patches. Devices that run on Amazon IoT Greengrass V1 won't be disrupted and will continue to operate and to connect to the cloud. We strongly recommend that you migrate to Amazon IoT Greengrass Version 2, which adds significant new features and support for additional platforms.

ML Image Classification connector

Warning

This connector has moved into the extended life phase, and Amazon IoT Greengrass won't release updates that provide features, enhancements to existing features, security patches, or bug fixes. For more information, see Amazon IoT Greengrass Version 1 maintenance policy.

The ML Image Classification connectors provide a machine learning (ML) inference service that runs on the Amazon IoT Greengrass core. This local inference service performs image classification using a model trained by the SageMaker image classification algorithm.

User-defined Lambda functions use the Amazon IoT Greengrass Machine Learning SDK to submit inference requests to the local inference service. The service runs inference locally and returns probabilities that the input image belongs to specific categories.

Amazon IoT Greengrass provides the following versions of this connector, which is available for multiple platforms.

Version 2

Connector	Description and ARN
ML Image Classification Aarch64 JTX2	Image classification inference service for NVIDIA Jetson TX2. Supports GPU acceleration. ARN: `arn:aws:greengrass:region::/connectors/ImageClassificationAarch64JTX2/versions/2`
ML Image Classification x86_64	Image classification inference service for x86_64 platforms. ARN: `arn:aws:greengrass:region::/connectors/ImageClassificationx86-64/versions/2`
ML Image Classification ARMv7	Image classification inference service for ARMv7 platforms. ARN: `arn:aws:greengrass:region::/connectors/ImageClassificationARMv7/versions/2`

Connector

Description and ARN

ML Image Classification Aarch64 JTX2

Image classification inference service for NVIDIA Jetson TX2. Supports GPU acceleration.

ARN: arn:aws:greengrass:region::/connectors/ImageClassificationAarch64JTX2/versions/2

ML Image Classification x86_64

Image classification inference service for x86_64 platforms.

ARN: arn:aws:greengrass:region::/connectors/ImageClassificationx86-64/versions/2

ML Image Classification ARMv7

Image classification inference service for ARMv7 platforms.

ARN: arn:aws:greengrass:region::/connectors/ImageClassificationARMv7/versions/2

Version 1

Connector	Description and ARN
ML Image Classification Aarch64 JTX2	Image classification inference service for NVIDIA Jetson TX2. Supports GPU acceleration. ARN: `arn:aws:greengrass:region::/connectors/ImageClassificationAarch64JTX2/versions/1`
ML Image Classification x86_64	Image classification inference service for x86_64 platforms. ARN: `arn:aws:greengrass:region::/connectors/ImageClassificationx86-64/versions/1`
ML Image Classification Armv7	Image classification inference service for Armv7 platforms. ARN: `arn:aws:greengrass:region::/connectors/ImageClassificationARMv7/versions/1`

Connector

Description and ARN

ML Image Classification Aarch64 JTX2

Image classification inference service for NVIDIA Jetson TX2. Supports GPU acceleration.

ARN: arn:aws:greengrass:region::/connectors/ImageClassificationAarch64JTX2/versions/1

ML Image Classification x86_64

Image classification inference service for x86_64 platforms.

ARN: arn:aws:greengrass:region::/connectors/ImageClassificationx86-64/versions/1

ML Image Classification Armv7

Image classification inference service for Armv7 platforms.

ARN: arn:aws:greengrass:region::/connectors/ImageClassificationARMv7/versions/1

For information about version changes, see the Changelog.

Requirements

These connectors have the following requirements:

Version 2

Amazon IoT Greengrass Core Software v1.9.3 or later.
Python version 3.7 or 3.8 installed on the core device and added to the PATH environment variable.
Note
To use Python 3.8, run the following command to create a symbolic link from the the default Python 3.7 installation folder to the installed Python 3.8 binaries.
```
sudo ln -s path-to-python-3.8/python3.8 /usr/bin/python3.7
```
This configures your device to meet the Python requirement for Amazon IoT Greengrass.
Dependencies for the Apache MXNet framework installed on the core device. For more information, see Installing MXNet dependencies on the Amazon IoT Greengrass core.
An ML resource in the Greengrass group that references an SageMaker model source. This model must be trained by the SageMaker image classification algorithm. For more information, see Image classification algorithm in the Amazon SageMaker Developer Guide.
The ML Feedback connector added to the Greengrass group and configured. This is required only if you want to use the connector to upload model input data and publish predictions to an MQTT topic.
The Greengrass group role configured to allow the sagemaker:DescribeTrainingJob action on the target training job, as shown in the following example IAM policy.
```
{
    "Version": "2012-10-17",
    "Statement": [
        {
            "Effect": "Allow",
            "Action": [
                "sagemaker:DescribeTrainingJob"
            ],
            "Resource": "arn:aws:sagemaker:region:account-id:training-job:training-job-name"
        }
    ]
}
```
For the group role requirement, you must configure the role to grant the required permissions and make sure the role has been added to the group. For more information, see Managing the Greengrass group role (console) or Managing the Greengrass group role (CLI).

You can grant granular or conditional access to resources (for example, by using a wildcard * naming scheme). If you change the target training job in the future, make sure to update the group role.
Amazon IoT Greengrass Machine Learning SDK v1.1.0 is required to interact with this connector.

Version 1

Amazon IoT Greengrass Core Software v1.7 or later.
Python version 2.7 installed on the core device and added to the PATH environment variable.
Dependencies for the Apache MXNet framework installed on the core device. For more information, see Installing MXNet dependencies on the Amazon IoT Greengrass core.
An ML resource in the Greengrass group that references an SageMaker model source. This model must be trained by the SageMaker image classification algorithm. For more information, see Image classification algorithm in the Amazon SageMaker Developer Guide.
The Greengrass group role configured to allow the sagemaker:DescribeTrainingJob action on the target training job, as shown in the following example IAM policy.
```
{
    "Version": "2012-10-17",
    "Statement": [
        {
            "Effect": "Allow",
            "Action": [
                "sagemaker:DescribeTrainingJob"
            ],
            "Resource": "arn:aws:sagemaker:region:account-id:training-job:training-job-name"
        }
    ]
}
```
For the group role requirement, you must configure the role to grant the required permissions and make sure the role has been added to the group. For more information, see Managing the Greengrass group role (console) or Managing the Greengrass group role (CLI).

You can grant granular or conditional access to resources (for example, by using a wildcard * naming scheme). If you change the target training job in the future, make sure to update the group role.
Amazon IoT Greengrass Machine Learning SDK v1.0.0 or later is required to interact with this connector.

Connector Parameters

These connectors provide the following parameters.

Version 2

MLModelDestinationPath

The absolute local path of the ML resource inside the Lambda environment. This is the destination path that's specified for the ML resource.

Note

If you created the ML resource in the console, this is the local path.

Display name in the Amazon IoT console: Model destination path

Required: true

Type: string

Valid pattern: .+

MLModelResourceId

The ID of the ML resource that references the source model.

Display name in the Amazon IoT console: SageMaker job ARN resource

Required: true

Type: string

Valid pattern: [a-zA-Z0-9:_-]+

MLModelSageMakerJobArn

The ARN of the SageMaker training job that represents the SageMaker model source. The model must be trained by the SageMaker image classification algorithm.

Display name in the Amazon IoT console: SageMaker job ARN

Required: true

Type: string

Valid pattern: ^arn:aws:sagemaker:[a-zA-Z0-9-]+:[0-9]+:training-job/[a-zA-Z0-9][a-zA-Z0-9-]+$

LocalInferenceServiceName

The name for the local inference service. User-defined Lambda functions invoke the service by passing the name to the invoke_inference_service function of the Amazon IoT Greengrass Machine Learning SDK. For an example, see Usage Example.

Display name in the Amazon IoT console: Local inference service name

Required: true

Type: string

Valid pattern: [a-zA-Z0-9][a-zA-Z0-9-]{1,62}

LocalInferenceServiceTimeoutSeconds

The amount of time (in seconds) before the inference request is terminated. The minimum value is 1.

Display name in the Amazon IoT console: Timeout (second)

Required: true

Type: string

Valid pattern: [1-9][0-9]*

LocalInferenceServiceMemoryLimitKB

The amount of memory (in KB) that the service has access to. The minimum value is 1.

Display name in the Amazon IoT console: Memory limit (KB)

Required: true

Type: string

Valid pattern: [1-9][0-9]*

GPUAcceleration

The CPU or GPU (accelerated) computing context. This property applies to the ML Image Classification Aarch64 JTX2 connector only.

Display name in the Amazon IoT console: GPU acceleration

Required: true

Type: string

Valid values: CPU or GPU

MLFeedbackConnectorConfigId

The ID of the feedback configuration to use to upload model input data. This must match the ID of a feedback configuration defined for the ML Feedback connector.

This parameter is required only if you want to use the ML Feedback connector to upload model input data and publish predictions to an MQTT topic.

Display name in the Amazon IoT console: ML Feedback connector configuration ID

Required: false

Type: string

Valid pattern: ^$|^[a-zA-Z0-9][a-zA-Z0-9-]{1,62}$

Version 1

MLModelDestinationPath

The absolute local path of the ML resource inside the Lambda environment. This is the destination path that's specified for the ML resource.

Note

If you created the ML resource in the console, this is the local path.

Display name in the Amazon IoT console: Model destination path

Required: true

Type: string

Valid pattern: .+

MLModelResourceId

The ID of the ML resource that references the source model.

Display name in the Amazon IoT console: SageMaker job ARN resource

Required: true

Type: string

Valid pattern: [a-zA-Z0-9:_-]+

MLModelSageMakerJobArn

The ARN of the SageMaker training job that represents the SageMaker model source. The model must be trained by the SageMaker image classification algorithm.

Display name in the Amazon IoT console: SageMaker job ARN

Required: true

Type: string

Valid pattern: ^arn:aws:sagemaker:[a-zA-Z0-9-]+:[0-9]+:training-job/[a-zA-Z0-9][a-zA-Z0-9-]+$

LocalInferenceServiceName

Display name in the Amazon IoT console: Local inference service name

Required: true

Type: string

Valid pattern: [a-zA-Z0-9][a-zA-Z0-9-]{1,62}

LocalInferenceServiceTimeoutSeconds

The amount of time (in seconds) before the inference request is terminated. The minimum value is 1.

Display name in the Amazon IoT console: Timeout (second)

Required: true

Type: string

Valid pattern: [1-9][0-9]*

LocalInferenceServiceMemoryLimitKB

The amount of memory (in KB) that the service has access to. The minimum value is 1.

Display name in the Amazon IoT console: Memory limit (KB)

Required: true

Type: string

Valid pattern: [1-9][0-9]*

GPUAcceleration

The CPU or GPU (accelerated) computing context. This property applies to the ML Image Classification Aarch64 JTX2 connector only.

Display name in the Amazon IoT console: GPU acceleration

Required: true

Type: string

Valid values: CPU or GPU

Create Connector Example (Amazon CLI)

The following CLI commands create a ConnectorDefinition with an initial version that contains an ML Image Classification connector.

Example: CPU Instance

This example creates an instance of the ML Image Classification Armv7l connector.


aws greengrass create-connector-definition --name MyGreengrassConnectors --initial-version '{
    "Connectors": [
        {
            "Id": "MyImageClassificationConnector",
            "ConnectorArn": "arn:aws:greengrass:region::/connectors/ImageClassificationARMv7/versions/2",
            "Parameters": {
                "MLModelDestinationPath": "/path-to-model",
                "MLModelResourceId": "my-ml-resource",
                "MLModelSageMakerJobArn": "arn:aws:sagemaker:us-west-2:123456789012:training-job:MyImageClassifier",
                "LocalInferenceServiceName": "imageClassification",
                "LocalInferenceServiceTimeoutSeconds": "10",
                "LocalInferenceServiceMemoryLimitKB": "500000",
                "MLFeedbackConnectorConfigId": "MyConfig0"
            }
        }
    ]
}'

Example: GPU Instance

This example creates an instance of the ML Image Classification Aarch64 JTX2 connector, which supports GPU acceleration on an NVIDIA Jetson TX2 board.


aws greengrass create-connector-definition --name MyGreengrassConnectors --initial-version '{
    "Connectors": [
        {
            "Id": "MyImageClassificationConnector",
            "ConnectorArn": "arn:aws:greengrass:region::/connectors/ImageClassificationAarch64JTX2/versions/2",
            "Parameters": {
                "MLModelDestinationPath": "/path-to-model",
                "MLModelResourceId": "my-ml-resource",
                "MLModelSageMakerJobArn": "arn:aws:sagemaker:us-west-2:123456789012:training-job:MyImageClassifier",
                "LocalInferenceServiceName": "imageClassification",
                "LocalInferenceServiceTimeoutSeconds": "10",
                "LocalInferenceServiceMemoryLimitKB": "500000",
                "GPUAcceleration": "GPU",
                "MLFeedbackConnectorConfigId": "MyConfig0"
            }
        }
    ]
}'

Note

The Lambda function in these connectors have a long-lived lifecycle.

In the Amazon IoT Greengrass console, you can add a connector from the group's Connectors page. For more information, see Getting started with Greengrass connectors (console).

Input data

These connectors accept an image file as input. Input image files must be in jpeg or png format. For more information, see Usage Example.

These connectors don't accept MQTT messages as input data.

Output data

These connectors return a formatted prediction for the object identified in the input image:


[0.3,0.1,0.04,...]

The prediction contains a list of values that correspond with the categories used in the training dataset during model training. Each value represents the probability that the image falls under the corresponding category. The category with the highest probability is the dominant prediction.

These connectors don't publish MQTT messages as output data.

Usage Example

The following example Lambda function uses the Amazon IoT Greengrass Machine Learning SDK to interact with an ML Image Classification connector.

Note

You can download the SDK from the Amazon IoT Greengrass Machine Learning SDK downloads page.

The example initializes an SDK client and synchronously calls the SDK's invoke_inference_service function to invoke the local inference service. It passes in the algorithm type, service name, image type, and image content. Then, the example parses the service response to get the probability results (predictions).

Python 3.7


import logging
from threading import Timer

import numpy as np

import greengrass_machine_learning_sdk as ml

# We assume the inference input image is provided as a local file
# to this inference client Lambda function.
with open('/test_img/test.jpg', 'rb') as f:
    content = bytearray(f.read())

client = ml.client('inference')

def infer():
    logging.info('invoking Greengrass ML Inference service')

    try:
        resp = client.invoke_inference_service(
            AlgoType='image-classification',
            ServiceName='imageClassification',
            ContentType='image/jpeg',
            Body=content
        )
    except ml.GreengrassInferenceException as e:
        logging.info('inference exception {}("{}")'.format(e.__class__.__name__, e))
        return
    except ml.GreengrassDependencyException as e:
        logging.info('dependency exception {}("{}")'.format(e.__class__.__name__, e))
        return

    logging.info('resp: {}'.format(resp))
    predictions = resp['Body'].read().decode("utf-8")
    logging.info('predictions: {}'.format(predictions))
    
    # The connector output is in the format: [0.3,0.1,0.04,...]
    # Remove the '[' and ']' at the beginning and end.
    predictions = predictions[1:-1]
    count = len(predictions.split(','))
    predictions_arr = np.fromstring(predictions, count=count, sep=',')

    # Perform business logic that relies on the predictions_arr, which is an array
    # of probabilities.
    
    # Schedule the infer() function to run again in one second.
    Timer(1, infer).start()
    return

infer()

def function_handler(event, context):
    return

Python 2.7


import logging
from threading import Timer

import numpy

import greengrass_machine_learning_sdk as gg_ml

# The inference input image.
with open("/test_img/test.jpg", "rb") as f:
    content = f.read()

client = gg_ml.client("inference")


def infer():
    logging.info("Invoking Greengrass ML Inference service")

    try:
        resp = client.invoke_inference_service(
            AlgoType="image-classification",
            ServiceName="imageClassification",
            ContentType="image/jpeg",
            Body=content,
        )
    except gg_ml.GreengrassInferenceException as e:
        logging.info('Inference exception %s("%s")', e.__class__.__name__, e)
        return
    except gg_ml.GreengrassDependencyException as e:
        logging.info('Dependency exception %s("%s")', e.__class__.__name__, e)
        return

    logging.info("Response: %s", resp)
    predictions = resp["Body"].read()
    logging.info("Predictions: %s", predictions)

    # The connector output is in the format: [0.3,0.1,0.04,...]
    # Remove the '[' and ']' at the beginning and end.
    predictions = predictions[1:-1]
    predictions_arr = numpy.fromstring(predictions, sep=",")
    logging.info("Split into %s predictions.", len(predictions_arr))

    # Perform business logic that relies on predictions_arr, which is an array
    # of probabilities.

    # Schedule the infer() function to run again in one second.
    Timer(1, infer).start()


infer()


# In this example, the required AWS Lambda handler is never called.
def function_handler(event, context):
    return

The invoke_inference_service function in the Amazon IoT Greengrass Machine Learning SDK accepts the following arguments.

Argument	Description
`AlgoType`	The name of the algorithm type to use for inference. Currently, only `image-classification` is supported. Required: `true` Type: `string` Valid values: `image-classification`
`ServiceName`	The name of the local inference service. Use the name that you specified for the `LocalInferenceServiceName` parameter when you configured the connector. Required: `true` Type: `string`
`ContentType`	The mime type of the input image. Required: `true` Type: `string` Valid values: `image/jpeg, image/png`
`Body`	The content of the input image file. Required: `true` Type: `binary`

Installing MXNet dependencies on the Amazon IoT Greengrass core

To use an ML Image Classification connector, you must install the dependencies for the Apache MXNet framework on the core device. The connectors use the framework to serve the ML model.

Note

These connectors are bundled with a precompiled MXNet library, so you don't need to install the MXNet framework on the core device.

Amazon IoT Greengrass provides scripts to install the dependencies for the following common platforms and devices (or to use as a reference for installing them). If you're using a different platform or device, see the MXNet documentation for your configuration.

Before installing the MXNet dependencies, make sure that the required system libraries (with the specified minimum versions) are present on the device.

NVIDIA Jetson TX2

Install CUDA Toolkit 9.0 and cuDNN 7.0. You can follow the instructions in Setting up other devices in the Getting Started tutorial.
Enable universe repositories so the connector can install community-maintained open software. For more information, see Repositories/Ubuntu in the Ubuntu documentation.
1. Open the /etc/apt/sources.list file.
2. Make sure that the following lines are uncommented.
```
deb http://ports.ubuntu.com/ubuntu-ports/ xenial universe
deb-src http://ports.ubuntu.com/ubuntu-ports/ xenial universe
deb http://ports.ubuntu.com/ubuntu-ports/ xenial-updates universe
deb-src http://ports.ubuntu.com/ubuntu-ports/ xenial-updates universe
```

Save a copy of the following installation script to a file named nvidiajtx2.sh on the core device.

Python 3.7Python 2.7

Python 3.7


#!/bin/bash
set -e

echo "Installing dependencies on the system..."
echo 'Assuming that universe repos are enabled and checking dependencies...'
apt-get -y update
apt-get -y dist-upgrade
apt-get install -y liblapack3 libopenblas-dev liblapack-dev libatlas-base-dev
apt-get install -y python3.7 python3.7-dev

python3.7 -m pip install --upgrade pip
python3.7 -m pip install numpy==1.15.0
python3.7 -m pip install opencv-python || echo 'Error: Unable to install OpenCV with pip on this platform. Try building the latest OpenCV from source (https://github.com/opencv/opencv).'

echo 'Dependency installation/upgrade complete.'

Note

If OpenCV does not install successfully using this script, you can try building from source. For more information, see Installation in Linux in the OpenCV documentation, or refer to other online resources for your platform.

Python 2.7


#!/bin/bash
set -e

echo "Installing dependencies on the system..."
echo 'Assuming that universe repos are enabled and checking dependencies...'
apt-get -y update
apt-get -y dist-upgrade
apt-get install -y liblapack3 libopenblas-dev liblapack-dev libatlas-base-dev python-dev

echo 'Install latest pip...'
wget https://bootstrap.pypa.io/get-pip.py
python get-pip.py
rm get-pip.py

pip install numpy==1.15.0 scipy

echo 'Dependency installation/upgrade complete.'

From the directory where you saved the file, run the following command:
```
sudo nvidiajtx2.sh
```

x86_64 (Ubuntu or Amazon Linux)

Save a copy of the following installation script to a file named x86_64.sh on the core device.

Python 3.7Python 2.7

Python 3.7


#!/bin/bash
set -e

echo "Installing dependencies on the system..."

release=$(awk -F= '/^NAME/{print $2}' /etc/os-release)

if [ "$release" == '"Ubuntu"' ]; then
  # Ubuntu. Supports EC2 and DeepLens. DeepLens has all the dependencies installed, so
  # this is mostly to prepare dependencies on Ubuntu EC2 instance.
  apt-get -y update
  apt-get -y dist-upgrade

  apt-get install -y libgfortran3 libsm6 libxext6 libxrender1
  apt-get install -y python3.7 python3.7-dev
elif [ "$release" == '"Amazon Linux"' ]; then
  # Amazon Linux. Expect python to be installed already
  yum -y update
  yum -y upgrade

  yum install -y compat-gcc-48-libgfortran libSM libXrender libXext
else
  echo "OS Release not supported: $release"
  exit 1
fi

python3.7 -m pip install --upgrade pip
python3.7 -m pip install numpy==1.15.0
python3.7 -m pip install opencv-python || echo 'Error: Unable to install OpenCV with pip on this platform. Try building the latest OpenCV from source (https://github.com/opencv/opencv).'

echo 'Dependency installation/upgrade complete.'

Note

Python 2.7


#!/bin/bash
set -e

echo "Installing dependencies on the system..."

release=$(awk -F= '/^NAME/{print $2}' /etc/os-release)

if [ "$release" == '"Ubuntu"' ]; then
  # Ubuntu. Supports EC2 and DeepLens. DeepLens has all the dependencies installed, so
  # this is mostly to prepare dependencies on Ubuntu EC2 instance.
  apt-get -y update
  apt-get -y dist-upgrade

  apt-get install -y libgfortran3 libsm6 libxext6 libxrender1 python-dev python-pip
elif [ "$release" == '"Amazon Linux"' ]; then
  # Amazon Linux. Expect python to be installed already
  yum -y update
  yum -y upgrade

  yum install -y compat-gcc-48-libgfortran libSM libXrender libXext python-pip
else
  echo "OS Release not supported: $release"
  exit 1
fi

pip install numpy==1.15.0 scipy opencv-python

echo 'Dependency installation/upgrade complete.'

From the directory where you saved the file, run the following command:
```
sudo x86_64.sh
```

Armv7 (Raspberry Pi)

Save a copy of the following installation script to a file named armv7l.sh on the core device.

Python 3.7Python 2.7

Python 3.7


#!/bin/bash
set -e

echo "Installing dependencies on the system..."

apt-get update
apt-get -y upgrade

apt-get install -y liblapack3 libopenblas-dev liblapack-dev
apt-get install -y python3.7 python3.7-dev

python3.7 -m pip install --upgrade pip
python3.7 -m pip install numpy==1.15.0
python3.7 -m pip install opencv-python || echo 'Error: Unable to install OpenCV with pip on this platform. Try building the latest OpenCV from source (https://github.com/opencv/opencv).'

echo 'Dependency installation/upgrade complete.'

Note

Python 2.7


#!/bin/bash
set -e

echo "Installing dependencies on the system..."

apt-get update
apt-get -y upgrade

apt-get install -y liblapack3 libopenblas-dev liblapack-dev python-dev

# python-opencv depends on python-numpy. The latest version in the APT repository is python-numpy-1.8.2
# This script installs python-numpy first so that python-opencv can be installed, and then install the latest
# numpy-1.15.x with pip
apt-get install -y python-numpy python-opencv
dpkg --remove --force-depends python-numpy

echo 'Install latest pip...'
wget https://bootstrap.pypa.io/get-pip.py
python get-pip.py
rm get-pip.py

pip install --upgrade numpy==1.15.0 picamera scipy

echo 'Dependency installation/upgrade complete.'

From the directory where you saved the file, run the following command:
```
sudo bash armv7l.sh
```
Note
On a Raspberry Pi, using pip to install machine learning dependencies is a memory-intensive operation that can cause the device to run out of memory and become unresponsive. As a workaround, you can temporarily increase the swap size:
In /etc/dphys-swapfile, increase the value of the CONF_SWAPSIZE variable and then run the following command to restart dphys-swapfile.
```
/etc/init.d/dphys-swapfile restart
```

Logging and troubleshooting

Depending on your group settings, event and error logs are written to CloudWatch Logs, the local file system, or both. Logs from this connector use the prefix LocalInferenceServiceName. If the connector behaves unexpectedly, check the connector's logs. These usually contain useful debugging information, such as a missing ML library dependency or the cause of a connector startup failure.

If the Amazon IoT Greengrass group is configured to write local logs, the connector writes log files to greengrass-root/ggc/var/log/user/region/aws/. For more information about Greengrass logging, see Monitoring with Amazon IoT Greengrass logs.

Use the following information to help troubleshoot issues with the ML Image Classification connectors.

Required system libraries

The following tabs list the system libraries required for each ML Image Classification connector.

ML Image Classification Aarch64 JTX2

Library	Minimum version
ld-linux-aarch64.so.1	GLIBC_2.17
libc.so.6	GLIBC_2.17
libcublas.so.9.0	not applicable
libcudart.so.9.0	not applicable
libcudnn.so.7	not applicable
libcufft.so.9.0	not applicable
libcurand.so.9.0	not applicable
libcusolver.so.9.0	not applicable
libgcc_s.so.1	GCC_4.2.0
libgomp.so.1	GOMP_4.0, OMP_1.0
libm.so.6	GLIBC_2.23
libpthread.so.0	GLIBC_2.17
librt.so.1	GLIBC_2.17
libstdc++.so.6	GLIBCXX_3.4.21, CXXABI_1.3.8

ML Image Classification x86_64

Library	Minimum version
ld-linux-x86-64.so.2	GCC_4.0.0
libc.so.6	GLIBC_2.4
libgfortran.so.3	GFORTRAN_1.0
libm.so.6	GLIBC_2.23
libpthread.so.0	GLIBC_2.2.5
librt.so.1	GLIBC_2.2.5
libstdc++.so.6	CXXABI_1.3.8, GLIBCXX_3.4.21

ML Image Classification Armv7

Library	Minimum version
ld-linux-armhf.so.3	GLIBC_2.4
libc.so.6	GLIBC_2.7
libgcc_s.so.1	GCC_4.0.0
libgfortran.so.3	GFORTRAN_1.0
libm.so.6	GLIBC_2.4
libpthread.so.0	GLIBC_2.4
librt.so.1	GLIBC_2.4
libstdc++.so.6	CXXABI_1.3.8, CXXABI_ARM_1.3.3, GLIBCXX_3.4.20

Issues

Symptom Solution

Symptom	Solution
On a Raspberry Pi, the following error message is logged and you are not using the camera: `Failed to initialize libdc1394`	Run the following command to disable the driver: `sudo ln /dev/null /dev/raw1394` This operation is ephemeral and the symbolic link will disappear after rebooting. Consult the manual of your OS distribution to learn how to automatically create the link up on reboot.

On a Raspberry Pi, the following error message is logged and you are not using the camera: Failed to initialize libdc1394

Run the following command to disable the driver:


sudo ln /dev/null /dev/raw1394

This operation is ephemeral and the symbolic link will disappear after rebooting. Consult the manual of your OS distribution to learn how to automatically create the link up on reboot.

Licenses

The ML Image Classification connectors includes the following third-party software/licensing:

Amazon SDK for Python (Boto3)/Apache License 2.0
botocore/Apache License 2.0
dateutil/PSF License
docutils/BSD License, GNU General Public License (GPL), Python Software Foundation License, Public Domain
jmespath/MIT License
s3transfer/Apache License 2.0
urllib3/MIT License

Deep Neural Network Library (DNNL)/Apache License 2.0
OpenMP* Runtime Library/See Intel OpenMP Runtime Library licensing.
mxnet/Apache License 2.0
six/MIT

Intel OpenMP Runtime Library licensing. The Intel® OpenMP* runtime is dual-licensed, with a commercial (COM) license as part of the Intel® Parallel Studio XE Suite products, and a BSD open source (OSS) license.

This connector is released under the Greengrass Core Software License Agreement.

Changelog

The following table describes the changes in each version of the connector.

Version	Changes
2	Added the `MLFeedbackConnectorConfigId` parameter to support the use of the ML Feedback connector to upload model input data, publish predictions to an MQTT topic, and publish metrics to Amazon CloudWatch.
1	Initial release.

A Greengrass group can contain only one version of the connector at a time. For information about upgrading a connector version, see Upgrading connector versions.