How to configure machine learning inference using the AWS Management Console - AWS IoT Greengrass

How to configure machine learning inference using the AWS Management Console

To follow the steps in this tutorial, you must be using AWS IoT Greengrass Core v1.10 or later.

You can perform machine learning (ML) inference locally on a Greengrass core device using locally generated data. For information, including requirements and constraints, see Perform machine learning inference.

This tutorial describes how to use the AWS Management Console to configure a Greengrass group to run a Lambda inference app that recognizes images from a camera locally, without sending data to the cloud. The inference app accesses the camera module on a Raspberry Pi and runs inference using the open source SqueezeNet model.

The tutorial contains the following high-level steps:

Prerequisites

To complete this tutorial, you need:

Note

This tutorial uses a Raspberry Pi, but AWS IoT Greengrass supports other platforms, such as Intel Atom and NVIDIA Jetson TX2. In the example for Jetson TX2, you can use static images instead of images streamed from a camera. If using the Jetson TX2 example, you might need to install Python 3.6 instead of Python 3.7. For information about configuring your device so you can install the AWS IoT Greengrass Core software, see Setting up other devices.

For third party platforms that AWS IoT Greengrass does not support, you must run your Lambda function in non-containerized mode. To run in non-containerized mode, you must run your Lambda function as root. For more information, see Considerations when choosing Lambda function containerization and Setting the default access identity for Lambda functions in a group.

Step 1: Configure the Raspberry Pi

In this step, install updates to the Raspbian operating system, install the camera module software and Python dependencies, and enable the camera interface.

Run the following commands in your Raspberry Pi terminal.

  1. Install updates to Raspbian.

    sudo apt-get update sudo apt-get dist-upgrade
  2. Install the picamera interface for the camera module and other Python libraries that are required for this tutorial.

    sudo apt-get install -y python3-dev python3-setuptools python3-pip python3-picamera

    Validate the installation:

    • Make sure that your Python 3.7 installation includes pip.

      python3 -m pip

      If pip isn't installed, download it from the pip website and then run the following command.

      python3 get-pip.py
    • Make sure that your Python version is 3.7 or higher.

      python3 --version

      If the output lists an earlier version, run the following command.

      sudo apt-get install -y python3.7-dev
    • Make sure that Setuptools and Picamera installed successfully.

      sudo -u ggc_user bash -c 'python3 -c "import setuptools"' sudo -u ggc_user bash -c 'python3 -c "import picamera"'

      If the output doesn't contain errors, the validation is successful.

    Note

    If the Python executable installed on your device is python3.7, use python3.7 instead of python3 for the commands in this tutorial. Make sure that your pip installation maps to the correct python3.7 or python3 version to avoid dependency errors.

  3. Reboot the Raspberry Pi.

    sudo reboot
  4. Open the Raspberry Pi configuration tool.

    sudo raspi-config
  5. Use the arrow keys to open Interfacing Options and enable the camera interface. If prompted, allow the device to reboot.

  6. Use the following command to test the camera setup.

    raspistill -v -o test.jpg

    This opens a preview window on the Raspberry Pi, saves a picture named test.jpg to your current directory, and displays information about the camera in the Raspberry Pi terminal.

Step 2: Install the MXNet framework

In this step, install MXNet libraries on your Raspberry Pi.

  1. Sign in to your Raspberry Pi remotely.

    ssh pi@your-device-ip-address
  2. Open the MXNet documentation, open Installing MXNet, and follow the instructions to install MXNet on the device.

    Note

    We recommend installing version 1.5.0 and building MXNet from source for this tutorial to avoid device conflicts.

  3. After you install MXNet, validate the following configuration:

    • Make sure the ggc_user system account can use the MXNet framework.

      sudo -u ggc_user bash -c 'python3 -c "import mxnet"'
    • Make sure NumPy is installed.

      sudo -u ggc_user bash -c 'python3 -c "import numpy"'

Step 3: Create an MXNet model package

In this step, create a model package that contains a sample pretrained MXNet model to upload to Amazon S3. AWS IoT Greengrass can use a model package from Amazon S3, provided that you use the tar.gz or zip format.

  1. On your computer, download the MXNet sample for Raspberry Pi from Machine learning samples.

  2. Unzip the downloaded mxnet-py3-armv7l.tar.gz file.

  3. Navigate to the squeezenet directory.

    cd path-to-downloaded-sample/mxnet-py3-armv7l/models/squeezenet

    The squeezenet.zip file in this directory is your model package. It contains SqueezeNet open source model artifacts for an image classification model. Later, you upload this model package to Amazon S3.

Step 4: Create and publish a Lambda function

In this step, create a Lambda function deployment package and Lambda function. Then, publish a function version and create an alias.

First, create the Lambda function deployment package.

  1. On your computer, navigate to the examples directory in the sample package that you unzipped in Step 3: Create an MXNet model package.

    cd path-to-downloaded-sample/mxnet-py3-armv7l/examples

    The examples directory contains function code and dependencies.

    • greengrassObjectClassification.py is the inference code used in this tutorial. You can use this code as a template to create your own inference function.

    • greengrasssdk is version 1.5.0 of the AWS IoT Greengrass Core SDK for Python.

      Note

      If a new version is available, you can download it and upgrade the SDK version in your deployment package. For more information, see AWS IoT Greengrass Core SDK for Python on GitHub.

  2. Compress the contents of the examples directory into a file named greengrassObjectClassification.zip. This is your deployment package.

    zip -r greengrassObjectClassification.zip .
    Note

    Make sure the .py files and dependencies are in the root of the directory.

     

    Next, create the Lambda function.

  3. In the AWS IoT console, in the navigation pane, choose Greengrass, and then choose Groups.

    
            The navigation pane in the AWS IoT console with Groups highlighted.
  4. Choose the Greengrass group where you want to add the Lambda function.

  5. On the group configuration page, choose Lambdas, and then choose Add Lambda.

    
            The group page with Lambdas and Add Lambda highlighted.
  6. On the Add a Lambda to your Greengrass Group page, choose Create new Lambda. This opens the AWS Lambda console.

    
            The Add a Lambda to your Greengrass Group page with Create new Lambda highlighted.
  7. Choose Author from scratch and use the following values to create your function:

    • For Function name, enter greengrassObjectClassification.

    • For Runtime, choose Python 3.7.

    For Permissions, keep the default setting. This creates an execution role that grants basic Lambda permissions. This role isn't used by AWS IoT Greengrass.

  8. Choose Create function.

    
            The Create function page with Create function highlighted.

     

    Now, upload your Lambda function deployment package and register the handler.

  9. On the Configuration tab for the greengrassObjectClassification function, for Function code, use the following values:

    • For Code entry type, choose Upload a .zip file.

    • For Runtime, choose Python 3.7.

    • For Handler, enter greengrassObjectClassification.function_handler.

  10. Choose Upload.

    
            The Function code section with Upload highlighted.
  11. Choose your greengrassObjectClassification.zip deployment package.

  12. Choose Save.

     

    Next, publish the first version of your Lambda function. Then, create an alias for the version.

    Note

    Greengrass groups can reference a Lambda function by alias (recommended) or by version. Using an alias makes it easier to manage code updates because you don't have to change your subscription table or group definition when the function code is updated. Instead, you just point the alias to the new function version.

  13. From the Actions menu, choose Publish new version.

    
            The Publish new version option in the Actions menu.
  14. For Version description, enter First version, and then choose Publish.

  15. On the greengrassObjectClassification: 1 configuration page, from the Actions menu, choose Create alias.

    
            The Create alias option in the Actions menu.
  16. On the Create a new alias page, use the following values:

    • For Name, enter mlTest.

    • For Version, enter 1.

    Note

    AWS IoT Greengrass doesn't support Lambda aliases for $LATEST versions.

  17. Choose Create.

    
            The Create a new alias page with Create highlighted.

    Now, add the Lambda function to your Greengrass group.

Step 5: Add the Lambda function to the Greengrass group

In this step, add the Lambda function to the group and then configure its lifecycle and environment variables.

First, add the Lambda function to your Greengrass group.

  1. In the AWS IoT console, open the group configuration page.

  2. Choose Lambdas, and then choose Add Lambda.

    
            The group page with Lambdas and Add Lambda highlighted.
  3. On the Add a Lambda to your Greengrass Group page, choose Use existing Lambda.

    
            The Add a Lambda to your Greengrass Group page with Use existing Lambda highlighted.
  4. Choose greengrassObjectClassification, and then choose Next.

  5. On the Select a Lambda version page, choose Alias:mlTest, and then choose Finish.

     

    Next, configure the lifecycle and environment variables of the Lambda function.

  6. On the Lambdas page, choose the greengrassObjectClassification Lambda function.

    
            The Lambdas page with the greengrassObjectClassification Lambda function highlighted.
  7. On the greengrassObjectClassification configuration page, choose Edit.

  8. On the Group-specific Lambda configuration page, make the following updates.

    Note

    We recommend that you run your Lambda function without containerization unless your business case requires it. This helps enable access to your device GPU and camera without configuring device resources. If you run without containerization, you must also grant root access to your AWS IoT Greengrass Lambda functions.

    1. To run without containerization:

    2. To run in containerized mode instead:

      Note

      We do not recommend running in containerized mode unless your business case requires it.

      • For Run as, choose Use group default.

      • For Containerization, choose Use group default.

      • For Memory limit, enter 96 MB.

      • For Timeout, enter 10 seconds.

      • For Lambda lifecycle, choose Make this function long-lived and keep it running indefinitely.

        For more information, see Lifecycle configuration for Greengrass Lambda functions.

      • For Read access to /sys directory, choose Enable.

  9. Under Environment variables, create a key-value pair. A key-value pair is required by functions that interact with MXNet models on a Raspberry Pi.

    For the key, use MXNET_ENGINE_TYPE. For the value, use NaiveEngine.

    Note

    In your own user-defined Lambda functions, you can optionally set the environment variable in your function code.

  10. Keep the default values for all other properties and choose Update.

Step 6: Add resources to the Greengrass group

In this step, create resources for the camera module and the ML inference model and affiliate the resources with the Lambda function. This makes it possible for the Lambda function to access the resources on the core device.

Note

If you run in non-containerized mode, AWS IoT Greengrass can access your device GPU and camera without configuring these device resources.

First, create two local device resources for the camera: one for shared memory and one for the device interface. For more information about local resource access, see Access local resources with Lambda functions and connectors.

  1. On the group configuration page, choose Resources.

    
            The group configuration page with Resources highlighted.
  2. On the Local tab, choose Add a local resource.

  3. On the Create a local resource page, use the following values:

    • For Resource name, enter videoCoreSharedMemory.

    • For Resource type, choose Device.

    • For Device path, enter /dev/vcsm.

      The device path is the local absolute path of the device resource. This path can only refer to a character device or block device under /dev.

    • For Group owner file access permission, choose Automatically add OS group permissions of the Linux group that owns the resource.

      The Group owner file access permission option lets you grant additional file access permissions to the Lambda process. For more information, see Group owner file access permission.

    
            The Create a local resource page with edited resource properties.
  4. Under Lambda function affiliations, choose Select.

  5. Choose greengrassObjectClassification, choose Read and write access, and then choose Done.

    
            Lambda function affiliation properties with Done highlighted.

    Next, you add a local device resource for the camera interface.

  6. Choose Add another resource.

  7. On the Create a local resource page, use the following values:

    • For Resource name, enter videoCoreInterface.

    • For Resource type, choose Device.

    • For Device path, enter /dev/vchiq.

    • For Group owner file access permission, choose Automatically add OS group permissions of the Linux group that owns the resource.

    
            The Create a local resource page with edited resource properties.
  8. Under Lambda function affiliations, choose Select.

  9. Choose greengrassObjectClassification, choose Read and write access, and then choose Done.

  10. At the bottom of the page, choose Save.

 

Now, add the inference model as a machine learning resource. This step includes uploading the squeezenet.zip model package to Amazon S3.

  1. On the Resources page for your group, choose Machine Learning, and then choose Add a machine learning resource.

  2. On the Create a machine learning resource page, for Resource name, enter squeezenet_model.

    
            The Add Machine Learning Model page with updated properties.
  3. For Model source, choose Upload a model in S3.

  4. Under Model from S3, choose Select.

  5. Choose Upload a model. This opens up a new tab to the Amazon S3 console.

  6. In the Amazon S3 console tab, upload the squeezenet.zip file to an Amazon S3 bucket. For information, see How do I upload files and folders to an S3 Bucket? in the Amazon Simple Storage Service Console User Guide.

    Note

    For the bucket to be accessible, your bucket name must contain the string greengrass. Choose a unique name (such as greengrass-bucket-user-id-epoch-time). Don't use a period (.) in the bucket name.

  7. In the AWS IoT Greengrass console tab, locate and choose your Amazon S3 bucket. Locate your uploaded squeezenet.zip file, and choose Select. You might need to choose Refresh to update the list of available buckets and files.

  8. For Local path, enter /greengrass-machine-learning/mxnet/squeezenet.

    This is the destination for the local model in the Lambda runtime namespace. When you deploy the group, AWS IoT Greengrass retrieves the source model package and then extracts the contents to the specified directory. The sample Lambda function for this tutorial is already configured to use this path (in the model_path variable).

  9. Under Identify resource owner and set access permissions, choose No OS group.

  10. Under Lambda function affiliations, choose Select.

  11. Choose greengrassObjectClassification, choose Read-only access, and then choose Done.

  12. Choose Save.

Using Amazon SageMaker trained models

This tutorial uses a model that's stored in Amazon S3, but you can easily use Amazon SageMaker models too. The AWS IoT Greengrass console has built-in Amazon SageMaker integration, so you don't need to manually upload these models to Amazon S3. For requirements and limitations for using Amazon SageMaker models, see Supported model sources.

To use an Amazon SageMaker model:

  • For Model source, choose Use an existing SageMaker model, and then choose the name of the model's training job.

  • For Local path, enter the path to the directory where your Lambda function looks for the model.

Step 7: Add a subscription to the Greengrass group

In this step, add a subscription to the group. This subscription enables the Lambda function to send prediction results to AWS IoT by publishing to an MQTT topic.

  1. On the group configuration page, choose Subscriptions, and then choose Add Subscription.

    
            The group page with Subscriptions and Add Subscription highlighted.
  2. On the Select your source and target page, configure the source and target, as follows:

    1. In Select a source, choose Lambdas, and then choose greengrassObjectClassification.

    2. In Select a target, choose Services, and then choose IoT Cloud.

    3. Choose Next.

      
                The Select your source and target page with Next highlighted.
  3. On the Filter your data with a topic page, in Topic filter, enter hello/world, and then choose Next.

    
            The Filter your data with a topic page with Next highlighted.
  4. Choose Finish.

Step 8: Deploy the Greengrass group

In this step, deploy the current version of the group definition to the Greengrass core device. The definition contains the Lambda function, resources, and subscription configurations that you added.

  1. Make sure that the AWS IoT Greengrass core is running. Run the following commands in your Raspberry Pi terminal, as needed.

    1. To check whether the daemon is running:

      ps aux | grep -E 'greengrass.*daemon'

      If the output contains a root entry for /greengrass/ggc/packages/1.10.2/bin/daemon, then the daemon is running.

      Note

      The version in the path depends on the AWS IoT Greengrass Core software version that's installed on your core device.

    2. To start the daemon:

      cd /greengrass/ggc/core/ sudo ./greengrassd start
  2. On the group configuration page, choose Deployments, and from the Actions menu, choose Deploy.

    
            The group page with Deployments and Deploy highlighted.
  3. On the Configure how devices discover your core page, choose Automatic detection.

    This enables devices to automatically acquire connectivity information for the core, such as IP address, DNS, and port number. Automatic detection is recommended, but AWS IoT Greengrass also supports manually specified endpoints. You're only prompted for the discovery method the first time that the group is deployed.

    
            The Configure how devices discover your core page with Automatic detection highlighted.
    Note

    If prompted, grant permission to create the Greengrass service role and associate it with your AWS account in the current AWS Region. This role allows AWS IoT Greengrass to access your resources in AWS services.

    The Deployments page shows the deployment timestamp, version ID, and status. When completed, the status displayed for the deployment should be Successfully completed.

    For more information about deployments, see Deploy AWS IoT Greengrass groups to an AWS IoT Greengrass core. For troubleshooting help, see Troubleshooting AWS IoT Greengrass.

Step 9: Test the inference app

Now you can verify whether the deployment is configured correctly. To test, you subscribe to the hello/world topic and view the prediction results that are published by the Lambda function.

Note

If a monitor is attached to the Raspberry Pi, the live camera feed is displayed in a preview window.

  1. In the AWS IoT console, choose Test.

    
            The navigation pane in the AWS IoT console with Test highlighted.
  2. For Subscriptions, use the following values:

    • For the subscription topic, use hello/world.

    • For MQTT payload display,choose Display payloads as strings.

  3. Choose Subscribe to topic.

    If the test is successful, the messages from the Lambda function appear at the bottom of the page. Each message contains the top five prediction results of the image, using the format: probability, predicted class ID, and corresponding class name.

    
            The Subscriptions page showing test results with message data.

Troubleshooting AWS IoT Greengrass ML inference

If the test is not successful, you can try the following troubleshooting steps. Run the commands in your Raspberry Pi terminal.

Check error logs

  1. Switch to the root user and navigate to the log directory. Access to AWS IoT Greengrass logs requires root permissions.

    sudo su cd /greengrass/ggc/var/log
  2. In the system directory, check runtime.log or python_runtime.log.

    In the user/region/account-id directory, check greengrassObjectClassification.log.

    For more information, see Troubleshooting with logs.

Unpacking error in runtime.log

If runtime.log contains an error similar to the following, make sure that your tar.gz source model package has a parent directory.

Greengrass deployment error: unable to download the artifact model-arn: Error while processing. Error while unpacking the file from /tmp/greengrass/artifacts/model-arn/path to /greengrass/ggc/deployment/path/model-arn, error: open /greengrass/ggc/deployment/path/model-arn/squeezenet/squeezenet_v1.1-0000.params: no such file or directory

If your package doesn't have a parent directory that contains the model files, use the following command to repackage the model:

tar -zcvf model.tar.gz ./model

For example:

─$ tar -zcvf test.tar.gz ./test ./test ./test/some.file ./test/some.file2 ./test/some.file3
Note

Don't include trailing /* characters in this command.

 

Verify that the Lambda function is successfully deployed

  1. List the contents of the deployed Lambda in the /lambda directory. Replace the placeholder values before you run the command.

    cd /greengrass/ggc/deployment/lambda/arn:aws:lambda:region:account:function:function-name:function-version ls -la
  2. Verify that the directory contains the same content as the greengrassObjectClassification.zip deployment package that you uploaded in Step 4: Create and publish a Lambda function.

    Make sure that the .py files and dependencies are in the root of the directory.

 

Verify that the inference model is successfully deployed

  1. Find the process identification number (PID) of the Lambda runtime process:

    ps aux | grep 'lambda-function-name*'

    In the output, the PID appears in the second column of the line for the Lambda runtime process.

  2. Enter the Lambda runtime namespace. Be sure to replace the placeholder pid value before you run the command.

    Note

    This directory and its contents are in the Lambda runtime namespace, so they aren't visible in a regular Linux namespace.

    sudo nsenter -t pid -m /bin/bash
  3. List the contents of the local directory that you specified for the ML resource.

    cd /greengrass-machine-learning/mxnet/squeezenet/ ls -ls

    You should see the following files:

    32 -rw-r--r-- 1 ggc_user ggc_group   31675 Nov 18 15:19 synset.txt 32 -rw-r--r-- 1 ggc_user ggc_group   28707 Nov 18 15:19 squeezenet_v1.1-symbol.json 4832 -rw-r--r-- 1 ggc_user ggc_group 4945062 Nov 18 15:19 squeezenet_v1.1-0000.params

Next steps

Next, explore other inference apps. AWS IoT Greengrass provides other Lambda functions that you can use to try out local inference. You can find the examples package in the precompiled libraries folder that you downloaded in Step 2: Install the MXNet framework.

Configuring an Intel Atom

To run this tutorial on an Intel Atom device, you must provide source images, configure the Lambda function, and add another local device resource. To use the GPU for inference, make sure the following software is installed on your device:

  • OpenCL version 1.0 or later

  • Python 3.7 and pip

    Note

    If your device is prebuilt with Python 3.6, you can create a symlink to Python 3.7 instead. For more information, see Step 2.

  • NumPy

  • OpenCV on Wheels

  1. Download static PNG or JPG images for the Lambda function to use for image classification. The example works best with small image files.

    Save your image files in the directory that contains the greengrassObjectClassification.py file (or in a subdirectory of this directory). This is in the Lambda function deployment package that you upload in Step 4: Create and publish a Lambda function.

    Note

    If you're using AWS DeepLens, you can use the onboard camera or mount your own camera to perform inference on captured images instead of static images. However, we strongly recommend you start with static images first.

    If you use a camera, make sure that the awscam APT package is installed and up to date. For more information, see Update your AWS DeepLens device in the AWS DeepLens Developer Guide.

  2. If you're using Python 3.6, make sure to create a symlink from Python 3.7 to Python 3.6. This configures your device to use Python 3 with AWS IoT Greengrass. Run the following command to locate your Python installation:

    which python3

    Run the following command to create the symlink:

    sudo ln -s path-to-python-3.6/python3.6 path-to-python-3.7/python3.7

    Reboot the device.

  3. Edit the configuration of the Lambda function. Follow the procedure in Step 5: Add the Lambda function to the Greengrass group.

    Note

    We recommend that you run your Lambda function without containerization unless your business case requires it. This helps enable access to your device GPU and camera without configuring device resources. If you run without containerization, you must also grant root access to your AWS IoT Greengrass Lambda functions.

    1. To run without containerization:

      • For Run as, choose Another user ID/group ID. For UID, enter 0. For GUID, enter 0.

        This allows your Lambda function to run as root. For more information about running as root, see Setting the default access identity for Lambda functions in a group.

        Tip

        You also must update your config.json file to grant root access to your Lambda function. For the procedure , see Running a Lambda function as root.

      • For Containerization, choose No container.

        For more information about running without containerization, see Considerations when choosing Lambda function containerization.

      • Update the Timeout value to 5 seconds. This ensures that the request does not time out too early. It takes a few minutes after setup to run inference.

      • For Read access to /sys directory, choose Enable.

      • For Lambda lifecycle, choose Make this function long-lived and keep it running indefinitely.

    2. To run in containerized mode instead:

      Note

      We do not recommend running in containerized mode unless your business case requires it.

      • Update the Timeout value to 5 seconds. This ensures that the request does not time out too early. It takes a few minutes after setup to run inference.

      • For Read access to /sys directory, choose Enable.

      • For Lambda lifecycle, choose Make this function long-lived and keep it running indefinitely.

  4. If running in containerized mode, add the required local device resource to grant access to your device GPU.

    Note

    If you run in non-containerized mode, AWS IoT Greengrass can access your device GPU without configuring device resources.

    1. On the group configuration page, choose Resources.

      
                The group configuration page with Resources highlighted.
    2. On the Local tab, choose Add a local resource.

    3. Define the resource:

      • For Resource name, enter renderD128.

      • For Resource type, choose Device.

      • For Device path, enter /dev/dri/renderD128.

      • For Group owner file access permission, choose Automatically add OS group permissions of the Linux group that owns the resource.

      • For Lambda function affiliations, grant Read and write access to your Lambda function.

Configuring an NVIDIA Jetson TX2

To run this tutorial on an NVIDIA Jetson TX2, provide source images and configure the Lambda function. If you're using the GPU, you must also add local device resources.

  1. Make sure your Jetson device is configured so you can install the AWS IoT Greengrass Core software. For more information about configuring your device, see Setting up other devices.

  2. Open the MXNet documentation, go to Installing MXNet on a Jetson, and follow the instructions to install MXNet on the Jetson device.

    Note

    If you want to build MXNet from source, follow the instructions to build the shared library. Edit the following settings in your config.mk file to work with a Jetson TX2 device:

    • Add -gencode arch=compute-62, code=sm_62 to the CUDA_ARCH setting.

    • Turn on CUDA.

      USE_CUDA = 1
  3. Download static PNG or JPG images for the Lambda function to use for image classification. The app works best with small image files. Alternatively, you can instrument a camera on the Jetson board to capture the source images.

    Save your image files in the directory that contains the greengrassObjectClassification.py file. You can also save them in a subdirectory of this directory. This directory is in the Lambda function deployment package that you upload in Step 4: Create and publish a Lambda function.

  4. Create a symlink from Python 3.7 to Python 3.6 to use Python 3 with AWS IoT Greengrass. Run the following command to locate your Python installation:

    which python3

    Run the following command to create the symlink:

    sudo ln -s path-to-python-3.6/python3.6 path-to-python-3.7/python3.7

    Reboot the device.

  5. Make sure the ggc_user system account can use the MXNet framework:

    “sudo -u ggc_user bash -c 'python3 -c "import mxnet"'
  6. Edit the configuration of the Lambda function. Follow the procedure in Step 5: Add the Lambda function to the Greengrass group.

    Note

    We recommend that you run your Lambda function without containerization unless your business case requires it. This helps enable access to your device GPU and camera without configuring device resources. If you run without containerization, you must also grant root access to your AWS IoT Greengrass Lambda functions.

    1. To run without containerization:

      • For Run as, choose Another user ID/group ID. For UID, enter 0. For GUID, enter 0.

        This allows your Lambda function to run as root. For more information about running as root, see Setting the default access identity for Lambda functions in a group.

        Tip

        You also must update your config.json file to grant root access to your Lambda function. For the procedure, see Running a Lambda function as root.

      • For Containerization, choose No container.

        For more information about running without containerization, see Considerations when choosing Lambda function containerization.

      • For Read access to /sys directory, choose Enable.

      • Under Environment variables, add the following key-value pairs to your Lambda function. This configures AWS IoT Greengrass to use the MXNet framework.

        Key

        Value

        PATH

        /usr/local/cuda/bin:$PATH

        MXNET_HOME

        $HOME/mxnet/

        PYTHONPATH

        $MXNET_HOME/python:$PYTHONPATH

        CUDA_HOME

        /usr/local/cuda

        LD_LIBRARY_PATH

        $LD_LIBRARY_PATH:${CUDA_HOME}/lib64

    2. To run in containerized mode instead:

      Note

      We do not recommend running in containerized mode unless your business case requires it.

      • Increase the Memory limit value. Use 500 MB for CPU, or at least 2000 MB for GPU.

      • For Read access to /sys directory, choose Enable.

      • Under Environment variables, add the following key-value pairs to your Lambda function. This configures AWS IoT Greengrass to use the MXNet framework.

        Key

        Value

        PATH

        /usr/local/cuda/bin:$PATH

        MXNET_HOME

        $HOME/mxnet/

        PYTHONPATH

        $MXNET_HOME/python:$PYTHONPATH

        CUDA_HOME

        /usr/local/cuda

        LD_LIBRARY_PATH

        $LD_LIBRARY_PATH:${CUDA_HOME}/lib64

  7. If running in containerized mode, add the following local device resources to grant access to your device GPU. Follow the procedure in Step 6: Add resources to the Greengrass group.

    Note

    If you run in non-containerized mode, AWS IoT Greengrass can access your device GPU without configuring device resources.

    For each resource:

    • For Resource type, choose Device.

    • For Group owner file access permission, choose Automatically add OS group permissions of the Linux group that owns the resource.

    • For Lambda function affiliations, grant Read and write access to your Lambda function.

       

      Name

      Device path

      nvhost-ctrl

      /dev/nvhost-ctrl

      nvhost-gpu

      /dev/nvhost-gpu

      nvhost-ctrl-gpu

      /dev/nvhost-ctrl-gpu

      nvhost-dbg-gpu

      /dev/nvhost-dbg-gpu

      nvhost-prof-gpu

      /dev/nvhost-prof-gpu

      nvmap

      /dev/nvmap

      nvhost-vic

      /dev/nvhost-vic

      tegra_dc_ctrl

      /dev/tegra_dc_ctrl

  8. If running in containerized mode, add the following local volume resource to grant access to your device camera. Follow the procedure in Step 6: Add resources to the Greengrass group.

    Note

    If you run in non-containerized mode, AWS IoT Greengrass can access your device camera without configuring volume resources.

    • For Resource type, choose Volume.

    • For Group owner file access permission, choose Automatically add OS group permissions of the Linux group that owns the resource.

    • For Lambda function affiliations, grant Read and write access to your Lambda function.

       

      Name

      Source path

      Destination path

      shm

      /dev/shm

      /dev/shm

      tmp

      /tmp

      /tmp