Medical Laboratories inc. / DaLIA-PPG S1_E4 - Heart Rate and Variability Estimation with Multilabel Data and Regression Block Public

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Deploy to any Linux-based development board

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Install the Edge Impulse Linux CLI
Run edge-impulse-linux-runner (run with --clean to switch projects)

See the CLI documentation for more information and setup instructions. Alternatively, you can download your model for below.

Run your model as a Docker container

To run your model as a container with an HTTP interface, use:

Container:

public.ecr.aws/g7a8t7v6/inference-container:v1.82.9

Arguments:

--api-key ei_05664c1eb18d97350e2ae9557f9effa5e50c3980621dae03a88d7cba016449ca --run-http-server 1337

Ports to expose:

1337

For example, in a one-liner locally:

docker run --rm -it \
    -p 1337:1337 \
    public.ecr.aws/g7a8t7v6/inference-container:v1.82.9 \
        --api-key ei_05664c1eb18d97350e2ae9557f9effa5e50c3980621dae03a88d7cba016449ca \
        --run-http-server 1337

This automatically builds and downloads the latest model (incl. hardware optimizations), and runs an HTTP endpoint at http://localhost:1337 with instructions.

Read the docs for information, like bundling in your model inside the container and selecting extra hardware optimizations.

Run your model as a Docker container

To run your model as a container with an HTTP interface on NVIDIA Jetson's GPUs (JetPack 4.6.x), use:

Container:

public.ecr.aws/g7a8t7v6/inference-container-jetson:v1.82.9

Arguments:

--api-key ei_05664c1eb18d97350e2ae9557f9effa5e50c3980621dae03a88d7cba016449ca --run-http-server 1337

Ports to expose:

1337

For example, in a one-liner locally:

docker run --rm -it --runtime=nvidia --gpus all \
    -p 1337:1337 \
    public.ecr.aws/g7a8t7v6/inference-container-jetson:v1.82.9 \
        --api-key ei_05664c1eb18d97350e2ae9557f9effa5e50c3980621dae03a88d7cba016449ca \
        --run-http-server 1337

This automatically builds and downloads the latest model (incl. hardware optimizations), and runs an HTTP endpoint at http://localhost:1337 with instructions.

Read the docs for information, like bundling in your model inside the container and selecting extra hardware optimizations.

Run your model as a Docker container

To run your model as a container with an HTTP interface on NVIDIA Jetson Orin's GPUs (JetPack 5.1.x), use:

Container:

public.ecr.aws/g7a8t7v6/inference-container-jetson-orin:v1.82.9

Arguments:

--api-key ei_05664c1eb18d97350e2ae9557f9effa5e50c3980621dae03a88d7cba016449ca --run-http-server 1337

Ports to expose:

1337

For example, in a one-liner locally:

docker run --rm -it --runtime=nvidia --gpus all \
    -p 1337:1337 \
    public.ecr.aws/g7a8t7v6/inference-container-jetson-orin:v1.82.9 \
        --api-key ei_05664c1eb18d97350e2ae9557f9effa5e50c3980621dae03a88d7cba016449ca \
        --run-http-server 1337

This automatically builds and downloads the latest model (incl. hardware optimizations), and runs an HTTP endpoint at http://localhost:1337 with instructions.

Read the docs for information, like bundling in your model inside the container and selecting extra hardware optimizations.

Run your model as a Docker container

To run your model as a container with an HTTP interface on NVIDIA Jetson Orin's GPUs (JetPack 6.0), use:

Container:

public.ecr.aws/g7a8t7v6/inference-container-jetson-orin-6-0:v1.82.9

Arguments:

--api-key ei_05664c1eb18d97350e2ae9557f9effa5e50c3980621dae03a88d7cba016449ca --run-http-server 1337

Ports to expose:

1337

For example, in a one-liner locally:

docker run --rm -it --runtime=nvidia --gpus all \
    -p 1337:1337 \
    public.ecr.aws/g7a8t7v6/inference-container-jetson-orin-6-0:v1.82.9 \
        --api-key ei_05664c1eb18d97350e2ae9557f9effa5e50c3980621dae03a88d7cba016449ca \
        --run-http-server 1337

This automatically builds and downloads the latest model (incl. hardware optimizations), and runs an HTTP endpoint at http://localhost:1337 with instructions.

Read the docs for information, like bundling in your model inside the container and selecting extra hardware optimizations.

Run your model as a Docker container

To run your model as a container with an HTTP interface on Qualcomm Adreno 702 GPUs, use:

Container:

public.ecr.aws/g7a8t7v6/inference-container-qc-adreno-702:v1.82.10

Arguments:

--api-key ei_05664c1eb18d97350e2ae9557f9effa5e50c3980621dae03a88d7cba016449ca --run-http-server 1337 --force-target runner-linux-aarch64-gpu

Ports to expose:

1337

For example, in a one-liner locally:

docker run --rm -it --device /dev/dri \
    -p 1337:1337 \
    public.ecr.aws/g7a8t7v6/inference-container-qc-adreno-702:v1.82.10 \
        --api-key ei_05664c1eb18d97350e2ae9557f9effa5e50c3980621dae03a88d7cba016449ca \
        --run-http-server 1337 --force-target runner-linux-aarch64-gpu

This automatically builds and downloads the latest model (incl. hardware optimizations), and runs an HTTP endpoint at http://localhost:1337 with instructions.

Read the docs for information, like bundling in your model inside the container and selecting extra hardware optimizations.

Run your model as a Docker container

To run your model as a container with an HTTP interface on Arduino UNO Q CPU, use:

Container:

public.ecr.aws/g7a8t7v6/inference-container-qc-adreno-702:v1.82.10

Arguments:

--api-key ei_05664c1eb18d97350e2ae9557f9effa5e50c3980621dae03a88d7cba016449ca --run-http-server 1337

Ports to expose:

1337

For example, in a one-liner locally:

docker run --rm -it \
    -p 1337:1337 \
    public.ecr.aws/g7a8t7v6/inference-container-qc-adreno-702:v1.82.10 \
        --api-key ei_05664c1eb18d97350e2ae9557f9effa5e50c3980621dae03a88d7cba016449ca \
        --run-http-server 1337

This automatically builds and downloads the latest model (incl. hardware optimizations), and runs an HTTP endpoint at http://localhost:1337 with instructions.

Read the docs for information, like bundling in your model inside the container and selecting extra hardware optimizations.

Clone this project to deploy this impulse.

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Built STM32Cube.MX CMSIS-PACK

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Built Arduino library

Add this library through the Arduino IDE via:

Sketch > Include Library > Add .ZIP Library...

Examples can then be found under:

File > Examples > DaLIA-PPG_S1_E4_-_Heart_Rate_and_Variability_Estimation_with_Multilabel_Data_and_Regression_Block_inferencing

Built Zephyr module

Add this module to your application updating the CMakeLists.txt file adding:

list(APPEND ZEPHYR_EXTRA_MODULES ${CMAKE_CURRENT_SOURCE_DIR}/model)

Adapting the path to the folder where you have the model in your application.

To add the Edge Impulse SDK update the manifest file in use adding the entry for edge-impulse-sdk-zephyr as specified in the west.yml in the downloaded zip.

Learn how to integrate this model as module into your Zephyr application

Built Simplicity Studio Component

Built Open CMSIS package

Import the model and the Edge Impulse SDK packages to your project.

Play video to see how to import the package.