DaLIA-PPG S1_E4 - Heart Rate and Variability Estimation with Multilabel Data and Regression Block - Deployment

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

Target: Cortex-M4F 80MHz Clone this project

Configure your deployment

Your model contains an Edge Impulse HRV DSP block which requires a license to deploy. To proceed, contact sales.

Contact sales

You can deploy your impulse to any device. This makes the model run without an internet connection, minimizes latency, and runs with minimal power consumption. Read more.

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:a81a7c9aa9d7a34b5dfc6244bd139e6c185f9144

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:a81a7c9aa9d7a34b5dfc6244bd139e6c185f9144 \
        --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:f30be133733dd5cb1e881256268c8ef5e9a80cb7

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:f30be133733dd5cb1e881256268c8ef5e9a80cb7 \
        --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-6-0:59bcd8ceaae2a29be137a3093bdc8f1fb80c4e10

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-6-0:59bcd8ceaae2a29be137a3093bdc8f1fb80c4e10 \
        --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.

Model Optimizations

Model optimizations can increase on-device performance but may reduce accuracy.

Quantized (int8)


Latency
Ram
Flash
Accuracy

HR/HRV	Regression	Total
793 ms.	1 ms.	794 ms.
14.5K	3.5K	14.5K
-	28.7K	-
		-

Unoptimized (float32)


Latency
Ram
Flash
Accuracy

HR/HRV	Regression	Total
793 ms.	10 ms.	803 ms.
14.5K	2.3K	14.5K
-	31.6K	-
		48.60%

To compare model accuracy, clone this project and run model testing.

Estimate for Cortex-M4F 80MHz.

Clone this project to deploy this impulse.

Configure your deployment

Deploy to any Linux-based development board

Run your model as a Docker container

Run your model as a Docker container

Run your model as a Docker container

Run your model as a Docker container

Model Optimizations

Quantized (int8)

Unoptimized (float32)

Latest build

Run this model

Build output

Model testing output

Configure your deployment

Default Deployment

C++ library

Deploy to any Linux-based development board

Run your model as a Docker container

Run your model as a Docker container

Run your model as a Docker container

Run your model as a Docker container

Model Optimizations

EON™ Compiler

EON™ Compiler

TensorFlow Lite

Quantized (int8)

Unoptimized (float32)

Latest build

Run this model

Build output

Model testing output