HRV AFIB - Tabular data - Deployment

Edge Impulse Inc. / HRV AFIB - Tabular data Public

Target: Cortex-M4F 80MHz Clone this project

Configure your deployment

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:bfc3b315fd289582242b19120237810ed1c6bb98

Arguments:

--api-key ei_af4da79751143dffb9bcba51ff30c9e450daf3a13020ed99da0776a2a068f6d4 --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:bfc3b315fd289582242b19120237810ed1c6bb98 \
        --api-key ei_af4da79751143dffb9bcba51ff30c9e450daf3a13020ed99da0776a2a068f6d4 \
        --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:5099e21de99ef77b3aee1e7a664df8849b488c75

Arguments:

--api-key ei_af4da79751143dffb9bcba51ff30c9e450daf3a13020ed99da0776a2a068f6d4 --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:5099e21de99ef77b3aee1e7a664df8849b488c75 \
        --api-key ei_af4da79751143dffb9bcba51ff30c9e450daf3a13020ed99da0776a2a068f6d4 \
        --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:cefd80d921502fe00e1b0091ee6d1aad56b93c25

Arguments:

--api-key ei_af4da79751143dffb9bcba51ff30c9e450daf3a13020ed99da0776a2a068f6d4 --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:cefd80d921502fe00e1b0091ee6d1aad56b93c25 \
        --api-key ei_af4da79751143dffb9bcba51ff30c9e450daf3a13020ed99da0776a2a068f6d4 \
        --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

Raw data	Classifier	Total
-	1 ms.	1 ms.
0.2K	1.4K	1.4K
-	16.2K	-
		-

Unoptimized (float32)


Latency
Ram
Flash
Accuracy

Raw data	Classifier	Total
-	1 ms.	1 ms.
0.2K	1.6K	1.6K
-	16.8K	-
		94.99%

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