RA8D1 - Medical Classification - Deployment

Edge Impulse Experts / RA8D1 - Medical Classification Public

Target: Renesas RA8D1 (Cortex-M85 480MHz) 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:b7e0dc65374f7562ff72a40dd4c6623c8df0b5a9

Arguments:

--api-key ei_04435ad078a646a5ee3fa89e1451f51a08eed6c9254468dd32ceb06faa1b30f5 --run-http-server 1337 --impulse-id 2

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:b7e0dc65374f7562ff72a40dd4c6623c8df0b5a9 \
        --api-key ei_04435ad078a646a5ee3fa89e1451f51a08eed6c9254468dd32ceb06faa1b30f5 \
        --run-http-server 1337 \
        --impulse-id 2

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:fc4ef2bb6ce3e51bd1a391551d0d57ca745d8257

Arguments:

--api-key ei_04435ad078a646a5ee3fa89e1451f51a08eed6c9254468dd32ceb06faa1b30f5 --run-http-server 1337 --impulse-id 2

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:fc4ef2bb6ce3e51bd1a391551d0d57ca745d8257 \
        --api-key ei_04435ad078a646a5ee3fa89e1451f51a08eed6c9254468dd32ceb06faa1b30f5 \
        --run-http-server 1337 \
        --impulse-id 2

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:bebbe4e4fd1d6ced9b04da167c86411c50d2dc48

Arguments:

--api-key ei_04435ad078a646a5ee3fa89e1451f51a08eed6c9254468dd32ceb06faa1b30f5 --run-http-server 1337 --impulse-id 2

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:bebbe4e4fd1d6ced9b04da167c86411c50d2dc48 \
        --api-key ei_04435ad078a646a5ee3fa89e1451f51a08eed6c9254468dd32ceb06faa1b30f5 \
        --run-http-server 1337 \
        --impulse-id 2

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

Image	Transfer learning	Total
1 ms.	80 ms.	81 ms.
4.0K	721.5K	721.5K
-	627.6K	-
		-

Unoptimized (float32)


Latency
Ram
Flash
Accuracy

Image	Transfer learning	Total
1 ms.	431 ms.	432 ms.
4.0K	2.4M	2.4M
-	1.8M	-
		72.00%

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

Estimate for Renesas RA8D1 (Cortex-M85 480MHz).

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 (RAM optimized) Enterprise

EON™ Compiler

TensorFlow Lite

Quantized (int8)

Unoptimized (float32)

Latest build

Run this model

Build output

Model testing output

EON™ Compiler (RAM optimized)
Enterprise