Raspi - Object Detection - Deployment

MJRoBot (Marcelo Rovai) / Raspi - Object Detection Public

Target: Raspberry Pi 4 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:8cff41327357bcd85bb652679f481ee2c9afe590

Arguments:

--api-key ei_f0846f4d01074f61aebfd1b84c4d03d31595b3389359f39e5d2044f6734e5fb9 --run-http-server 1337 --impulse-id 13

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:8cff41327357bcd85bb652679f481ee2c9afe590 \
        --api-key ei_f0846f4d01074f61aebfd1b84c4d03d31595b3389359f39e5d2044f6734e5fb9 \
        --run-http-server 1337 \
        --impulse-id 13

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:ee4746b7ad1ec843711cd33fd776f7dd66db19ca

Arguments:

--api-key ei_f0846f4d01074f61aebfd1b84c4d03d31595b3389359f39e5d2044f6734e5fb9 --run-http-server 1337 --impulse-id 13

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:ee4746b7ad1ec843711cd33fd776f7dd66db19ca \
        --api-key ei_f0846f4d01074f61aebfd1b84c4d03d31595b3389359f39e5d2044f6734e5fb9 \
        --run-http-server 1337 \
        --impulse-id 13

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-6-0:74569fbd8ea04344229d16b6a3187bf558f0db81

Arguments:

--api-key ei_f0846f4d01074f61aebfd1b84c4d03d31595b3389359f39e5d2044f6734e5fb9 --run-http-server 1337 --impulse-id 13

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-6-0:74569fbd8ea04344229d16b6a3187bf558f0db81 \
        --api-key ei_f0846f4d01074f61aebfd1b84c4d03d31595b3389359f39e5d2044f6734e5fb9 \
        --run-http-server 1337 \
        --impulse-id 13

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.

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

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

Latest build

Run this model

Build output

Model testing output