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:cae164c03201358017505f5d6e7fb0cb34e4600f

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:cae164c03201358017505f5d6e7fb0cb34e4600f \
        --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:2cdb05a4230cd2d0768ae456e4f0da5b18d32a59

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:2cdb05a4230cd2d0768ae456e4f0da5b18d32a59 \
        --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-6-0:6fd8ea8b763f300cbac204734608839cf89d2014

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-6-0:6fd8ea8b763f300cbac204734608839cf89d2014 \
        --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