Deploy to any Linux-based development board
Edge Impulse for Linux lets you run your models on any Linux-based development board,
with SDKs for Node.js, Python, Go and C++ to integrate your models quickly into
your application.
- Install the Edge Impulse Linux CLI
- Run
edge-impulse-linux-runner (run with --clean to switch projects)
Run your model as a Docker container
To run your model as a container with an HTTP interface, use:
Container:
public.ecr.aws/g7a8t7v6/inference-container:fe5460b2194303701aeb66561f540367f57684fa
Arguments:
--api-key ei_346de1bf1b719dc257afa6b3e7240933c6e6e2999edaa11a8913be7aac54afd6 --run-http-server 1337
For example, in a one-liner locally:
docker run --rm -it \
-p 1337:1337 \
public.ecr.aws/g7a8t7v6/inference-container:fe5460b2194303701aeb66561f540367f57684fa \
--api-key ei_346de1bf1b719dc257afa6b3e7240933c6e6e2999edaa11a8913be7aac54afd6 \
--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's GPUs (JetPack 4.6.x), use:
Container:
public.ecr.aws/g7a8t7v6/inference-container-jetson:41393fc71df2d9a61be251245f43a3da0f67be18
Arguments:
--api-key ei_346de1bf1b719dc257afa6b3e7240933c6e6e2999edaa11a8913be7aac54afd6 --run-http-server 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:41393fc71df2d9a61be251245f43a3da0f67be18 \
--api-key ei_346de1bf1b719dc257afa6b3e7240933c6e6e2999edaa11a8913be7aac54afd6 \
--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:1826dce6a8adc996347c0037c5b20bbb15e26207
Arguments:
--api-key ei_346de1bf1b719dc257afa6b3e7240933c6e6e2999edaa11a8913be7aac54afd6 --run-http-server 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:1826dce6a8adc996347c0037c5b20bbb15e26207 \
--api-key ei_346de1bf1b719dc257afa6b3e7240933c6e6e2999edaa11a8913be7aac54afd6 \
--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-6-0:512154167e27f9139b228b7b4f48be415b30d050
Arguments:
--api-key ei_346de1bf1b719dc257afa6b3e7240933c6e6e2999edaa11a8913be7aac54afd6 --run-http-server 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-6-0:512154167e27f9139b228b7b4f48be415b30d050 \
--api-key ei_346de1bf1b719dc257afa6b3e7240933c6e6e2999edaa11a8913be7aac54afd6 \
--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.
