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/z9b3d4t5/inference-container:f7506662b79167fb1e67701d333af8feb04ff22e
Arguments:
--api-key ei_86d778cbdd6b2cfa5793641bf6522fb358a21ef58e72eed22205de39c80cf071 --run-http-server 1337 --impulse-id 17
For example, in a one-liner locally:
docker run --rm -it \
-p 1337:1337 \
public.ecr.aws/z9b3d4t5/inference-container:f7506662b79167fb1e67701d333af8feb04ff22e \
--api-key ei_86d778cbdd6b2cfa5793641bf6522fb358a21ef58e72eed22205de39c80cf071 \
--run-http-server 1337 \
--impulse-id 17
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/z9b3d4t5/inference-container-jetson:5bdd14a7e1811111c8e01c64947fc045f576399d
Arguments:
--api-key ei_86d778cbdd6b2cfa5793641bf6522fb358a21ef58e72eed22205de39c80cf071 --run-http-server 1337 --impulse-id 17
For example, in a one-liner locally:
docker run --rm -it --runtime=nvidia --gpus all \
-p 1337:1337 \
public.ecr.aws/z9b3d4t5/inference-container-jetson:5bdd14a7e1811111c8e01c64947fc045f576399d \
--api-key ei_86d778cbdd6b2cfa5793641bf6522fb358a21ef58e72eed22205de39c80cf071 \
--run-http-server 1337 \
--impulse-id 17
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/z9b3d4t5/inference-container-jetson-orin:7527dbccfd89c3e8c1d2785aa60d2dfc2bfd53dd
Arguments:
--api-key ei_86d778cbdd6b2cfa5793641bf6522fb358a21ef58e72eed22205de39c80cf071 --run-http-server 1337 --impulse-id 17
For example, in a one-liner locally:
docker run --rm -it --runtime=nvidia --gpus all \
-p 1337:1337 \
public.ecr.aws/z9b3d4t5/inference-container-jetson-orin:7527dbccfd89c3e8c1d2785aa60d2dfc2bfd53dd \
--api-key ei_86d778cbdd6b2cfa5793641bf6522fb358a21ef58e72eed22205de39c80cf071 \
--run-http-server 1337 \
--impulse-id 17
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/z9b3d4t5/inference-container-jetson-orin-6-0:57b6946617cad3b76737f7a0b1d1baecc9856110
Arguments:
--api-key ei_86d778cbdd6b2cfa5793641bf6522fb358a21ef58e72eed22205de39c80cf071 --run-http-server 1337 --impulse-id 17
For example, in a one-liner locally:
docker run --rm -it --runtime=nvidia --gpus all \
-p 1337:1337 \
public.ecr.aws/z9b3d4t5/inference-container-jetson-orin-6-0:57b6946617cad3b76737f7a0b1d1baecc9856110 \
--api-key ei_86d778cbdd6b2cfa5793641bf6522fb358a21ef58e72eed22205de39c80cf071 \
--run-http-server 1337 \
--impulse-id 17
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 Qualcomm Adreno 702 GPUs, use:
Container:
public.ecr.aws/z9b3d4t5/inference-container-qc-adreno-702:4496c16e5c16577e7b00252a4f6d7020ae63a67e
Arguments:
--api-key ei_86d778cbdd6b2cfa5793641bf6522fb358a21ef58e72eed22205de39c80cf071 --run-http-server 1337 --impulse-id 17
For example, in a one-liner locally:
docker run --rm -it --device /dev/dri \
-p 1337:1337 \
public.ecr.aws/z9b3d4t5/inference-container-qc-adreno-702:4496c16e5c16577e7b00252a4f6d7020ae63a67e \
--api-key ei_86d778cbdd6b2cfa5793641bf6522fb358a21ef58e72eed22205de39c80cf071 \
--run-http-server 1337 \
--impulse-id 17
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.
