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:262fc7e07c93877a8301f32ffe679c15300172bd
Arguments:
--api-key ei_483e791b6bce906c6a577167852fb3cbf101e526c7da874edc2c258a99bd7221 --run-http-server 1337 --impulse-id undefined
For example, in a one-liner locally:
docker run --rm -it \
-p 1337:1337 \
public.ecr.aws/g7a8t7v6/inference-container:262fc7e07c93877a8301f32ffe679c15300172bd \
--api-key ei_483e791b6bce906c6a577167852fb3cbf101e526c7da874edc2c258a99bd7221 \
--run-http-server 1337 \
--impulse-id undefined
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:c063fff5f6f5b7cd6adc81a68cb21bce40b03561
Arguments:
--api-key ei_483e791b6bce906c6a577167852fb3cbf101e526c7da874edc2c258a99bd7221 --run-http-server 1337 --impulse-id undefined
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:c063fff5f6f5b7cd6adc81a68cb21bce40b03561 \
--api-key ei_483e791b6bce906c6a577167852fb3cbf101e526c7da874edc2c258a99bd7221 \
--run-http-server 1337 \
--impulse-id undefined
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:15be12fce4d06e0ef011f068f6e6a292dc7a9a68
Arguments:
--api-key ei_483e791b6bce906c6a577167852fb3cbf101e526c7da874edc2c258a99bd7221 --run-http-server 1337 --impulse-id undefined
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:15be12fce4d06e0ef011f068f6e6a292dc7a9a68 \
--api-key ei_483e791b6bce906c6a577167852fb3cbf101e526c7da874edc2c258a99bd7221 \
--run-http-server 1337 \
--impulse-id undefined
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:addb407c6b60cc1bc1529c1ab49e74963892b205
Arguments:
--api-key ei_483e791b6bce906c6a577167852fb3cbf101e526c7da874edc2c258a99bd7221 --run-http-server 1337 --impulse-id undefined
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:addb407c6b60cc1bc1529c1ab49e74963892b205 \
--api-key ei_483e791b6bce906c6a577167852fb3cbf101e526c7da874edc2c258a99bd7221 \
--run-http-server 1337 \
--impulse-id undefined
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.
