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:bab5aaff6f034064a11eee4f2ff637ccdb2e3890
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:bab5aaff6f034064a11eee4f2ff637ccdb2e3890 \
--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:375102feb280c7a751b28722816d24d9f27ebe70
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:375102feb280c7a751b28722816d24d9f27ebe70 \
--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:e7d8ee1b7924dd448fdb6ae8cf5fb579b31e72a1
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:e7d8ee1b7924dd448fdb6ae8cf5fb579b31e72a1 \
--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:aa7cc8877fd4d8f9d6cf3ca6393e59acaa6d23f2
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:aa7cc8877fd4d8f9d6cf3ca6393e59acaa6d23f2 \
--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.
