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:b78a14bf8357a6ecc159d17dd731fb51a56d178b
Arguments:
--api-key ei_01da6345df37b46d179e43e58c537a3aa105796b8a06154fae028e9b19faf359 --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:b78a14bf8357a6ecc159d17dd731fb51a56d178b \
--api-key ei_01da6345df37b46d179e43e58c537a3aa105796b8a06154fae028e9b19faf359 \
--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:cae164c03201358017505f5d6e7fb0cb34e4600f
Arguments:
--api-key ei_01da6345df37b46d179e43e58c537a3aa105796b8a06154fae028e9b19faf359 --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:cae164c03201358017505f5d6e7fb0cb34e4600f \
--api-key ei_01da6345df37b46d179e43e58c537a3aa105796b8a06154fae028e9b19faf359 \
--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:2cdb05a4230cd2d0768ae456e4f0da5b18d32a59
Arguments:
--api-key ei_01da6345df37b46d179e43e58c537a3aa105796b8a06154fae028e9b19faf359 --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:2cdb05a4230cd2d0768ae456e4f0da5b18d32a59 \
--api-key ei_01da6345df37b46d179e43e58c537a3aa105796b8a06154fae028e9b19faf359 \
--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-6-0:6fd8ea8b763f300cbac204734608839cf89d2014
Arguments:
--api-key ei_01da6345df37b46d179e43e58c537a3aa105796b8a06154fae028e9b19faf359 --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-6-0:6fd8ea8b763f300cbac204734608839cf89d2014 \
--api-key ei_01da6345df37b46d179e43e58c537a3aa105796b8a06154fae028e9b19faf359 \
--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.
