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:d314d2095eb0d3e5c3286f085c452b6e324c7416
Arguments:
--api-key ei_1b66d333a989dbec711af538affd73db17ff672abef986b6bcb164ce1b13b16c --run-http-server 1337 --impulse-id 71
For example, in a one-liner locally:
docker run --rm -it \
-p 1337:1337 \
public.ecr.aws/g7a8t7v6/inference-container:d314d2095eb0d3e5c3286f085c452b6e324c7416 \
--api-key ei_1b66d333a989dbec711af538affd73db17ff672abef986b6bcb164ce1b13b16c \
--run-http-server 1337 \
--impulse-id 71
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:59ce8b1ff7c0d6f1c8d720051d33e3de3dad012a
Arguments:
--api-key ei_1b66d333a989dbec711af538affd73db17ff672abef986b6bcb164ce1b13b16c --run-http-server 1337 --impulse-id 71
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:59ce8b1ff7c0d6f1c8d720051d33e3de3dad012a \
--api-key ei_1b66d333a989dbec711af538affd73db17ff672abef986b6bcb164ce1b13b16c \
--run-http-server 1337 \
--impulse-id 71
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:7a8510660c556e4bff45fcad8495238c2dcb78f1
Arguments:
--api-key ei_1b66d333a989dbec711af538affd73db17ff672abef986b6bcb164ce1b13b16c --run-http-server 1337 --impulse-id 71
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:7a8510660c556e4bff45fcad8495238c2dcb78f1 \
--api-key ei_1b66d333a989dbec711af538affd73db17ff672abef986b6bcb164ce1b13b16c \
--run-http-server 1337 \
--impulse-id 71
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-6-0:7ab2e1c9e2efba63a5d847e52529e8199a11d617
Arguments:
--api-key ei_1b66d333a989dbec711af538affd73db17ff672abef986b6bcb164ce1b13b16c --run-http-server 1337 --impulse-id 71
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-6-0:7ab2e1c9e2efba63a5d847e52529e8199a11d617 \
--api-key ei_1b66d333a989dbec711af538affd73db17ff672abef986b6bcb164ce1b13b16c \
--run-http-server 1337 \
--impulse-id 71
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.