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:cd1e9c9f72586e6a72eaffa59fff6dc2bcb1e05d
Arguments:
--api-key ei_401b3e4c66c71dd246e628be593915375ca7d33fc567d4ede4cb8f6e265a63d7 --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:cd1e9c9f72586e6a72eaffa59fff6dc2bcb1e05d \
--api-key ei_401b3e4c66c71dd246e628be593915375ca7d33fc567d4ede4cb8f6e265a63d7 \
--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:04a55744970098321ae6038c9bf8e6e202cceff8
Arguments:
--api-key ei_401b3e4c66c71dd246e628be593915375ca7d33fc567d4ede4cb8f6e265a63d7 --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:04a55744970098321ae6038c9bf8e6e202cceff8 \
--api-key ei_401b3e4c66c71dd246e628be593915375ca7d33fc567d4ede4cb8f6e265a63d7 \
--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:d61f6fe8210af05d59f5e98716d1bdf4e4a5190e
Arguments:
--api-key ei_401b3e4c66c71dd246e628be593915375ca7d33fc567d4ede4cb8f6e265a63d7 --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:d61f6fe8210af05d59f5e98716d1bdf4e4a5190e \
--api-key ei_401b3e4c66c71dd246e628be593915375ca7d33fc567d4ede4cb8f6e265a63d7 \
--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-6-0:744c5bcae67b62cdd8e357b93a81756807e69820
Arguments:
--api-key ei_401b3e4c66c71dd246e628be593915375ca7d33fc567d4ede4cb8f6e265a63d7 --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-6-0:744c5bcae67b62cdd8e357b93a81756807e69820 \
--api-key ei_401b3e4c66c71dd246e628be593915375ca7d33fc567d4ede4cb8f6e265a63d7 \
--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.
