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:fe4fc164befee05fe4a91d641f4ac84934c5b8b2
Arguments:
--api-key ei_2994dcb172c1bb8ca37867ce0c9e6116a527816c4f6b21b967dd6e1cad217218 --run-http-server 1337 --impulse-id 2
For example, in a one-liner locally:
docker run --rm -it \
-p 1337:1337 \
public.ecr.aws/g7a8t7v6/inference-container:fe4fc164befee05fe4a91d641f4ac84934c5b8b2 \
--api-key ei_2994dcb172c1bb8ca37867ce0c9e6116a527816c4f6b21b967dd6e1cad217218 \
--run-http-server 1337 \
--impulse-id 2
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:bd33296863cd39365a00dec8863c5ab51da275f1
Arguments:
--api-key ei_2994dcb172c1bb8ca37867ce0c9e6116a527816c4f6b21b967dd6e1cad217218 --run-http-server 1337 --impulse-id 2
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:bd33296863cd39365a00dec8863c5ab51da275f1 \
--api-key ei_2994dcb172c1bb8ca37867ce0c9e6116a527816c4f6b21b967dd6e1cad217218 \
--run-http-server 1337 \
--impulse-id 2
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:7ec1726b36fe1bec368959ce8bca11651ce7d99f
Arguments:
--api-key ei_2994dcb172c1bb8ca37867ce0c9e6116a527816c4f6b21b967dd6e1cad217218 --run-http-server 1337 --impulse-id 2
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:7ec1726b36fe1bec368959ce8bca11651ce7d99f \
--api-key ei_2994dcb172c1bb8ca37867ce0c9e6116a527816c4f6b21b967dd6e1cad217218 \
--run-http-server 1337 \
--impulse-id 2
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-6-0:8615f331755ad6256a1891d2a67b920ae56870b7
Arguments:
--api-key ei_2994dcb172c1bb8ca37867ce0c9e6116a527816c4f6b21b967dd6e1cad217218 --run-http-server 1337 --impulse-id 2
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-6-0:8615f331755ad6256a1891d2a67b920ae56870b7 \
--api-key ei_2994dcb172c1bb8ca37867ce0c9e6116a527816c4f6b21b967dd6e1cad217218 \
--run-http-server 1337 \
--impulse-id 2
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.
