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:7a1098ccb355fba21f3c1461b4b3837592101b8b
Arguments:
--api-key ei_d6d8ae5371f5cc4669edf6179df84481c8dd778746114e6949f3854baea7c16e --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:7a1098ccb355fba21f3c1461b4b3837592101b8b \
--api-key ei_d6d8ae5371f5cc4669edf6179df84481c8dd778746114e6949f3854baea7c16e \
--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:f3aee4789711d70d460b38bbf3d47972b64b7349
Arguments:
--api-key ei_d6d8ae5371f5cc4669edf6179df84481c8dd778746114e6949f3854baea7c16e --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:f3aee4789711d70d460b38bbf3d47972b64b7349 \
--api-key ei_d6d8ae5371f5cc4669edf6179df84481c8dd778746114e6949f3854baea7c16e \
--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:99e47aa5540dfae9b2bfb9f1c592505d81ed8619
Arguments:
--api-key ei_d6d8ae5371f5cc4669edf6179df84481c8dd778746114e6949f3854baea7c16e --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:99e47aa5540dfae9b2bfb9f1c592505d81ed8619 \
--api-key ei_d6d8ae5371f5cc4669edf6179df84481c8dd778746114e6949f3854baea7c16e \
--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-6-0:7397b660f4b1b56f7796e49f17a7b9e9387d4acd
Arguments:
--api-key ei_d6d8ae5371f5cc4669edf6179df84481c8dd778746114e6949f3854baea7c16e --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-6-0:7397b660f4b1b56f7796e49f17a7b9e9387d4acd \
--api-key ei_d6d8ae5371f5cc4669edf6179df84481c8dd778746114e6949f3854baea7c16e \
--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.