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:feba530406c805b3b61fa68305cda70a5d9878a2
Arguments:
--api-key ei_105bfddf531d682d3c5c87dd9d9393fa1628e54892e3809d692e6ae3200c8f06 --run-http-server 1337 --impulse-id 3
For example, in a one-liner locally:
docker run --rm -it \
-p 1337:1337 \
public.ecr.aws/g7a8t7v6/inference-container:feba530406c805b3b61fa68305cda70a5d9878a2 \
--api-key ei_105bfddf531d682d3c5c87dd9d9393fa1628e54892e3809d692e6ae3200c8f06 \
--run-http-server 1337 \
--impulse-id 3
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:ceb75a1c0c0f1e43d48c1ce100f84fa6a19d2d27
Arguments:
--api-key ei_105bfddf531d682d3c5c87dd9d9393fa1628e54892e3809d692e6ae3200c8f06 --run-http-server 1337 --impulse-id 3
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:ceb75a1c0c0f1e43d48c1ce100f84fa6a19d2d27 \
--api-key ei_105bfddf531d682d3c5c87dd9d9393fa1628e54892e3809d692e6ae3200c8f06 \
--run-http-server 1337 \
--impulse-id 3
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:e02e287edc85acf40e675f8fc5dcb9904f4798bb
Arguments:
--api-key ei_105bfddf531d682d3c5c87dd9d9393fa1628e54892e3809d692e6ae3200c8f06 --run-http-server 1337 --impulse-id 3
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:e02e287edc85acf40e675f8fc5dcb9904f4798bb \
--api-key ei_105bfddf531d682d3c5c87dd9d9393fa1628e54892e3809d692e6ae3200c8f06 \
--run-http-server 1337 \
--impulse-id 3
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:f53a733e9895d8a1b92d3c6e51a5993ce2c21360
Arguments:
--api-key ei_105bfddf531d682d3c5c87dd9d9393fa1628e54892e3809d692e6ae3200c8f06 --run-http-server 1337 --impulse-id 3
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:f53a733e9895d8a1b92d3c6e51a5993ce2c21360 \
--api-key ei_105bfddf531d682d3c5c87dd9d9393fa1628e54892e3809d692e6ae3200c8f06 \
--run-http-server 1337 \
--impulse-id 3
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.
