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/z9b3d4t5/inference-container:263a532ac90f474402d1bda2d37d65fd12617aa2
Arguments:
--api-key ei_e83ad354f044a9ba535e50e0fced8a607d08a25cd4f28a119b1b8d3f7ad8b040 --run-http-server 1337
For example, in a one-liner locally:
docker run --rm -it \
-p 1337:1337 \
public.ecr.aws/z9b3d4t5/inference-container:263a532ac90f474402d1bda2d37d65fd12617aa2 \
--api-key ei_e83ad354f044a9ba535e50e0fced8a607d08a25cd4f28a119b1b8d3f7ad8b040 \
--run-http-server 1337
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, use:
Container:
public.ecr.aws/z9b3d4t5/inference-container-jetson:22150dc38ffc65d7e15aa30f13ed470f3ed7ae03
Arguments:
--api-key ei_e83ad354f044a9ba535e50e0fced8a607d08a25cd4f28a119b1b8d3f7ad8b040 --run-http-server 1337
For example, in a one-liner locally:
docker run --rm -it --runtime=nvidia --gpus all \
-p 1337:1337 \
public.ecr.aws/z9b3d4t5/inference-container-jetson:22150dc38ffc65d7e15aa30f13ed470f3ed7ae03 \
--api-key ei_e83ad354f044a9ba535e50e0fced8a607d08a25cd4f28a119b1b8d3f7ad8b040 \
--run-http-server 1337
This automatically builds and downloads the latest model with TensorRT support, 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, use:
Container:
public.ecr.aws/z9b3d4t5/inference-container-jetson-orin:be0d96553819a2a88a90f2671309c02bebf619e6
Arguments:
--api-key ei_e83ad354f044a9ba535e50e0fced8a607d08a25cd4f28a119b1b8d3f7ad8b040 --run-http-server 1337
For example, in a one-liner locally:
docker run --rm -it --runtime=nvidia --gpus all \
-p 1337:1337 \
public.ecr.aws/z9b3d4t5/inference-container-jetson-orin:be0d96553819a2a88a90f2671309c02bebf619e6 \
--api-key ei_e83ad354f044a9ba535e50e0fced8a607d08a25cd4f28a119b1b8d3f7ad8b040 \
--run-http-server 1337
This automatically builds and downloads the latest model with TensorRT support, 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.