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:05fbbb865b4586e743ddcafd9414e91d68c0f552
Arguments:
--api-key ei_76c78dc56c123b058b5cef8cc90f5895576261c9e7e3adb7859da62d1f48c99e --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/g7a8t7v6/inference-container:05fbbb865b4586e743ddcafd9414e91d68c0f552 \
--api-key ei_76c78dc56c123b058b5cef8cc90f5895576261c9e7e3adb7859da62d1f48c99e \
--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 (JetPack 4.6.4), use:
Container:
public.ecr.aws/g7a8t7v6/inference-container-jetson:f6825a14e50b551349ccc708063c2723316bc882
Arguments:
--api-key ei_76c78dc56c123b058b5cef8cc90f5895576261c9e7e3adb7859da62d1f48c99e --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/g7a8t7v6/inference-container-jetson:f6825a14e50b551349ccc708063c2723316bc882 \
--api-key ei_76c78dc56c123b058b5cef8cc90f5895576261c9e7e3adb7859da62d1f48c99e \
--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 (JetPack 5.1.2), use:
Container:
public.ecr.aws/g7a8t7v6/inference-container-jetson-orin:b41abf660af7ea93f8905c147eb3595482cd40de
Arguments:
--api-key ei_76c78dc56c123b058b5cef8cc90f5895576261c9e7e3adb7859da62d1f48c99e --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/g7a8t7v6/inference-container-jetson-orin:b41abf660af7ea93f8905c147eb3595482cd40de \
--api-key ei_76c78dc56c123b058b5cef8cc90f5895576261c9e7e3adb7859da62d1f48c99e \
--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 (JetPack 6.0), use:
Container:
public.ecr.aws/g7a8t7v6/inference-container-jetson-orin-6-0:e05fc6ec9a9e623f9b4a66986942089739dbb003
Arguments:
--api-key ei_76c78dc56c123b058b5cef8cc90f5895576261c9e7e3adb7859da62d1f48c99e --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/g7a8t7v6/inference-container-jetson-orin-6-0:e05fc6ec9a9e623f9b4a66986942089739dbb003 \
--api-key ei_76c78dc56c123b058b5cef8cc90f5895576261c9e7e3adb7859da62d1f48c99e \
--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.