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:9a76b35372317133579fc10e36c91d046a280fb4
Arguments:
--api-key ei_b8b44e3867c08470b5243fd841ba0fcf41d621787c279f7148c05673b26f0e0d --run-http-server 1337
For example, in a one-liner locally:
docker run --rm -it \
-p 1337:1337 \
public.ecr.aws/g7a8t7v6/inference-container:9a76b35372317133579fc10e36c91d046a280fb4 \
--api-key ei_b8b44e3867c08470b5243fd841ba0fcf41d621787c279f7148c05673b26f0e0d \
--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/g7a8t7v6/inference-container-jetson:18cb770f173582a9519bdaa006701a18f238fa10
Arguments:
--api-key ei_b8b44e3867c08470b5243fd841ba0fcf41d621787c279f7148c05673b26f0e0d --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:18cb770f173582a9519bdaa006701a18f238fa10 \
--api-key ei_b8b44e3867c08470b5243fd841ba0fcf41d621787c279f7148c05673b26f0e0d \
--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/g7a8t7v6/inference-container-jetson-orin:49c521a3a58aa144e78f04d4b0d2b3d21aeeaa15
Arguments:
--api-key ei_b8b44e3867c08470b5243fd841ba0fcf41d621787c279f7148c05673b26f0e0d --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:49c521a3a58aa144e78f04d4b0d2b3d21aeeaa15 \
--api-key ei_b8b44e3867c08470b5243fd841ba0fcf41d621787c279f7148c05673b26f0e0d \
--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.