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:22c3556493bc16408f50e6a94b152703cfc9bab4
Arguments:
--api-key ei_e572f55993a3cfcbc1f990866f2de9de91526b69780b93d4f27e80c88ae4b93d --run-http-server 1337
For example, in a one-liner locally:
docker run --rm -it \
-p 1337:1337 \
public.ecr.aws/g7a8t7v6/inference-container:22c3556493bc16408f50e6a94b152703cfc9bab4 \
--api-key ei_e572f55993a3cfcbc1f990866f2de9de91526b69780b93d4f27e80c88ae4b93d \
--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:adb88067a87a16b44d09469d623ec0237c727dbb
Arguments:
--api-key ei_e572f55993a3cfcbc1f990866f2de9de91526b69780b93d4f27e80c88ae4b93d --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:adb88067a87a16b44d09469d623ec0237c727dbb \
--api-key ei_e572f55993a3cfcbc1f990866f2de9de91526b69780b93d4f27e80c88ae4b93d \
--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:cf1f4a857b5d8d9ef261831c6c3a2df19e22e400
Arguments:
--api-key ei_e572f55993a3cfcbc1f990866f2de9de91526b69780b93d4f27e80c88ae4b93d --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:cf1f4a857b5d8d9ef261831c6c3a2df19e22e400 \
--api-key ei_e572f55993a3cfcbc1f990866f2de9de91526b69780b93d4f27e80c88ae4b93d \
--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.