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:59c2133034f3a194b5c23fa429a4fb335d22421c
Arguments:
--api-key ei_d2a022ab384e3b8771cb1992d4c21a60f1b5a1442261dc01f15c7f05f32b2d2e --run-http-server 1337
For example, in a one-liner locally:
docker run --rm -it \
-p 1337:1337 \
public.ecr.aws/g7a8t7v6/inference-container:59c2133034f3a194b5c23fa429a4fb335d22421c \
--api-key ei_d2a022ab384e3b8771cb1992d4c21a60f1b5a1442261dc01f15c7f05f32b2d2e \
--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.x), use:
Container:
public.ecr.aws/g7a8t7v6/inference-container-jetson:ab05d9658b76a4d0003c78983adc14928107cee0
Arguments:
--api-key ei_d2a022ab384e3b8771cb1992d4c21a60f1b5a1442261dc01f15c7f05f32b2d2e --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:ab05d9658b76a4d0003c78983adc14928107cee0 \
--api-key ei_d2a022ab384e3b8771cb1992d4c21a60f1b5a1442261dc01f15c7f05f32b2d2e \
--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 Orin's GPUs (JetPack 5.1.x), use:
Container:
public.ecr.aws/g7a8t7v6/inference-container-jetson-orin:fd0ff6fcfa603c0360dfb87968769e6810f11629
Arguments:
--api-key ei_d2a022ab384e3b8771cb1992d4c21a60f1b5a1442261dc01f15c7f05f32b2d2e --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:fd0ff6fcfa603c0360dfb87968769e6810f11629 \
--api-key ei_d2a022ab384e3b8771cb1992d4c21a60f1b5a1442261dc01f15c7f05f32b2d2e \
--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 Orin's GPUs (JetPack 6.0), use:
Container:
public.ecr.aws/g7a8t7v6/inference-container-jetson-orin-6-0:dd5ea99681aa6cd87d314c8a0f22dbe099186cd8
Arguments:
--api-key ei_d2a022ab384e3b8771cb1992d4c21a60f1b5a1442261dc01f15c7f05f32b2d2e --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:dd5ea99681aa6cd87d314c8a0f22dbe099186cd8 \
--api-key ei_d2a022ab384e3b8771cb1992d4c21a60f1b5a1442261dc01f15c7f05f32b2d2e \
--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.
