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:af67e8b6cfae9da1f42415ea59af7aaa2a00bceb
Arguments:
--api-key ei_775e8c4c1c7f41e263334d0bd7012411fe5e2c8146f1501d32f089f8d3a9bc59 --run-http-server 1337 --impulse-id 2
For example, in a one-liner locally:
docker run --rm -it \
-p 1337:1337 \
public.ecr.aws/g7a8t7v6/inference-container:af67e8b6cfae9da1f42415ea59af7aaa2a00bceb \
--api-key ei_775e8c4c1c7f41e263334d0bd7012411fe5e2c8146f1501d32f089f8d3a9bc59 \
--run-http-server 1337 \
--impulse-id 2
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:963f241df9a154f995dd0e9732e7047b97122e7c
Arguments:
--api-key ei_775e8c4c1c7f41e263334d0bd7012411fe5e2c8146f1501d32f089f8d3a9bc59 --run-http-server 1337 --impulse-id 2
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:963f241df9a154f995dd0e9732e7047b97122e7c \
--api-key ei_775e8c4c1c7f41e263334d0bd7012411fe5e2c8146f1501d32f089f8d3a9bc59 \
--run-http-server 1337 \
--impulse-id 2
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:29d3d970172776c14eb3eb2f67f7e276acd97897
Arguments:
--api-key ei_775e8c4c1c7f41e263334d0bd7012411fe5e2c8146f1501d32f089f8d3a9bc59 --run-http-server 1337 --impulse-id 2
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:29d3d970172776c14eb3eb2f67f7e276acd97897 \
--api-key ei_775e8c4c1c7f41e263334d0bd7012411fe5e2c8146f1501d32f089f8d3a9bc59 \
--run-http-server 1337 \
--impulse-id 2
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:e4549a1a5397d11caacabde25718d0943fb3bec5
Arguments:
--api-key ei_775e8c4c1c7f41e263334d0bd7012411fe5e2c8146f1501d32f089f8d3a9bc59 --run-http-server 1337 --impulse-id 2
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:e4549a1a5397d11caacabde25718d0943fb3bec5 \
--api-key ei_775e8c4c1c7f41e263334d0bd7012411fe5e2c8146f1501d32f089f8d3a9bc59 \
--run-http-server 1337 \
--impulse-id 2
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.
