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:68c843971478437fe64c01a8e88a6e76753235b1
Arguments:
--api-key ei_bd22dc5f9fa9c54207bc01f7c37bafd95665a041d80649c0bd71e40b64ac848a --run-http-server 1337 --impulse-id undefined
For example, in a one-liner locally:
docker run --rm -it \
-p 1337:1337 \
public.ecr.aws/g7a8t7v6/inference-container:68c843971478437fe64c01a8e88a6e76753235b1 \
--api-key ei_bd22dc5f9fa9c54207bc01f7c37bafd95665a041d80649c0bd71e40b64ac848a \
--run-http-server 1337 \
--impulse-id undefined
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:4ea457271414f4d41d903fe2ca92b41d8822a7a0
Arguments:
--api-key ei_bd22dc5f9fa9c54207bc01f7c37bafd95665a041d80649c0bd71e40b64ac848a --run-http-server 1337 --impulse-id undefined
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:4ea457271414f4d41d903fe2ca92b41d8822a7a0 \
--api-key ei_bd22dc5f9fa9c54207bc01f7c37bafd95665a041d80649c0bd71e40b64ac848a \
--run-http-server 1337 \
--impulse-id undefined
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:d6fb1070a7c2a1c24d896ddb13c59d889e2503c9
Arguments:
--api-key ei_bd22dc5f9fa9c54207bc01f7c37bafd95665a041d80649c0bd71e40b64ac848a --run-http-server 1337 --impulse-id undefined
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:d6fb1070a7c2a1c24d896ddb13c59d889e2503c9 \
--api-key ei_bd22dc5f9fa9c54207bc01f7c37bafd95665a041d80649c0bd71e40b64ac848a \
--run-http-server 1337 \
--impulse-id undefined
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-6-0:8295e644b8736100b8b14d59c33038543983994c
Arguments:
--api-key ei_bd22dc5f9fa9c54207bc01f7c37bafd95665a041d80649c0bd71e40b64ac848a --run-http-server 1337 --impulse-id undefined
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-6-0:8295e644b8736100b8b14d59c33038543983994c \
--api-key ei_bd22dc5f9fa9c54207bc01f7c37bafd95665a041d80649c0bd71e40b64ac848a \
--run-http-server 1337 \
--impulse-id undefined
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.
