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:78594ddd281465d5f1450d53b901826fd77bd098
Arguments:
--api-key ei_4691804bd5ef0118c1704f75f69836a8676bea053cd82158ce014d41ce001685 --run-http-server 1337
For example, in a one-liner locally:
docker run --rm -it \
-p 1337:1337 \
public.ecr.aws/g7a8t7v6/inference-container:78594ddd281465d5f1450d53b901826fd77bd098 \
--api-key ei_4691804bd5ef0118c1704f75f69836a8676bea053cd82158ce014d41ce001685 \
--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:a44db548f995ee7557f6ea16d512636ac3f629f9
Arguments:
--api-key ei_4691804bd5ef0118c1704f75f69836a8676bea053cd82158ce014d41ce001685 --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:a44db548f995ee7557f6ea16d512636ac3f629f9 \
--api-key ei_4691804bd5ef0118c1704f75f69836a8676bea053cd82158ce014d41ce001685 \
--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:f427ba7150c6ec5770ab88fe32f48cb4be14196d
Arguments:
--api-key ei_4691804bd5ef0118c1704f75f69836a8676bea053cd82158ce014d41ce001685 --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:f427ba7150c6ec5770ab88fe32f48cb4be14196d \
--api-key ei_4691804bd5ef0118c1704f75f69836a8676bea053cd82158ce014d41ce001685 \
--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:ddfce00889b90179ff2bb364b5e1a34c36be42c6
Arguments:
--api-key ei_4691804bd5ef0118c1704f75f69836a8676bea053cd82158ce014d41ce001685 --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:ddfce00889b90179ff2bb364b5e1a34c36be42c6 \
--api-key ei_4691804bd5ef0118c1704f75f69836a8676bea053cd82158ce014d41ce001685 \
--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.
