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:ca68f12f67f05248098e8af6f2c3cec8c20a3d35
Arguments:
--api-key ei_c13c0fa70780376a79c64ab06702c9bb7d49166506e174586913d9043a68ca34 --run-http-server 1337
For example, in a one-liner locally:
docker run --rm -it \
-p 1337:1337 \
public.ecr.aws/g7a8t7v6/inference-container:ca68f12f67f05248098e8af6f2c3cec8c20a3d35 \
--api-key ei_c13c0fa70780376a79c64ab06702c9bb7d49166506e174586913d9043a68ca34 \
--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:1328dff7b279d8de0a31ff498589556621488f73
Arguments:
--api-key ei_c13c0fa70780376a79c64ab06702c9bb7d49166506e174586913d9043a68ca34 --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:1328dff7b279d8de0a31ff498589556621488f73 \
--api-key ei_c13c0fa70780376a79c64ab06702c9bb7d49166506e174586913d9043a68ca34 \
--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:6e9a186f317479455e1079335b114a6fce111435
Arguments:
--api-key ei_c13c0fa70780376a79c64ab06702c9bb7d49166506e174586913d9043a68ca34 --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:6e9a186f317479455e1079335b114a6fce111435 \
--api-key ei_c13c0fa70780376a79c64ab06702c9bb7d49166506e174586913d9043a68ca34 \
--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-6-0:343e1da04bf562e605778b2757761362c81002ab
Arguments:
--api-key ei_c13c0fa70780376a79c64ab06702c9bb7d49166506e174586913d9043a68ca34 --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-6-0:343e1da04bf562e605778b2757761362c81002ab \
--api-key ei_c13c0fa70780376a79c64ab06702c9bb7d49166506e174586913d9043a68ca34 \
--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.
