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:04e87b7e9d6737ffb3b1ccf7fac079fb4cf47f05
Arguments:
--api-key ei_5e10d3bc3cb374e136a6856a96bfedb6d9ed26a979f74675794849729163d7c0 --run-http-server 1337
For example, in a one-liner locally:
docker run --rm -it \
-p 1337:1337 \
public.ecr.aws/g7a8t7v6/inference-container:04e87b7e9d6737ffb3b1ccf7fac079fb4cf47f05 \
--api-key ei_5e10d3bc3cb374e136a6856a96bfedb6d9ed26a979f74675794849729163d7c0 \
--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, use:
Container:
public.ecr.aws/g7a8t7v6/inference-container-jetson:16f16a026210b2ac680c32753bf4649a438c7b95
Arguments:
--api-key ei_5e10d3bc3cb374e136a6856a96bfedb6d9ed26a979f74675794849729163d7c0 --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:16f16a026210b2ac680c32753bf4649a438c7b95 \
--api-key ei_5e10d3bc3cb374e136a6856a96bfedb6d9ed26a979f74675794849729163d7c0 \
--run-http-server 1337
This automatically builds and downloads the latest model with TensorRT support, 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, use:
Container:
public.ecr.aws/g7a8t7v6/inference-container-jetson-orin:64d90b41b41f4479194bd64fab8546359bac5911
Arguments:
--api-key ei_5e10d3bc3cb374e136a6856a96bfedb6d9ed26a979f74675794849729163d7c0 --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:64d90b41b41f4479194bd64fab8546359bac5911 \
--api-key ei_5e10d3bc3cb374e136a6856a96bfedb6d9ed26a979f74675794849729163d7c0 \
--run-http-server 1337
This automatically builds and downloads the latest model with TensorRT support, 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.