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:d64e71863f594e68a69194e805ffa1a70c0bd511
Arguments:
--api-key ei_924657a44932663be08a3fe43112f5bcc8d48e1c598c0ad07cfbf92c40a34ac6 --run-http-server 1337
For example, in a one-liner locally:
docker run --rm -it \
-p 1337:1337 \
public.ecr.aws/g7a8t7v6/inference-container:d64e71863f594e68a69194e805ffa1a70c0bd511 \
--api-key ei_924657a44932663be08a3fe43112f5bcc8d48e1c598c0ad07cfbf92c40a34ac6 \
--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:e1d689201416a04f5d74d77e842b9d12b9209222
Arguments:
--api-key ei_924657a44932663be08a3fe43112f5bcc8d48e1c598c0ad07cfbf92c40a34ac6 --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:e1d689201416a04f5d74d77e842b9d12b9209222 \
--api-key ei_924657a44932663be08a3fe43112f5bcc8d48e1c598c0ad07cfbf92c40a34ac6 \
--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:d12f36f9cce4944b264af5aa18e97e19d831cec7
Arguments:
--api-key ei_924657a44932663be08a3fe43112f5bcc8d48e1c598c0ad07cfbf92c40a34ac6 --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:d12f36f9cce4944b264af5aa18e97e19d831cec7 \
--api-key ei_924657a44932663be08a3fe43112f5bcc8d48e1c598c0ad07cfbf92c40a34ac6 \
--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.