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:34e23c4f5652937dfe806157378b25635004fcfd
Arguments:
--api-key ei_ca02642d8651f8e35acf2d1fee32f6013eff9a12b3eebe8f0e208ac31ebb237c --run-http-server 1337
For example, in a one-liner locally:
docker run --rm -it \
-p 1337:1337 \
public.ecr.aws/g7a8t7v6/inference-container:34e23c4f5652937dfe806157378b25635004fcfd \
--api-key ei_ca02642d8651f8e35acf2d1fee32f6013eff9a12b3eebe8f0e208ac31ebb237c \
--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:58ac078f56491767b49bb01fa87994bd6f1f7487
Arguments:
--api-key ei_ca02642d8651f8e35acf2d1fee32f6013eff9a12b3eebe8f0e208ac31ebb237c --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:58ac078f56491767b49bb01fa87994bd6f1f7487 \
--api-key ei_ca02642d8651f8e35acf2d1fee32f6013eff9a12b3eebe8f0e208ac31ebb237c \
--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:1d7a7adf39a4300c606a9c79bb5a2e11fdbd71ee
Arguments:
--api-key ei_ca02642d8651f8e35acf2d1fee32f6013eff9a12b3eebe8f0e208ac31ebb237c --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:1d7a7adf39a4300c606a9c79bb5a2e11fdbd71ee \
--api-key ei_ca02642d8651f8e35acf2d1fee32f6013eff9a12b3eebe8f0e208ac31ebb237c \
--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.