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/z9b3d4t5/inference-container:543f7950caf8db29261f69616450a00bc1ee0f38
Arguments:
--api-key ei_ebb075df22d59ee9e0a7388a2a749528db1e6c50f9d8bb6c93891bed325c5f51 --run-http-server 1337
For example, in a one-liner locally:
docker run --rm -it \
-p 1337:1337 \
public.ecr.aws/z9b3d4t5/inference-container:543f7950caf8db29261f69616450a00bc1ee0f38 \
--api-key ei_ebb075df22d59ee9e0a7388a2a749528db1e6c50f9d8bb6c93891bed325c5f51 \
--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/z9b3d4t5/inference-container-jetson:2f42a53d05c09f450e5503029822e70c6acf8de9
Arguments:
--api-key ei_ebb075df22d59ee9e0a7388a2a749528db1e6c50f9d8bb6c93891bed325c5f51 --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/z9b3d4t5/inference-container-jetson:2f42a53d05c09f450e5503029822e70c6acf8de9 \
--api-key ei_ebb075df22d59ee9e0a7388a2a749528db1e6c50f9d8bb6c93891bed325c5f51 \
--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/z9b3d4t5/inference-container-jetson-orin:0a03eee3725d93065020b9bee15af259c22e2c53
Arguments:
--api-key ei_ebb075df22d59ee9e0a7388a2a749528db1e6c50f9d8bb6c93891bed325c5f51 --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/z9b3d4t5/inference-container-jetson-orin:0a03eee3725d93065020b9bee15af259c22e2c53 \
--api-key ei_ebb075df22d59ee9e0a7388a2a749528db1e6c50f9d8bb6c93891bed325c5f51 \
--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.