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:f91508ca973162117269c1c83a9b4cf38dea4452
Arguments:
--api-key ei_37d9f9e459d546b36fa73c9ee5c5944d0b9598865ff5866bc17be9e565f3e07d --run-http-server 1337
For example, in a one-liner locally:
docker run --rm -it \
-p 1337:1337 \
public.ecr.aws/g7a8t7v6/inference-container:f91508ca973162117269c1c83a9b4cf38dea4452 \
--api-key ei_37d9f9e459d546b36fa73c9ee5c5944d0b9598865ff5866bc17be9e565f3e07d \
--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:fe61e54d5584221be271ea4899720e9d964b73f9
Arguments:
--api-key ei_37d9f9e459d546b36fa73c9ee5c5944d0b9598865ff5866bc17be9e565f3e07d --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:fe61e54d5584221be271ea4899720e9d964b73f9 \
--api-key ei_37d9f9e459d546b36fa73c9ee5c5944d0b9598865ff5866bc17be9e565f3e07d \
--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:b98e0417e19f6bc654904c2a9bf444ce63934002
Arguments:
--api-key ei_37d9f9e459d546b36fa73c9ee5c5944d0b9598865ff5866bc17be9e565f3e07d --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:b98e0417e19f6bc654904c2a9bf444ce63934002 \
--api-key ei_37d9f9e459d546b36fa73c9ee5c5944d0b9598865ff5866bc17be9e565f3e07d \
--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.