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:6e12db77285099efb67f4306143422d59f70c06e
Arguments:
--api-key ei_c11b94d2b04f654be76eb68ece5733fffc5ee2e95ab09bfc090e7073cf4d2a54 --run-http-server 1337
For example, in a one-liner locally:
docker run --rm -it \
-p 1337:1337 \
public.ecr.aws/z9b3d4t5/inference-container:6e12db77285099efb67f4306143422d59f70c06e \
--api-key ei_c11b94d2b04f654be76eb68ece5733fffc5ee2e95ab09bfc090e7073cf4d2a54 \
--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:52ea0ddf2cdeedfab18836434beaf63d5a2e585e
Arguments:
--api-key ei_c11b94d2b04f654be76eb68ece5733fffc5ee2e95ab09bfc090e7073cf4d2a54 --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:52ea0ddf2cdeedfab18836434beaf63d5a2e585e \
--api-key ei_c11b94d2b04f654be76eb68ece5733fffc5ee2e95ab09bfc090e7073cf4d2a54 \
--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:22c99733881a4596c2fbd460cc882a47baea846a
Arguments:
--api-key ei_c11b94d2b04f654be76eb68ece5733fffc5ee2e95ab09bfc090e7073cf4d2a54 --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:22c99733881a4596c2fbd460cc882a47baea846a \
--api-key ei_c11b94d2b04f654be76eb68ece5733fffc5ee2e95ab09bfc090e7073cf4d2a54 \
--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.