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:9bd53072a90fe19eb2acb0035e1e94a9543538de
Arguments:
--api-key ei_88ed41df14820518af9a56a354cc65e66592993842a69708bb3a43d5dc3bb8db --run-http-server 1337
For example, in a one-liner locally:
docker run --rm -it \
-p 1337:1337 \
public.ecr.aws/g7a8t7v6/inference-container:9bd53072a90fe19eb2acb0035e1e94a9543538de \
--api-key ei_88ed41df14820518af9a56a354cc65e66592993842a69708bb3a43d5dc3bb8db \
--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 (JetPack 4.6.x), use:
Container:
public.ecr.aws/g7a8t7v6/inference-container-jetson:5ac7ba5ff4c5b566682a90a1021c32e7e312de04
Arguments:
--api-key ei_88ed41df14820518af9a56a354cc65e66592993842a69708bb3a43d5dc3bb8db --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:5ac7ba5ff4c5b566682a90a1021c32e7e312de04 \
--api-key ei_88ed41df14820518af9a56a354cc65e66592993842a69708bb3a43d5dc3bb8db \
--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 Orin's GPUs (JetPack 5.1.x), use:
Container:
public.ecr.aws/g7a8t7v6/inference-container-jetson-orin:715fb69038f0fec900b6e1d315189efd539aec08
Arguments:
--api-key ei_88ed41df14820518af9a56a354cc65e66592993842a69708bb3a43d5dc3bb8db --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:715fb69038f0fec900b6e1d315189efd539aec08 \
--api-key ei_88ed41df14820518af9a56a354cc65e66592993842a69708bb3a43d5dc3bb8db \
--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 Orin's GPUs (JetPack 6.0), use:
Container:
public.ecr.aws/g7a8t7v6/inference-container-jetson-orin-6-0:e306f7b69cc1a305ebe489736b64b4bd65489e13
Arguments:
--api-key ei_88ed41df14820518af9a56a354cc65e66592993842a69708bb3a43d5dc3bb8db --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-6-0:e306f7b69cc1a305ebe489736b64b4bd65489e13 \
--api-key ei_88ed41df14820518af9a56a354cc65e66592993842a69708bb3a43d5dc3bb8db \
--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.