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:841f9879680249d1efa3e34a9863d565e7badf69
Arguments:
--api-key ei_6ae0265abfa32609adfa7426da26841a48965a6839a3c216334af2a69f61d474 --run-http-server 1337
For example, in a one-liner locally:
docker run --rm -it \
-p 1337:1337 \
public.ecr.aws/g7a8t7v6/inference-container:841f9879680249d1efa3e34a9863d565e7badf69 \
--api-key ei_6ae0265abfa32609adfa7426da26841a48965a6839a3c216334af2a69f61d474 \
--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:5e5d38fcacee9138502a2a9cf85238b5d92f2d6a
Arguments:
--api-key ei_6ae0265abfa32609adfa7426da26841a48965a6839a3c216334af2a69f61d474 --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:5e5d38fcacee9138502a2a9cf85238b5d92f2d6a \
--api-key ei_6ae0265abfa32609adfa7426da26841a48965a6839a3c216334af2a69f61d474 \
--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:ec46acf2db84a75f0f1e3d45811ac8424dd83bbb
Arguments:
--api-key ei_6ae0265abfa32609adfa7426da26841a48965a6839a3c216334af2a69f61d474 --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:ec46acf2db84a75f0f1e3d45811ac8424dd83bbb \
--api-key ei_6ae0265abfa32609adfa7426da26841a48965a6839a3c216334af2a69f61d474 \
--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-6-0:4485730539d8af1538df66048f5c34a2c2d61b73
Arguments:
--api-key ei_6ae0265abfa32609adfa7426da26841a48965a6839a3c216334af2a69f61d474 --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-6-0:4485730539d8af1538df66048f5c34a2c2d61b73 \
--api-key ei_6ae0265abfa32609adfa7426da26841a48965a6839a3c216334af2a69f61d474 \
--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.
