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:bdb45dec418c063bc322e775e318d7540e496147
Arguments:
--api-key ei_62ae42eec7d7154174cd5e17b949c8ed570d5bf99b1e3b8b6d3b118ed5c6ef3f --run-http-server 1337
For example, in a one-liner locally:
docker run --rm -it \
-p 1337:1337 \
public.ecr.aws/g7a8t7v6/inference-container:bdb45dec418c063bc322e775e318d7540e496147 \
--api-key ei_62ae42eec7d7154174cd5e17b949c8ed570d5bf99b1e3b8b6d3b118ed5c6ef3f \
--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:a81a7c9aa9d7a34b5dfc6244bd139e6c185f9144
Arguments:
--api-key ei_62ae42eec7d7154174cd5e17b949c8ed570d5bf99b1e3b8b6d3b118ed5c6ef3f --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:a81a7c9aa9d7a34b5dfc6244bd139e6c185f9144 \
--api-key ei_62ae42eec7d7154174cd5e17b949c8ed570d5bf99b1e3b8b6d3b118ed5c6ef3f \
--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:f30be133733dd5cb1e881256268c8ef5e9a80cb7
Arguments:
--api-key ei_62ae42eec7d7154174cd5e17b949c8ed570d5bf99b1e3b8b6d3b118ed5c6ef3f --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:f30be133733dd5cb1e881256268c8ef5e9a80cb7 \
--api-key ei_62ae42eec7d7154174cd5e17b949c8ed570d5bf99b1e3b8b6d3b118ed5c6ef3f \
--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-6-0:59bcd8ceaae2a29be137a3093bdc8f1fb80c4e10
Arguments:
--api-key ei_62ae42eec7d7154174cd5e17b949c8ed570d5bf99b1e3b8b6d3b118ed5c6ef3f --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-6-0:59bcd8ceaae2a29be137a3093bdc8f1fb80c4e10 \
--api-key ei_62ae42eec7d7154174cd5e17b949c8ed570d5bf99b1e3b8b6d3b118ed5c6ef3f \
--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.
