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:b7e8a024511c5f537ba841b431e2d11573e62f9d
Arguments:
--api-key ei_fa86cbcc0047cf1b3abc13ef0c4c207650ecbb6fac213ae8ae0ea5b1e506b8b4 --run-http-server 1337
For example, in a one-liner locally:
docker run --rm -it \
-p 1337:1337 \
public.ecr.aws/g7a8t7v6/inference-container:b7e8a024511c5f537ba841b431e2d11573e62f9d \
--api-key ei_fa86cbcc0047cf1b3abc13ef0c4c207650ecbb6fac213ae8ae0ea5b1e506b8b4 \
--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:9999215a73488663d24a75d2508101fc050d4f43
Arguments:
--api-key ei_fa86cbcc0047cf1b3abc13ef0c4c207650ecbb6fac213ae8ae0ea5b1e506b8b4 --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:9999215a73488663d24a75d2508101fc050d4f43 \
--api-key ei_fa86cbcc0047cf1b3abc13ef0c4c207650ecbb6fac213ae8ae0ea5b1e506b8b4 \
--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:f43cf25dc1a4ac3cfeea8da55d68c428d0754484
Arguments:
--api-key ei_fa86cbcc0047cf1b3abc13ef0c4c207650ecbb6fac213ae8ae0ea5b1e506b8b4 --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:f43cf25dc1a4ac3cfeea8da55d68c428d0754484 \
--api-key ei_fa86cbcc0047cf1b3abc13ef0c4c207650ecbb6fac213ae8ae0ea5b1e506b8b4 \
--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.