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:a4bbdfd0422c5d5288de234c186d0b145cfcb90e
Arguments:
--api-key ei_b2585dfb48d4eff879088ece9c2360c79c40d9f5a2e0348b415a8b510d0f3ef7 --run-http-server 1337
For example, in a one-liner locally:
docker run --rm -it \
-p 1337:1337 \
public.ecr.aws/z9b3d4t5/inference-container:a4bbdfd0422c5d5288de234c186d0b145cfcb90e \
--api-key ei_b2585dfb48d4eff879088ece9c2360c79c40d9f5a2e0348b415a8b510d0f3ef7 \
--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:65278793a40cbcbaa408c786b663e4cb4e86ff04
Arguments:
--api-key ei_b2585dfb48d4eff879088ece9c2360c79c40d9f5a2e0348b415a8b510d0f3ef7 --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:65278793a40cbcbaa408c786b663e4cb4e86ff04 \
--api-key ei_b2585dfb48d4eff879088ece9c2360c79c40d9f5a2e0348b415a8b510d0f3ef7 \
--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:f35dc060bbdfc8514b5dc303b6e046be086d1059
Arguments:
--api-key ei_b2585dfb48d4eff879088ece9c2360c79c40d9f5a2e0348b415a8b510d0f3ef7 --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:f35dc060bbdfc8514b5dc303b6e046be086d1059 \
--api-key ei_b2585dfb48d4eff879088ece9c2360c79c40d9f5a2e0348b415a8b510d0f3ef7 \
--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.