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:d3e1fecb1442ec2336b628def2b342e6cf4bfade
Arguments:
--api-key ei_ce7c26bec12aec2fa4ecd7cb652b18a314905c28b277e622169b1799f29a505e --run-http-server 1337
For example, in a one-liner locally:
docker run --rm -it \
-p 1337:1337 \
public.ecr.aws/g7a8t7v6/inference-container:d3e1fecb1442ec2336b628def2b342e6cf4bfade \
--api-key ei_ce7c26bec12aec2fa4ecd7cb652b18a314905c28b277e622169b1799f29a505e \
--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:8cff41327357bcd85bb652679f481ee2c9afe590
Arguments:
--api-key ei_ce7c26bec12aec2fa4ecd7cb652b18a314905c28b277e622169b1799f29a505e --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:8cff41327357bcd85bb652679f481ee2c9afe590 \
--api-key ei_ce7c26bec12aec2fa4ecd7cb652b18a314905c28b277e622169b1799f29a505e \
--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:ee4746b7ad1ec843711cd33fd776f7dd66db19ca
Arguments:
--api-key ei_ce7c26bec12aec2fa4ecd7cb652b18a314905c28b277e622169b1799f29a505e --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:ee4746b7ad1ec843711cd33fd776f7dd66db19ca \
--api-key ei_ce7c26bec12aec2fa4ecd7cb652b18a314905c28b277e622169b1799f29a505e \
--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-6-0:74569fbd8ea04344229d16b6a3187bf558f0db81
Arguments:
--api-key ei_ce7c26bec12aec2fa4ecd7cb652b18a314905c28b277e622169b1799f29a505e --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-6-0:74569fbd8ea04344229d16b6a3187bf558f0db81 \
--api-key ei_ce7c26bec12aec2fa4ecd7cb652b18a314905c28b277e622169b1799f29a505e \
--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.
