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:0b689d4a5e8259e0d6b3bdcdae863c6e7d92418e
Arguments:
--api-key ei_105bfddf531d682d3c5c87dd9d9393fa1628e54892e3809d692e6ae3200c8f06 --run-http-server 1337 --impulse-id 2
For example, in a one-liner locally:
docker run --rm -it \
-p 1337:1337 \
public.ecr.aws/g7a8t7v6/inference-container:0b689d4a5e8259e0d6b3bdcdae863c6e7d92418e \
--api-key ei_105bfddf531d682d3c5c87dd9d9393fa1628e54892e3809d692e6ae3200c8f06 \
--run-http-server 1337 \
--impulse-id 2
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:71f71f0ca030b9992b7b167cae85f105bc82b97d
Arguments:
--api-key ei_105bfddf531d682d3c5c87dd9d9393fa1628e54892e3809d692e6ae3200c8f06 --run-http-server 1337 --impulse-id 2
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:71f71f0ca030b9992b7b167cae85f105bc82b97d \
--api-key ei_105bfddf531d682d3c5c87dd9d9393fa1628e54892e3809d692e6ae3200c8f06 \
--run-http-server 1337 \
--impulse-id 2
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:219d31f8a9fc02f5712cd4984e8bc0069b0126e6
Arguments:
--api-key ei_105bfddf531d682d3c5c87dd9d9393fa1628e54892e3809d692e6ae3200c8f06 --run-http-server 1337 --impulse-id 2
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:219d31f8a9fc02f5712cd4984e8bc0069b0126e6 \
--api-key ei_105bfddf531d682d3c5c87dd9d9393fa1628e54892e3809d692e6ae3200c8f06 \
--run-http-server 1337 \
--impulse-id 2
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-6-0:6d9fe8bc81590eb1c8daa4a657360926067a7314
Arguments:
--api-key ei_105bfddf531d682d3c5c87dd9d9393fa1628e54892e3809d692e6ae3200c8f06 --run-http-server 1337 --impulse-id 2
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-6-0:6d9fe8bc81590eb1c8daa4a657360926067a7314 \
--api-key ei_105bfddf531d682d3c5c87dd9d9393fa1628e54892e3809d692e6ae3200c8f06 \
--run-http-server 1337 \
--impulse-id 2
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.
