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)
See the
CLI documentation for more information and setup instructions.
Alternatively, you can download your model for
below.
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:53f01aef9aea14f0350a73bfaf198ceccfe19647
Arguments:
--api-key ei_c3775b1a9520d460a32fd7d72af9308453feba499af7fe78a68014803a214ff2 --run-http-server 1337
For example, in a one-liner locally:
docker run --rm -it \
-p 1337:1337 \
public.ecr.aws/z9b3d4t5/inference-container:53f01aef9aea14f0350a73bfaf198ceccfe19647 \
--api-key ei_c3775b1a9520d460a32fd7d72af9308453feba499af7fe78a68014803a214ff2 \
--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/z9b3d4t5/inference-container-jetson:b2e2a2324624ed3ea267327a4b74a2101b2f6e72
Arguments:
--api-key ei_c3775b1a9520d460a32fd7d72af9308453feba499af7fe78a68014803a214ff2 --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:b2e2a2324624ed3ea267327a4b74a2101b2f6e72 \
--api-key ei_c3775b1a9520d460a32fd7d72af9308453feba499af7fe78a68014803a214ff2 \
--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/z9b3d4t5/inference-container-jetson-orin:2e86fb872396178dfe6fc539cf4ffd4ae9cef4b5
Arguments:
--api-key ei_c3775b1a9520d460a32fd7d72af9308453feba499af7fe78a68014803a214ff2 --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:2e86fb872396178dfe6fc539cf4ffd4ae9cef4b5 \
--api-key ei_c3775b1a9520d460a32fd7d72af9308453feba499af7fe78a68014803a214ff2 \
--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/z9b3d4t5/inference-container-jetson-orin-6-0:a5bb74ee51d5067410d5a6de139c68d78039a0da
Arguments:
--api-key ei_c3775b1a9520d460a32fd7d72af9308453feba499af7fe78a68014803a214ff2 --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-6-0:a5bb74ee51d5067410d5a6de139c68d78039a0da \
--api-key ei_c3775b1a9520d460a32fd7d72af9308453feba499af7fe78a68014803a214ff2 \
--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 Qualcomm Adreno 702 GPUs, use:
Container:
public.ecr.aws/z9b3d4t5/inference-container-qc-adreno-702:5602e0db2ad09ac92a94ee80249a9eb13caac6c7
Arguments:
--api-key ei_c3775b1a9520d460a32fd7d72af9308453feba499af7fe78a68014803a214ff2 --run-http-server 1337
For example, in a one-liner locally:
docker run --rm -it --device /dev/dri \
-p 1337:1337 \
public.ecr.aws/z9b3d4t5/inference-container-qc-adreno-702:5602e0db2ad09ac92a94ee80249a9eb13caac6c7 \
--api-key ei_c3775b1a9520d460a32fd7d72af9308453feba499af7fe78a68014803a214ff2 \
--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.
