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:387d018de0b88f9de0e119f06dd16a6b5a7146e2
Arguments:
--api-key ei_9abf83552b1be9eb0cb7cccba0a4ea409157ed04dcff1d6d51113e1a79adf14f --run-http-server 1337
For example, in a one-liner locally:
docker run --rm -it \
-p 1337:1337 \
public.ecr.aws/z9b3d4t5/inference-container:387d018de0b88f9de0e119f06dd16a6b5a7146e2 \
--api-key ei_9abf83552b1be9eb0cb7cccba0a4ea409157ed04dcff1d6d51113e1a79adf14f \
--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:a3a22b1ca6d0dc810e01a1f3b2a23e1d4496bd2d
Arguments:
--api-key ei_9abf83552b1be9eb0cb7cccba0a4ea409157ed04dcff1d6d51113e1a79adf14f --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:a3a22b1ca6d0dc810e01a1f3b2a23e1d4496bd2d \
--api-key ei_9abf83552b1be9eb0cb7cccba0a4ea409157ed04dcff1d6d51113e1a79adf14f \
--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:69fb65019837543dc3ed8c068487b2ea8c4518be
Arguments:
--api-key ei_9abf83552b1be9eb0cb7cccba0a4ea409157ed04dcff1d6d51113e1a79adf14f --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:69fb65019837543dc3ed8c068487b2ea8c4518be \
--api-key ei_9abf83552b1be9eb0cb7cccba0a4ea409157ed04dcff1d6d51113e1a79adf14f \
--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:b4f778626b710594874006b5e119c04250d156eb
Arguments:
--api-key ei_9abf83552b1be9eb0cb7cccba0a4ea409157ed04dcff1d6d51113e1a79adf14f --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:b4f778626b710594874006b5e119c04250d156eb \
--api-key ei_9abf83552b1be9eb0cb7cccba0a4ea409157ed04dcff1d6d51113e1a79adf14f \
--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:ac980f6b8f5d99fb935c06205f69f17fd4bdedc4
Arguments:
--api-key ei_9abf83552b1be9eb0cb7cccba0a4ea409157ed04dcff1d6d51113e1a79adf14f --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:ac980f6b8f5d99fb935c06205f69f17fd4bdedc4 \
--api-key ei_9abf83552b1be9eb0cb7cccba0a4ea409157ed04dcff1d6d51113e1a79adf14f \
--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.
