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:27e1f7a7984c04496751cf9b73c68ed130279a64
Arguments:
--api-key ei_69615138086783a6e29d2b6959840ad9186542fea5862c2a2cbff16846bec75a --run-http-server 1337 --impulse-id undefined
For example, in a one-liner locally:
docker run --rm -it \
-p 1337:1337 \
public.ecr.aws/g7a8t7v6/inference-container:27e1f7a7984c04496751cf9b73c68ed130279a64 \
--api-key ei_69615138086783a6e29d2b6959840ad9186542fea5862c2a2cbff16846bec75a \
--run-http-server 1337 \
--impulse-id undefined
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:cdbb532393e95a798a17987568f8b6bd7af6200f
Arguments:
--api-key ei_69615138086783a6e29d2b6959840ad9186542fea5862c2a2cbff16846bec75a --run-http-server 1337 --impulse-id undefined
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:cdbb532393e95a798a17987568f8b6bd7af6200f \
--api-key ei_69615138086783a6e29d2b6959840ad9186542fea5862c2a2cbff16846bec75a \
--run-http-server 1337 \
--impulse-id undefined
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:851fcedf2d7123a82b43a858eb6c5d1e19021d71
Arguments:
--api-key ei_69615138086783a6e29d2b6959840ad9186542fea5862c2a2cbff16846bec75a --run-http-server 1337 --impulse-id undefined
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:851fcedf2d7123a82b43a858eb6c5d1e19021d71 \
--api-key ei_69615138086783a6e29d2b6959840ad9186542fea5862c2a2cbff16846bec75a \
--run-http-server 1337 \
--impulse-id undefined
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-6-0:432350de759cb850dc4762086258d740da0354f0
Arguments:
--api-key ei_69615138086783a6e29d2b6959840ad9186542fea5862c2a2cbff16846bec75a --run-http-server 1337 --impulse-id undefined
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-6-0:432350de759cb850dc4762086258d740da0354f0 \
--api-key ei_69615138086783a6e29d2b6959840ad9186542fea5862c2a2cbff16846bec75a \
--run-http-server 1337 \
--impulse-id undefined
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.