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:41eba1d6a77354d982ffe4be1063e0d01b5e5247
Arguments:
--api-key ei_d080baf0bd945116d037b5f9dfe6e5371ab1a48f6c9702fce6fd4b05cc56e194 --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:41eba1d6a77354d982ffe4be1063e0d01b5e5247 \
--api-key ei_d080baf0bd945116d037b5f9dfe6e5371ab1a48f6c9702fce6fd4b05cc56e194 \
--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:6aae2fed919bf8e65fc87b34fb6bdbd4797fc38d
Arguments:
--api-key ei_d080baf0bd945116d037b5f9dfe6e5371ab1a48f6c9702fce6fd4b05cc56e194 --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:6aae2fed919bf8e65fc87b34fb6bdbd4797fc38d \
--api-key ei_d080baf0bd945116d037b5f9dfe6e5371ab1a48f6c9702fce6fd4b05cc56e194 \
--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:38824ba5eeec2f291a79840dfd1a7ca2e863646f
Arguments:
--api-key ei_d080baf0bd945116d037b5f9dfe6e5371ab1a48f6c9702fce6fd4b05cc56e194 --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:38824ba5eeec2f291a79840dfd1a7ca2e863646f \
--api-key ei_d080baf0bd945116d037b5f9dfe6e5371ab1a48f6c9702fce6fd4b05cc56e194 \
--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-6-0:31b81b4c01fdfd6fa24e10392fd4c9778ceef485
Arguments:
--api-key ei_d080baf0bd945116d037b5f9dfe6e5371ab1a48f6c9702fce6fd4b05cc56e194 --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-6-0:31b81b4c01fdfd6fa24e10392fd4c9778ceef485 \
--api-key ei_d080baf0bd945116d037b5f9dfe6e5371ab1a48f6c9702fce6fd4b05cc56e194 \
--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.
