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:3b2631c010873f04d84f04a93d439b15cc56a501
Arguments:
--api-key ei_e678cda6c148086af711432e1bdedd3da0ea321edfab1c5e251048d37723647c --run-http-server 1337
For example, in a one-liner locally:
docker run --rm -it \
-p 1337:1337 \
public.ecr.aws/g7a8t7v6/inference-container:3b2631c010873f04d84f04a93d439b15cc56a501 \
--api-key ei_e678cda6c148086af711432e1bdedd3da0ea321edfab1c5e251048d37723647c \
--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, use:
Container:
public.ecr.aws/g7a8t7v6/inference-container-jetson:decac1470a4f781c08e12b5f14590aec61f838ba
Arguments:
--api-key ei_e678cda6c148086af711432e1bdedd3da0ea321edfab1c5e251048d37723647c --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/g7a8t7v6/inference-container-jetson:decac1470a4f781c08e12b5f14590aec61f838ba \
--api-key ei_e678cda6c148086af711432e1bdedd3da0ea321edfab1c5e251048d37723647c \
--run-http-server 1337
This automatically builds and downloads the latest model with TensorRT support, 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, use:
Container:
public.ecr.aws/g7a8t7v6/inference-container-jetson-orin:e01aa2c050c3a632c583c7dfdc4f448a51068e69
Arguments:
--api-key ei_e678cda6c148086af711432e1bdedd3da0ea321edfab1c5e251048d37723647c --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/g7a8t7v6/inference-container-jetson-orin:e01aa2c050c3a632c583c7dfdc4f448a51068e69 \
--api-key ei_e678cda6c148086af711432e1bdedd3da0ea321edfab1c5e251048d37723647c \
--run-http-server 1337
This automatically builds and downloads the latest model with TensorRT support, 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.