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:23d6195a1839044c1872ba6a422fdf6f15645e23
Arguments:
--api-key ei_8984190737c484f4bba800795c45948dc95bc84e8998775edb076d968654e705 --run-http-server 1337
For example, in a one-liner locally:
docker run --rm -it \
-p 1337:1337 \
public.ecr.aws/g7a8t7v6/inference-container:23d6195a1839044c1872ba6a422fdf6f15645e23 \
--api-key ei_8984190737c484f4bba800795c45948dc95bc84e8998775edb076d968654e705 \
--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/g7a8t7v6/inference-container-jetson:bf5a1dfa4e52b70c2e846d63bc6132457c7bb7be
Arguments:
--api-key ei_8984190737c484f4bba800795c45948dc95bc84e8998775edb076d968654e705 --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:bf5a1dfa4e52b70c2e846d63bc6132457c7bb7be \
--api-key ei_8984190737c484f4bba800795c45948dc95bc84e8998775edb076d968654e705 \
--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/g7a8t7v6/inference-container-jetson-orin:62be65e582d138e7cf2f6cffe876a46d73cb2068
Arguments:
--api-key ei_8984190737c484f4bba800795c45948dc95bc84e8998775edb076d968654e705 --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:62be65e582d138e7cf2f6cffe876a46d73cb2068 \
--api-key ei_8984190737c484f4bba800795c45948dc95bc84e8998775edb076d968654e705 \
--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/g7a8t7v6/inference-container-jetson-orin-6-0:296ea951a0a998d8967fc2e45d6b634253cac039
Arguments:
--api-key ei_8984190737c484f4bba800795c45948dc95bc84e8998775edb076d968654e705 --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-6-0:296ea951a0a998d8967fc2e45d6b634253cac039 \
--api-key ei_8984190737c484f4bba800795c45948dc95bc84e8998775edb076d968654e705 \
--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.
