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:48d4431476d633d16a9ec3f0304cf7a87f2a227d
Arguments:
--api-key ei_f6f91c256ed35fc55bd092d1e9ea3aaa119de2e6992d96c2af7545379535e3a9 --run-http-server 1337
For example, in a one-liner locally:
docker run --rm -it \
-p 1337:1337 \
public.ecr.aws/g7a8t7v6/inference-container:48d4431476d633d16a9ec3f0304cf7a87f2a227d \
--api-key ei_f6f91c256ed35fc55bd092d1e9ea3aaa119de2e6992d96c2af7545379535e3a9 \
--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:313072088c88eda230efc50e5fcdd77a13b363d4
Arguments:
--api-key ei_f6f91c256ed35fc55bd092d1e9ea3aaa119de2e6992d96c2af7545379535e3a9 --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:313072088c88eda230efc50e5fcdd77a13b363d4 \
--api-key ei_f6f91c256ed35fc55bd092d1e9ea3aaa119de2e6992d96c2af7545379535e3a9 \
--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:57e830d93ac2ebed49561f72bbeaf1018f7902fd
Arguments:
--api-key ei_f6f91c256ed35fc55bd092d1e9ea3aaa119de2e6992d96c2af7545379535e3a9 --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:57e830d93ac2ebed49561f72bbeaf1018f7902fd \
--api-key ei_f6f91c256ed35fc55bd092d1e9ea3aaa119de2e6992d96c2af7545379535e3a9 \
--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-6-0:9c0407534ddc5065a2bf6aa4388b22cf07b1dd5e
Arguments:
--api-key ei_f6f91c256ed35fc55bd092d1e9ea3aaa119de2e6992d96c2af7545379535e3a9 --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-6-0:9c0407534ddc5065a2bf6aa4388b22cf07b1dd5e \
--api-key ei_f6f91c256ed35fc55bd092d1e9ea3aaa119de2e6992d96c2af7545379535e3a9 \
--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/g7a8t7v6/inference-container-qc-adreno-702:aa4217ae4509b3ece9c934c300bf5b0e5f2d5208
Arguments:
--api-key ei_f6f91c256ed35fc55bd092d1e9ea3aaa119de2e6992d96c2af7545379535e3a9 --run-http-server 1337
For example, in a one-liner locally:
docker run --rm -it \
-p 1337:1337 \
public.ecr.aws/g7a8t7v6/inference-container-qc-adreno-702:aa4217ae4509b3ece9c934c300bf5b0e5f2d5208 \
--api-key ei_f6f91c256ed35fc55bd092d1e9ea3aaa119de2e6992d96c2af7545379535e3a9 \
--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.
