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/z9b3d4t5/inference-container:e53b4ff7c1382dc4cfe4ef3c789fa1ce58e23716
Arguments:
--api-key ei_1b152343f42772fc018b2df700a7d6de1b48fa05541c4b18b84498dac61cfb87 --run-http-server 1337
For example, in a one-liner locally:
docker run --rm -it \
-p 1337:1337 \
public.ecr.aws/z9b3d4t5/inference-container:e53b4ff7c1382dc4cfe4ef3c789fa1ce58e23716 \
--api-key ei_1b152343f42772fc018b2df700a7d6de1b48fa05541c4b18b84498dac61cfb87 \
--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/z9b3d4t5/inference-container-jetson:653b7a9b7e83c7b4ac1e88323a48167e9b1d8021
Arguments:
--api-key ei_1b152343f42772fc018b2df700a7d6de1b48fa05541c4b18b84498dac61cfb87 --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/z9b3d4t5/inference-container-jetson:653b7a9b7e83c7b4ac1e88323a48167e9b1d8021 \
--api-key ei_1b152343f42772fc018b2df700a7d6de1b48fa05541c4b18b84498dac61cfb87 \
--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/z9b3d4t5/inference-container-jetson-orin:0dd83e5a2cd07bd90fc6a4b12f5cdcc7370e42f2
Arguments:
--api-key ei_1b152343f42772fc018b2df700a7d6de1b48fa05541c4b18b84498dac61cfb87 --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/z9b3d4t5/inference-container-jetson-orin:0dd83e5a2cd07bd90fc6a4b12f5cdcc7370e42f2 \
--api-key ei_1b152343f42772fc018b2df700a7d6de1b48fa05541c4b18b84498dac61cfb87 \
--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/z9b3d4t5/inference-container-jetson-orin-6-0:869646ac5423e00381457a5651939f01659f4710
Arguments:
--api-key ei_1b152343f42772fc018b2df700a7d6de1b48fa05541c4b18b84498dac61cfb87 --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/z9b3d4t5/inference-container-jetson-orin-6-0:869646ac5423e00381457a5651939f01659f4710 \
--api-key ei_1b152343f42772fc018b2df700a7d6de1b48fa05541c4b18b84498dac61cfb87 \
--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/z9b3d4t5/inference-container-qc-adreno-702:632e5551de717bc35182f26b40159aae516220e1
Arguments:
--api-key ei_1b152343f42772fc018b2df700a7d6de1b48fa05541c4b18b84498dac61cfb87 --run-http-server 1337
For example, in a one-liner locally:
docker run --rm -it --device /dev/dri \
-p 1337:1337 \
public.ecr.aws/z9b3d4t5/inference-container-qc-adreno-702:632e5551de717bc35182f26b40159aae516220e1 \
--api-key ei_1b152343f42772fc018b2df700a7d6de1b48fa05541c4b18b84498dac61cfb87 \
--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.
