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:b9a427df64bd846bacdce334edcd4c8670ca5545
Arguments:
--api-key ei_afa304e0aee9e77b2169fd64763a38c5fbc879316b1269d385a7d74edc48f58f --run-http-server 1337
For example, in a one-liner locally:
docker run --rm -it \
-p 1337:1337 \
public.ecr.aws/z9b3d4t5/inference-container:b9a427df64bd846bacdce334edcd4c8670ca5545 \
--api-key ei_afa304e0aee9e77b2169fd64763a38c5fbc879316b1269d385a7d74edc48f58f \
--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:fb4da57b25bc87f9c1a3a213f4cfb707879689b2
Arguments:
--api-key ei_afa304e0aee9e77b2169fd64763a38c5fbc879316b1269d385a7d74edc48f58f --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:fb4da57b25bc87f9c1a3a213f4cfb707879689b2 \
--api-key ei_afa304e0aee9e77b2169fd64763a38c5fbc879316b1269d385a7d74edc48f58f \
--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:5bd204a89ce10d622e3efe53deadbd8a52ee7b16
Arguments:
--api-key ei_afa304e0aee9e77b2169fd64763a38c5fbc879316b1269d385a7d74edc48f58f --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:5bd204a89ce10d622e3efe53deadbd8a52ee7b16 \
--api-key ei_afa304e0aee9e77b2169fd64763a38c5fbc879316b1269d385a7d74edc48f58f \
--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-6-0:8cd63a61a1f66686d59055a9d43fec0d37270af8
Arguments:
--api-key ei_afa304e0aee9e77b2169fd64763a38c5fbc879316b1269d385a7d74edc48f58f --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-6-0:8cd63a61a1f66686d59055a9d43fec0d37270af8 \
--api-key ei_afa304e0aee9e77b2169fd64763a38c5fbc879316b1269d385a7d74edc48f58f \
--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-702:0006cf1f29f6da9e13637f56f852c79fb7b76458
Arguments:
--api-key ei_afa304e0aee9e77b2169fd64763a38c5fbc879316b1269d385a7d74edc48f58f --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-702:0006cf1f29f6da9e13637f56f852c79fb7b76458 \
--api-key ei_afa304e0aee9e77b2169fd64763a38c5fbc879316b1269d385a7d74edc48f58f \
--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.
