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:2b7d49c71b49c882ca7d066ea60148d34714e643
Arguments:
--api-key ei_fe0d2a6242ff141d8415a379eb84b81372f19201d1d2600213a483dbb615de78 --run-http-server 1337
For example, in a one-liner locally:
docker run --rm -it \
-p 1337:1337 \
public.ecr.aws/z9b3d4t5/inference-container:2b7d49c71b49c882ca7d066ea60148d34714e643 \
--api-key ei_fe0d2a6242ff141d8415a379eb84b81372f19201d1d2600213a483dbb615de78 \
--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:c997327b412e1b63a37f3e1e42b7165f357f6364
Arguments:
--api-key ei_fe0d2a6242ff141d8415a379eb84b81372f19201d1d2600213a483dbb615de78 --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:c997327b412e1b63a37f3e1e42b7165f357f6364 \
--api-key ei_fe0d2a6242ff141d8415a379eb84b81372f19201d1d2600213a483dbb615de78 \
--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:e2fa5589f7ac23471f029203b6596b99c4749ff9
Arguments:
--api-key ei_fe0d2a6242ff141d8415a379eb84b81372f19201d1d2600213a483dbb615de78 --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:e2fa5589f7ac23471f029203b6596b99c4749ff9 \
--api-key ei_fe0d2a6242ff141d8415a379eb84b81372f19201d1d2600213a483dbb615de78 \
--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-6-0:83f038bff95cca7fc39d35f592811dc33aa7aa23
Arguments:
--api-key ei_fe0d2a6242ff141d8415a379eb84b81372f19201d1d2600213a483dbb615de78 --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-6-0:83f038bff95cca7fc39d35f592811dc33aa7aa23 \
--api-key ei_fe0d2a6242ff141d8415a379eb84b81372f19201d1d2600213a483dbb615de78 \
--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:f7c0690d8ec21050e3971fe2d852a2bf56197cec
Arguments:
--api-key ei_fe0d2a6242ff141d8415a379eb84b81372f19201d1d2600213a483dbb615de78 --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:f7c0690d8ec21050e3971fe2d852a2bf56197cec \
--api-key ei_fe0d2a6242ff141d8415a379eb84b81372f19201d1d2600213a483dbb615de78 \
--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.
