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)
See the
CLI documentation for more information and setup instructions.
Alternatively, you can download your model for
below.
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:fd860993eaceaa83c435b5e8ab1bfcc1d6a31c7e
Arguments:
--api-key ei_533dbd995107a689665a741084b76d75dd47f7f61309211f04ecb2b5aaaed1cf --run-http-server 1337
For example, in a one-liner locally:
docker run --rm -it \
-p 1337:1337 \
public.ecr.aws/z9b3d4t5/inference-container:fd860993eaceaa83c435b5e8ab1bfcc1d6a31c7e \
--api-key ei_533dbd995107a689665a741084b76d75dd47f7f61309211f04ecb2b5aaaed1cf \
--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:17988e15ea7997f5894851879ddc92695aad65a7
Arguments:
--api-key ei_533dbd995107a689665a741084b76d75dd47f7f61309211f04ecb2b5aaaed1cf --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:17988e15ea7997f5894851879ddc92695aad65a7 \
--api-key ei_533dbd995107a689665a741084b76d75dd47f7f61309211f04ecb2b5aaaed1cf \
--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:f7297ea4d40fc1a159b0dfa8d80218d8895dc724
Arguments:
--api-key ei_533dbd995107a689665a741084b76d75dd47f7f61309211f04ecb2b5aaaed1cf --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:f7297ea4d40fc1a159b0dfa8d80218d8895dc724 \
--api-key ei_533dbd995107a689665a741084b76d75dd47f7f61309211f04ecb2b5aaaed1cf \
--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-6-0:571e2f8e1e5e824fd1447ccdb53d6ac2361eb9d0
Arguments:
--api-key ei_533dbd995107a689665a741084b76d75dd47f7f61309211f04ecb2b5aaaed1cf --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-6-0:571e2f8e1e5e824fd1447ccdb53d6ac2361eb9d0 \
--api-key ei_533dbd995107a689665a741084b76d75dd47f7f61309211f04ecb2b5aaaed1cf \
--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-702:90ee4f25a1091dba0eca773615fec66feb47d731
Arguments:
--api-key ei_533dbd995107a689665a741084b76d75dd47f7f61309211f04ecb2b5aaaed1cf --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-702:90ee4f25a1091dba0eca773615fec66feb47d731 \
--api-key ei_533dbd995107a689665a741084b76d75dd47f7f61309211f04ecb2b5aaaed1cf \
--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.
