wio-shake-lift - Deployment

Deb / wio-shake-lift Public

Target: Cortex-M4F 80MHz Clone this project

Configure your deployment

You can deploy your impulse to any device. This makes the model run without an internet connection, minimizes latency, and runs with minimal power consumption. Read more.

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:9eb30b5b67d462a6569f411dd03a44c9d1993993

Arguments:

--api-key ei_df94e6fd285e68de1d515d56166378f25679362df5362c9f122fc2dc2369bb0e --run-http-server 1337 --impulse-id 3

Ports to expose:

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:9eb30b5b67d462a6569f411dd03a44c9d1993993 \
        --api-key ei_df94e6fd285e68de1d515d56166378f25679362df5362c9f122fc2dc2369bb0e \
        --run-http-server 1337 \
        --impulse-id 3

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:a14935a55d84927d6eed21d8d9c2a40a633cd77d

Arguments:

--api-key ei_df94e6fd285e68de1d515d56166378f25679362df5362c9f122fc2dc2369bb0e --run-http-server 1337 --impulse-id 3

Ports to expose:

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:a14935a55d84927d6eed21d8d9c2a40a633cd77d \
        --api-key ei_df94e6fd285e68de1d515d56166378f25679362df5362c9f122fc2dc2369bb0e \
        --run-http-server 1337 \
        --impulse-id 3

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:aec2a7951d03a0f9eb769b8a7b0dd63c5e9c03bc

Arguments:

--api-key ei_df94e6fd285e68de1d515d56166378f25679362df5362c9f122fc2dc2369bb0e --run-http-server 1337 --impulse-id 3

Ports to expose:

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:aec2a7951d03a0f9eb769b8a7b0dd63c5e9c03bc \
        --api-key ei_df94e6fd285e68de1d515d56166378f25679362df5362c9f122fc2dc2369bb0e \
        --run-http-server 1337 \
        --impulse-id 3

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.

Clone this project to deploy this impulse.

Configure your deployment

Deploy to any Linux-based development board

Run your model as a Docker container

Run your model as a Docker container

Run your model as a Docker container

Run your model as a Docker container

Latest build

Run this model

Build output

Model testing output

Configure your deployment

Default Deployment

C++ library

Deploy to any Linux-based development board

Run your model as a Docker container

Run your model as a Docker container

Run your model as a Docker container

Run your model as a Docker container

Latest build

Run this model

Build output

Model testing output