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/g7a8t7v6/inference-container:19e22ac41e42380fd6704c02cb7dcfdfd97ed1f9
Arguments:
--api-key ei_775e8c4c1c7f41e263334d0bd7012411fe5e2c8146f1501d32f089f8d3a9bc59 --run-http-server 1337 --impulse-id 2
For example, in a one-liner locally:
docker run --rm -it \
-p 1337:1337 \
public.ecr.aws/g7a8t7v6/inference-container:19e22ac41e42380fd6704c02cb7dcfdfd97ed1f9 \
--api-key ei_775e8c4c1c7f41e263334d0bd7012411fe5e2c8146f1501d32f089f8d3a9bc59 \
--run-http-server 1337 \
--impulse-id 2
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/g7a8t7v6/inference-container-jetson:04459d186210873f5ef6800ed7103443ee6faec8
Arguments:
--api-key ei_775e8c4c1c7f41e263334d0bd7012411fe5e2c8146f1501d32f089f8d3a9bc59 --run-http-server 1337 --impulse-id 2
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:04459d186210873f5ef6800ed7103443ee6faec8 \
--api-key ei_775e8c4c1c7f41e263334d0bd7012411fe5e2c8146f1501d32f089f8d3a9bc59 \
--run-http-server 1337 \
--impulse-id 2
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:0883e5e377474c01e4cfef0cb896b70d6885298f
Arguments:
--api-key ei_775e8c4c1c7f41e263334d0bd7012411fe5e2c8146f1501d32f089f8d3a9bc59 --run-http-server 1337 --impulse-id 2
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:0883e5e377474c01e4cfef0cb896b70d6885298f \
--api-key ei_775e8c4c1c7f41e263334d0bd7012411fe5e2c8146f1501d32f089f8d3a9bc59 \
--run-http-server 1337 \
--impulse-id 2
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-6-0:106b7375a5fcd0bf58079f13d4f9955136567d2a
Arguments:
--api-key ei_775e8c4c1c7f41e263334d0bd7012411fe5e2c8146f1501d32f089f8d3a9bc59 --run-http-server 1337 --impulse-id 2
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-6-0:106b7375a5fcd0bf58079f13d4f9955136567d2a \
--api-key ei_775e8c4c1c7f41e263334d0bd7012411fe5e2c8146f1501d32f089f8d3a9bc59 \
--run-http-server 1337 \
--impulse-id 2
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.
