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:2c425f3b15bb3beb898f6e6005d7e8ccef0a8cda
Arguments:
--api-key ei_d7eb1954d655a94351e2548115e4b801ab13d761163ff1f92c20ff88483c9065 --run-http-server 1337
For example, in a one-liner locally:
docker run --rm -it \
-p 1337:1337 \
public.ecr.aws/g7a8t7v6/inference-container:2c425f3b15bb3beb898f6e6005d7e8ccef0a8cda \
--api-key ei_d7eb1954d655a94351e2548115e4b801ab13d761163ff1f92c20ff88483c9065 \
--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/g7a8t7v6/inference-container-jetson:bfc3b315fd289582242b19120237810ed1c6bb98
Arguments:
--api-key ei_d7eb1954d655a94351e2548115e4b801ab13d761163ff1f92c20ff88483c9065 --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/g7a8t7v6/inference-container-jetson:bfc3b315fd289582242b19120237810ed1c6bb98 \
--api-key ei_d7eb1954d655a94351e2548115e4b801ab13d761163ff1f92c20ff88483c9065 \
--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/g7a8t7v6/inference-container-jetson-orin:5099e21de99ef77b3aee1e7a664df8849b488c75
Arguments:
--api-key ei_d7eb1954d655a94351e2548115e4b801ab13d761163ff1f92c20ff88483c9065 --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/g7a8t7v6/inference-container-jetson-orin:5099e21de99ef77b3aee1e7a664df8849b488c75 \
--api-key ei_d7eb1954d655a94351e2548115e4b801ab13d761163ff1f92c20ff88483c9065 \
--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/g7a8t7v6/inference-container-jetson-orin-6-0:cefd80d921502fe00e1b0091ee6d1aad56b93c25
Arguments:
--api-key ei_d7eb1954d655a94351e2548115e4b801ab13d761163ff1f92c20ff88483c9065 --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/g7a8t7v6/inference-container-jetson-orin-6-0:cefd80d921502fe00e1b0091ee6d1aad56b93c25 \
--api-key ei_d7eb1954d655a94351e2548115e4b801ab13d761163ff1f92c20ff88483c9065 \
--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.
