Deployment block to merge multiple impulses

Note: Implemented for EON projects only. Anomaly detection blocks not tested.

Usage

By default, the quantized version is used when downloading the C++ libraries. To use float32, add the option --float32 as an argument.

Similarly by default the EON compiled model is used, if you want to use full tflite then add the option --full-tflite and be sure to include a recent version of tensorflow lite compiled for your device architecture in the root of your project in a folder named tensorflow-lite

If you need a mix of quantized and float32, you can look at the dzip.download_model function call in generate.py and change the code accordingly.

By default, the block will download cached version of builds. You can force new builds using the --force-build option.

Locally

Install the requirments pip install -r requirements.txt

Retrieve API Keys of your projects and run the generate.py command as follows: python generate.py --out-directory ./output --api-keys "ei_0b0e...", "ei_acde..."

Docker

Build the container: docker build -t multi-impulse .

Then run: docker run --rm -it -v $PWD:/home multi-impulse --api-keys "ei_0b0e...", "ei_acde..."

Custom deployment block

Initialize the custom block - select Deployment block and Library when prompted: edge-impulse-blocks init

Push the block: edge-impulse-blocks push

Then go your Organization and Edit the deployment block with:

• CLI arguments: --api-keys ei_0b0e...,ei_acde...
• Provileged mode: Enabled

Compiling the standalone example

The Makefile is for Desktop environment (macOS/Linux). For embedded targets, you'll need to change the cross-compiler or integrate the multi-impulse inference library within your application.

• Unzip the deploy.zip archive (from output/ directory if running on your laptop)
• Open the source/main.cpp file and fill the raw features arrays corresponding to the project IDs
• Run./build.sh to compile
• Run ./app to check the static inferencing results