Note: Implemented for EON projects only. Anomaly detection blocks not tested.
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.
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..."
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..."
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:
--api-keys ei_0b0e...,ei_acde...
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.
./build.sh
to compile./app
to check the static inferencing results