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