This document lists the open source repositories containing software known to work well with Deepwave Digital Products, including both third-party software and Deepwave Digital software made available under open source licensing terms. Open source software developed by Deepwave Digital is available via GitHub at https://github.com/deepwavedigital.
We provide a number of examples on how to use the AIR-T. While these examples are provided with a fresh AirStack installation, they are updated periodically between releases. Additionally, if you upgrade minor versions using .deb packages without flashing a complete image, these examples will not be updated. You can always find the most up-to-date set of example code in the below repositories.
- GitHub Repository URL: https://github.com/deepwavedigital/airstack-examples
- AIR-T Conda Environments: https://github.com/deepwavedigital/airstack-examples/tree/master/conda/environments
cuSignal (RAPIDS GitHub)¶
RAPIDS is an NVIDIA open source project to GPU accelerate data science. cuSignal is a GPU accelerated version of
scipy.signal. The RAPIDS cuSignal project is based upon CuPy, Numba, and the larger RAPIDS ecosystem for GPU accelerated computing. In some cases, cuSignal is a direct port of SciPy Signal to accelerate specific operations via CuPy primitives. It also contains Numba CUDA kernels for additional speedups for selected functions. cuSignal achieves its best gains on large signals and compute intensive functions but stresses online processing with zero-copy memory (pinned, mapped) between CPU and GPU.
This out of tree (OOT) module for GNU Radio contains code to provide an interface to call NVIDIA's TensorRT deep learning binaries from a GNU Radio flowgraph. TensorRT allows for deep learning networks to be optimized for inference operations on an NVIDIA graphics processing units (GPU).
For an example of how to use GR-Wavelearner, see our presentation here
GitHub Repository URL: https://github.com/deepwavedigital/gr-wavelearner
This OOT module contains experimental code on integration of GPU processing into GNU Radio by using the PyCUDA library to run CUDA code from within GNU Radio.
Tutorial: CUDA Blocks with GNU Radio and the AIR-T
GitHub Repository URL: https://github.com/deepwavedigital/gr-cuda