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Training these networks requires large numbers of training waveforms to be available to the network. We currently use LIGO's LIGO Algorithm Library (LAL) to produce these waveforms offline then read them in during the network training phase. For large parameter spaces this become infeasible since the training dataset increases in size to 100s of Terabytes. This project aims to produce training waveforms on the fly, interleaving a training epoch (on the GPU) with waveform production for the next epoch (on the CPU). You will use Python, LAL and TensorFlow to set up a pipeline that uses CPU cores to produce waveforms while using the produced waveforms to train neural networks. Students working on this project will have access to the HAL cluster at UIUC and Summit at Oak Ridge National lab.
Skills required:
- good understanding of Python
- working knowledge of Python multiprocessing module
- willing to learn new Python modules (h5py, numpy)
- experience using Linux and the command line interface
- some basic experience using Tensorflow
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