Thanks to the CUDA architecture  developed by NVIDIA, developers can exploit GPUs’ parallel computing power to perform general computation without extra efforts. Our objective is to evaluate the performance achieved by TensorFlow, PyTorch, and MXNet on Titan RTX.
Andrew Brock, first author of the high-profile research paper Large Scale GAN Training for High Fidelity Natural Image Synthesis (aka “BigGAN”), has posted a GitHub repository of an unofficial PyTorch BigGAN implementation that requires only 4-8 GPUs to train the model.
A new GitHub project, PyTorch Geometric (PyG), is attracting attention across the machine learning community. PyG is a geometric deep learning extension library for PyTorch dedicated to processing irregularly structured input data such as graphs, point clouds, and manifolds.