MXNet vs PyTorch: Comparison of the Deep Learning Frameworks

Deep learning rapidly grew in popularity as a subset of machine learning that learns through Artificial Neural Networks. Using the vast data, it educates its deep neural networks to attain better accuracy and results without a human programmer. Deep learning frameworks such as Caffe, Deeplearning4j, Keras, MXNet, PyTorch, and Tensorflow rely upon cuDNN, NCCL, DALI or other types of libraries for a high-performance multi-GPU accelerated training. NGC is a GPU-Optimized software hub that simplifies high-performance computing, deep learning, and machine learning structure and workflows. This is becoming a tremendous help to developers, researchers, and data scientists by eliminating the need to manage or build DL frameworks from the source.