Many developers who use Python for machine learning are now switching to PyTorch. Find out why and what the future could hold for TensorFlow.

The evolution of PyTorch Early on, academics and researchers were drawn to PyTorch because it was easier to use than TensorFlow for model development with graphics processing units (GPUs).

PyTorch recreates the graph on the fly at each iteration step. In contrast, TensorFlow by default creates a single data flow graph, optimizes the graph code for performance, and then trains the model.