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.

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.

PyTorch has a rapidly growing community, especially in the research sector, and is gaining on TensorFlow. Debugging PyTorch allows for straightforward debugging using standard Python tools.

Is PyTorch better than TensorFlow for general use cases? originally appeared on Quora: the place to gain and share knowledge, empowering people to learn from others and better understand the world ...

As cumbersome as TensorFlow might be to code, once it’s written is a lot easier to deploy than PyTorch. Tools like TensorFlow Serving and TensorFlow Lite make deployment to cloud, servers ...