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.

TensorFlow is your ally for scalability and production. PyTorch is your friend for research flexibility and ease of use. The choice depends on your project needs, expertise, and long-term goals.

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.

If this is what matters most for you, then your choice is probably TensorFlow. A network written in PyTorch is a Dynamic Computational Graph (DCG). It allows you to do any crazy thing you want to do.

First is PyTorch, with its tremendous following and mindshare. If you look at the metrics alone it might be easy to miss, but PyTorch is quite possibly the most used and talked about deep learning ...

TensorFlow uses a dataflow graph to represent computations. It shares this space with another open-source machine-learning framework called PyTorch.

Developers can submit ML training jobs created in TensorFlow, Keras, PyTorch, Scikit-learn, and XGBoost. Google now offers in-built algorithms based on linear classifier, wide and deep and XGBoost ...

TensorFlow Lite for mobile on-device AI has “grown beyond its TensorFlow roots to support models authored in PyTorch, JAX, and Keras.” ...