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

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.

While TensorFlow is the workhorse of Google’s ML efforts, it’s not the only open-source ML training library. In recent years the open-source PyTorch framework, originally created by Facebook ...

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

TensorFlow is an open-source machine learning and deep learning framework created by Google Brain in 2015. It provides a flexible and efficient ecosystem for building and training AI models ...

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.

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 ...