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.

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.

the more Pythonic approach offered by PyTorch’s automatic differentiation (autograd) seems to have won the war against static graphs. Unlike TensorFlow, PyTorch hasn’t experienced any major ...

In case you’re curious how TensorFlow’s graph execution works, it allows for optimizing computations and provides a clear overview of operations and dependencies. On the other side ...

PyTorch is still growing, while TensorFlow’s growth has stalled. Graph from StackOverflow trends. StackOverflow traffic for TensorFlow might not be declining at a rapid speed, but it’s ...

Everything we know about Google's AI builder TensorFlow uses a dataflow graph to represent computations. It shares this space with another open-source machine-learning framework called PyTorch.

PyTorch uses a technique known as dynamic computation that makes it easy to train neural networks. TensorFlow is based on static computation that executes the code only after the graph of ...

This article will discuss the seven popular tools and frameworks used for developing AI applications: TensorFlow, PyTorch, Keras, Caffe, Microsoft Cognitive Toolkit, Theano and Apache MXNet.

TensorFlow Lite for mobile on-device AI has “grown beyond its TensorFlow roots to support models authored in PyTorch, JAX, and Keras.” Google says the new branding “captures this multi ...

Microsoft is stepping up its support for enterprise customers that are using Facebook's PyTorch deep-learning framework on the Microsoft Azure cloud. Microsoft has been contributing to the open ...