Available today, PyTorch 1.3 comes with the ability to quantize a model for inference on to either server or mobile devices. Quantization is a way to perform computation at reduced precision. The ...

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, TensorFlow's major competitor in the deep-learning framework space, released its own official quantization-aware training tooling late last year.

Google Releases Post-Training Integer Quantization for TensorFlow Lite Jul 23, 2019 2 min read by Anthony Alford Senior Director, Development at Genesys Cloud Services Follow Like ...

There is a C++ API, but there isn’t half the support for other languages that TensorFlow offers. It’s quite conceivable that PyTorch will overtake TensorFlow within Python.

No longer the upstart nipping at TensorFlow’s heels, PyTorch is a major force in the deep learning world today, perhaps primarily for research, but also in production applications more and more.

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.

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.