Finally, it has a JIT (Just-In-Time) component that takes your code and optimizes it for the XLA compiler, resulting in significant performance improvements over TensorFlow and PyTorch.

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.

PassiveLogic’s optimizations to Differentiable Swift equated to Swift consuming a mere 34 J/GOps, while TensorFlow consumed 33,713 J/GOps and PyTorch 168,245 J/GOps—as benchmarked on NVIDIA ...

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.

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.