Facebook's AI team introduced version 1.1 of deep learning framework PyTorch today with improved JIT compiler speed and native TensorBoard support.

PyTorch's new integration with TensorBoard may help close that gap. The team also pointed out improvements to PyTorch's JIT compiler and distributed training.

As Spisak told me, one of the most important new features in PyTorch 1.1 is support for TensorBoard, Google’s visualization tool for TensorFlow that helps developers evaluate and inspect models.

"TensorBoard is a data science companion dashboard that helps PyTorch and TensorFlow developers visualize their dataset and model training," said Jeffrey Lew of the VS Code Python team in announcing ...

But why should you choose to use PyTorch instead of other frameworks like MXNet, Chainer, or TensorFlow? Let’s look into five reasons that add up to a strong case for PyTorch.