from torch.utils.tensorboard import SummaryWriter from clearml import Task # Connecting ClearML with the current process, # from here on everything is logged automatically task = Task.init ...

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.

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.