One lesser-known feature of PyTorch is its support for distributed computing. It allows you to train your models on multiple machines or GPUs, improving performance and scalability. This is particularly useful when working with massive datasets or computationally intensive tasks.

Another lesser-known feature is PyTorch's ability to seamlessly integrate CUDA, which enables GPU acceleration. This can significantly speed up computations, especially when dealing with large datasets or complex models.

Lastly, PyTorch makes it easy to deploy your models in production through frameworks like TorchServe. It provides tools for managing model versions, handling requests, and monitoring performance. This is crucial when building real-world applications based on PyTorch.

One lesser-known feature of PyTorch is its support for automatic differentiation. This allows you to compute gradients of your functions automatically, which is essential for training neural networks. PyTorch's dynamic computation graph also sets it apart from other frameworks.

Another interesting feature is PyTorch's TorchScript, which allows you to compile your PyTorch models into a portable representation that can be executed in other environments, such as mobile devices or embedded systems.

In addition, PyTorch provides a wide range of pre-trained models through its torchvision package. These models can be easily used for tasks like image classification, object detection, and style transfer. They serve as great starting points for researchers and developers alike.