One lesser-known feature of PyTorch is its support for automatic differentiation using the autograd package. This feature allows you to compute gradients of tensors and optimize models without explicitly writing out the backpropagation equations. It can save a lot of time and effort in the training process.

Another lesser-known feature is PyTorch's integration with NVIDIA's CUDA framework. This enables you to utilize the power of GPUs for accelerated computation, which is especially important when working with large datasets and complex models. CUDA also provides parallel computing capabilities, making PyTorch a great choice for high-performance computing.

Lastly, PyTorch offers a hybrid front-end called TorchScript, which allows you to write PyTorch code and compile it into a standalone, optimized executable. This is particularly useful when deploying models in production environments or integrating them with other languages and frameworks. It provides a seamless way to combine the flexibility of PyTorch with the speed and efficiency of compiled code.