This repository contains the most basics of Pytorch, from low to advance. You can always get some quick reference of what can be done using pytorch from these files.
PyTorch is a Python package that provides two high-level features:
- Tensor computation (like NumPy) with strong GPU acceleration
- Deep neural networks built on a tape-based autograd system
At a granular level, PyTorch is a library that consists of the following components:
| Component | Description |
|---|---|
| torch | a Tensor library like NumPy, with strong GPU support |
| torch.autograd | a tape-based automatic differentiation library that supports all differentiable Tensor operations in torch |
| torch.jit | a compilation stack (TorchScript) to create serializable and optimizable models from PyTorch code |
| torch.nn | a neural networks library deeply integrated with autograd designed for maximum flexibility |
| torch.multiprocessing | Python multiprocessing, but with magical memory sharing of torch Tensors across processes. Useful for data loading and Hogwild training |
| torch.utils | DataLoader and other utility functions for convenience |