Thank you for your interest in contributing to PyTorch! Once you implement and test your feature or bug-fix, please submit a Pull Request to https://github.com/pytorch/pytorch. This document covers some of the more technical aspects of contributing to PyTorch. For more non-technical guidance about how to contribute to PyTorch, see the Contributing Guide. If you want to have no-op incremental rebuilds (which are fast), see the section below titled "Make no-op build fast." This mode will symlink the Python files from the current local source tree into the Python install. Hence, if you modify a Python file, you do not need to reinstall PyTorch again and again. This is especially useful if you are only changing Python files. You do not need to repeatedly install after modifying Python files (.py). However, you would need to reinstall if you modify Python interface (.pyi, .pyi.in) or non-Python files (.cpp, .cc,

We are excited to announce the availability of PyTorch 1.8. This release is composed of more than 3,000 commits since 1.7. It includes major updates and new features for compilation, code optimization, frontend APIs for scientific computing, and AMD ROCm support through binaries that are available via pytorch.org. It also provides improved features for large-scale training for pipeline and model parallelism, and gradient compression. Along with 1.8, we are also releasing major updates to PyTorch libraries including TorchCSPRNG, TorchVision, TorchText and TorchAudio.

WeightedRandomSampler has been added as a custom sampler for the DataLoader. It samples elements from [0,..,len(weights)-1] with the given probabilities and is useful to sample from unbalanced datasets where some classes have many more samples than others. WeightedRandomSampler has been added as a custom sampler for the DataLoader. It samples elements from [0,..,len(weights)-1] with the given probabilities and is useful to sample from unbalanced datasets where some classes have many more samples than others.