This post is a general introduction of PyTorch-Ignite. It intends to give a brief but illustrative overview of what PyTorch-Ignite can offer for Deep Learning enthusiasts, professionals and researchers. Following the same philosophy as PyTorch, PyTorch-Ignite aims to keep it simple, flexible and extensible but performant and scalable.
Throughout this tutorial, we will introduce the basic concepts of PyTorch-Ignite with the training and evaluation of a MNIST classifier as a beginner application case. We also assume that the reader is familiar with PyTorch.
Content
- PyTorch-Ignite: What and Why?
- Quick-start example
- Advanced features
- Power of Events & Handlers
- Out-of-the-box metrics
- Out-of-the-box handlers
- Distributed and XLA device support
- Projects using PyTorch-Ignite
- Project news
PyTorch-Ignite: What and Why ?
PyTorch-Ignite is a high-level library to help with training and evaluating neural networks in PyTorch flexibly and transparently.
PyTorch-Ignite is designed to be at the crossroads of high-level Plug & Play features and under-the-hood expansion possibilities. PyTorch-Ignite aims to improve the deep learning community’s technical skills by promoting best practices. Things are not hidden behind a divine tool that does everything, but remain within the reach of users.
PyTorch-Ignite takes a “Do-It-Yourself” approach as research is unpredictable and it is important to capture its requirements without blocking things.
#deep learning #labs #machine learning #neural networks #python #pytorch #tutorial
