Updates, tips & best practices around PyTorch-Ignite.
Hey, I’m Sangho Lee, a master’s student from Seoul National University. I have participated in the PyTorch-Ignite project internship at Quansight Labs, working on test code improvements and features for distributed computations.
Read More
In this notebook, two PyTorch-Ignite’s metrics to evaluate Generative Adversarial Networks (or GAN in short) are introduced :
Heusel et al. 2002Barratt et al. 2018See here for more details about the implementation of the metrics in PyTorch-Ignite.
Most of the code here is from
DCGAN example in
pytorch/examples. In addition to the original tutorial, this notebook will use in-built GAN based metric in ignite.metrics to evaluate Frechet Inception Distnace and Inception Score and showcase other metric based features in ignite.
Along with the PyTorch-Ignite 0.4.5 release, we are excited to announce the new release of the web application for generating PyTorch-Ignite’s training pipelines. This blog post is an overview of the key features and updates of the Code Generator v0.2.0 project release.
Read More
Writing agnostic distributed code that supports different platforms, hardware configurations (GPUs, TPUs) and communication frameworks is tedious. In this blog, we will discuss how PyTorch-Ignite solves this problem with minimal code change.
Read More
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.
Read More