The PyTorch-Ignite Blog

Updates, tips & best practices around PyTorch-Ignite.

• String Reversal Example: Exploring Small Transformers with Trainite

  Published on August 3, 2026 – 12 mins

  Most of the time when you are working on a new project or just starting out or prototyping you have to write same boilerplate code again and again whether it’s training loop, configs, logging etc.

  Trainite can help you with that. Trainite is an open-source machine learning toolbox built on top of PyTorch-Ignite that takes care of generating this boilerplate code for you. Trainite generates clean, modular Python starter code that you fully own and can customize freely.

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• Reproducing "Knee-Deep in C-RASP" Experiments using Trainite

  Published on August 1, 2026 – 9 mins

  When you get an idea for a new model or experiment, the worst part is losing momentum while spending hours setting up training loops, configuration files, and logging before you can even run your first epoch.

  This is where Trainite comes in. Trainite is an open-source machine learning toolbox powered by PyTorch-Ignite that takes you from zero to a running PyTorch experiment in minutes. Instead of locking you into rigid framework abstractions, Trainite generates clean, modular Python starter code that you fully own and can customize freely.

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• Bridging Data Science Tools with PyTorch-Ignite's Code-Generator and Nebari

  Published on September 20, 2023 – 9 mins

  Bridging Data Science Tools with PyTorch-Ignite’s Code-Generator and Nebari To the readers, I am Aryan Gupta( @guptaaryan16), an EE Junior at IIT Roorkee, and this summer, I had a chance to work on PyTorch-Ignite’s Code-Generator project, a tailor-made web application to help machine learning researchers and enthusiasts and also keeping in mind the growing Kaggle community. Crossposted from https://labs.quansight.org/blog The project itself Let’s see the project itself.

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• Sangho's Internship at Quansight with PyTorch-Ignite project

  Published on February 3, 2023 – 3 mins

  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.

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• GAN evaluation using FID and IS

  Published on August 11, 2021 – 14 mins

  GAN Evaluation : the Frechet Inception Distance and Inception Score metrics

  

  In this notebook, two PyTorch-Ignite’s metrics to evaluate Generative Adversarial Networks (or GAN in short) are introduced :

  • Frechet Inception Distance, details can be found in Heusel et al. 2002
  • Inception Score, details can be found in Barratt et al. 2018

  See 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.

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• Introducing PyTorch-Ignite's Code Generator v0.2.0

  Published on July 16, 2021 – 3 mins

  

  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.

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• Distributed Training Made Easy with PyTorch-Ignite

  Published on June 28, 2021 – 5 mins

  

  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.

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• Introduction to PyTorch-Ignite

  Published on September 10, 2020 – 24 mins

  

  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.

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