Examples ======== We provide several examples ported from `pytorch/examples `_ using `ignite` to display how it helps to write compact and full-featured training loops in a few lines of code: MNIST example ------------- Basic neural network training on MNIST dataset with/without `ignite.contrib` module: - `MNIST with ignite.contrib TQDM/Tensorboard/Visdom loggers `_ - `MNIST with native TQDM/Tensorboard/Visdom logging `_ Distributed CIFAR10 example --------------------------- Training a small variant of ResNet on CIFAR10 in various configurations: 1) single gpu, 2) single node multiple gpus, 3) multiple nodes and multilple gpus. - `CIFAR10 `_ Other examples -------------- - `DCGAN `_ - `Reinforcement Learning `_ - `Fast Neural Style `_ Notebooks --------- - `Text Classification using Convolutional Neural Networks `_ - `Variational Auto Encoders `_ - `Training Cycle-GAN on Horses to Zebras `_ - `Finetuning EfficientNet-B0 on CIFAR100 `_ - `Convolutional Neural Networks for Classifying Fashion-MNIST Dataset `_ - `Hyperparameters tuning with Ax `_ Reproducible trainings ---------------------- Inspired by `torchvision/references `_, we provide several reproducible baselines for vision tasks: - `ImageNet `_ - `Pascal VOC2012 `_ Features: - Distributed training with mixed precision by `nvidia/apex `_ - Experiments tracking with `MLflow `_ or `Polyaxon `_