Dump memory to files on disk for manual inspection.LightningLite - Stepping Stone to Lightning.Įnjoy OS-level features such as auto-saving, document versioning, window restoration, notification center, app nap, etc.Run as a normal user, not as the superuser (root)!.Evaluate mathematical expressions automatically (eg: in a flash game, search for 58 * 8).Undo & Redo many kinds of changes, including searches.Save slice documents so that you can send cheats to your friends.Watch for what instructions access a variable in a document.Set breakpoints, resume from them when they're hit, view backtraces, manipulate thread registers, and step into/out/over instructions.Modify instruction's bytes directly, or by assembling instructions (including nopping).Tutorial 3: Initialization and Optimization.Tutorial 4: Inception, ResNet and DenseNet.Tutorial 5: Transformers and Multi-Head Attention.Tutorial 6: Basics of Graph Neural Networks.Tutorial 7: Deep Energy-Based Generative Models.Tutorial 9: Normalizing Flows for Image Modeling.Tutorial 10: Autoregressive Image Modeling.Tutorial 12: Meta-Learning - Learning to Learn.Tutorial 13: Self-Supervised Contrastive Learning with SimCLR.GPU and batched data augmentation with Kornia and PyTorch-Lightning.PyTorch Lightning CIFAR10 ~94% Baseline Tutorial.Finetune Transformers Models with PyTorch Lightning.# default used by the Trainer trainer = Trainer ( enable_model_summary = True ) # disable summarization trainer = Trainer ( enable_model_summary = False ) # enable custom summarization from pytorch_lightning.callbacks import ModelSummary trainer = Trainer ( enable_model_summary = True, callbacks = ) Trainer class API ¶ Methods ¶ init ¶ Trainer.
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