Lightning-Bolts documentation¶ Start here¶ Installation Introduction Guide Model quality control Callbacks¶ Monitoring Callbacks Print Table Metrics Data Monitoring in LightningModule Model Verification Self-supervised Callbacks BYOLMAWeightUpdate SSLOnlineEvaluator Variational Callbacks Latent Dim Interpolator Vision Callbacks Confused Logit Tensorboard Image Generator Torch ORT Callback SparseML Callback 1. Choose your Sparse Recipe 2. Train with SparseMLCallback 3. Export to ONNX! DataModules¶ DataModules Sklearn Datamodule Sklearn Dataset Class Sklearn DataModule Class Vision DataModules Supervised learning Semi-supervised learning Datasets¶ Datasets Debugging DataLoaders¶ AsynchronousLoader Losses¶ Losses Your Loss Object Detection GIoU Loss IoU Loss Reinforcement Learning DQN Loss Double DQN Loss Per DQN Loss Models¶ How to use models Predicting on your data Finetuning on your data Train from scratch For research Classic ML Models Linear Regression Logistic Regression Vision models¶ Autoencoders Basic AE Basic VAE Convolutional Architectures GPT-2 Image GPT Pixel CNN UNet Semantic Segmentation GANs Basic GAN DCGAN Reinforcement Learning Module authors DQN Models Policy Gradient Models Actor-Critic Models Self-supervised Learning Use cases Contrastive Learning Models Learning Rate Schedulers¶ Learning Rate Schedulers Your Learning Rate Scheduler Linear Warmup Cosine Annealing Learning Rate Scheduler Data Processing¶ Self-supervised learning Transforms CPC transforms AMDIM transforms MOCO V2 transforms SimCLR transforms Self-supervised learning Identity class SSL-ready resnets SSL backbone finetuner Semi-supervised learning Balanced classes half labeled batches Learning Tasks¶ Self-supervised Learning Contrastive tasks FeatureMapContrastiveTask Context prediction tasks Community¶ Contributing PL Bolts Governance | Persons of interest Changelog Indices and tables¶ Index Module Index Search Page