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backpropagation, mixed precision, multi-GPU, and distributed training, so the same model code scales from 1 to 10,000+ GPUs with zero changes.","\u003Ch2 id=\"project-overview\">Project Overview\u003C\u002Fh2>\u003Cp>PyTorch Lightning is a deep learning framework for pretraining and finetuning AI models. Training in plain PyTorch means writing repetitive, error-prone engineering code for backpropagation, mixed precision, multi-GPU, and distributed training. Lightning automates that infrastructure so you focus on model and data logic. The core code scales from CPU to multi-node GPU clusters without changes, and Lightning Fabric offers finer-grained expert control.\u003C\u002Fp>\n\u003Cblockquote>\n\u003Cp>You write the science, Lightning handles the engineering, and training scales linearly with hardware.\u003C\u002Fp>\n\u003C\u002Fblockquote>\n\u003Ctable>\n\u003Cthead>\n\u003Ctr>\n\u003Cth>Item\u003C\u002Fth>\n\u003Cth>Value\u003C\u002Fth>\n\u003C\u002Ftr>\n\u003C\u002Fthead>\n\u003Ctbody>\u003Ctr>\n\u003Ctd>Platform\u003C\u002Ftd>\n\u003Ctd>Linux, macOS, Windows\u003C\u002Ftd>\n\u003C\u002Ftr>\n\u003Ctr>\n\u003Ctd>Language\u003C\u002Ftd>\n\u003Ctd>Python\u003C\u002Ftd>\n\u003C\u002Ftr>\n\u003Ctr>\n\u003Ctd>License\u003C\u002Ftd>\n\u003Ctd>Apache-2.0\u003C\u002Ftd>\n\u003C\u002Ftr>\n\u003Ctr>\n\u003Ctd>Core packages\u003C\u002Ftd>\n\u003Ctd>PyTorch Lightning (high level) \u002F Lightning Fabric (expert control)\u003C\u002Ftd>\n\u003C\u002Ftr>\n\u003C\u002Ftbody>\u003C\u002Ftable>\n\u003Ch2 id=\"key-features\">Key Features\u003C\u002Fh2>\u003Cul>\n\u003Cli>\u003Cstrong>Automated training loop\u003C\u002Fstrong>: backpropagation, optimizer scheduling, validation, and logging are handled for you\u003C\u002Fli>\n\u003Cli>\u003Cstrong>Hardware-agnostic scaling\u003C\u002Fstrong>: switch the same code across CPUs, single GPUs, multi-GPU, and multi-node clusters\u003C\u002Fli>\n\u003Cli>\u003Cstrong>Mixed precision\u003C\u002Fstrong>: enable 16-bit training with one flag\u003C\u002Fli>\n\u003Cli>\u003Cstrong>LightningModule\u003C\u002Fstrong>: organize model, training step, and optimizers into a standard structure\u003C\u002Fli>\n\u003Cli>\u003Cstrong>Fabric expert mode\u003C\u002Fstrong>: insert fine-grained control where needed without rewriting your code\u003C\u002Fli>\n\u003Cli>\u003Cstrong>Callbacks and checkpoints\u003C\u002Fstrong>: resume from checkpoints, early stopping, and metric monitoring as pluggable callbacks\u003C\u002Fli>\n\u003C\u002Ful>\n\u003Ch2 id=\"target-audience\">Target Audience\u003C\u002Fh2>\u003Cul>\n\u003Cli>Researchers tired of rewriting training loops\u003C\u002Fli>\n\u003Cli>Engineers running multi-GPU distributed training\u003C\u002Fli>\n\u003Cli>Teams maintaining many deep learning projects\u003C\u002Fli>\n\u003Cli>Students reproducing paper experiments\u003C\u002Fli>\n\u003C\u002Ful>\n\u003Ch2 id=\"quick-start\">Quick Start\u003C\u002Fh2>\u003Col>\n\u003Cli>Install:\u003C\u002Fli>\n\u003C\u002Fol>\n\u003Cdiv class=\"code-block\">\u003Cpre>\u003Ccode class=\"hljs language-bash\">pip install lightning\u003C\u002Fcode>\u003C\u002Fpre>\u003C\u002Fdiv>\u003Col start=\"2\">\n\u003Cli>Write your model as a LightningModule implementing \u003Ccode>training_step\u003C\u002Fcode> and \u003Ccode>configure_optimizers\u003C\u002Fcode>\u003C\u002Fli>\n\u003Cli>Start training with the \u003Ccode>Trainer\u003C\u002Fcode> class, switching GPU count and precision via flags\u003C\u002Fli>\n\u003Cli>Switch to Lightning Fabric when you need finer control; code changes are minimal\u003C\u002Fli>\n\u003C\u002Fol>\n\u003Ch2 id=\"core-advantages\">Core Advantages\u003C\u002Fh2>\u003Cul>\n\u003Cli>\u003Cstrong>Scale without changes\u003C\u002Fstrong>: from 1 GPU to 10,000+ with the same core model code\u003C\u002Fli>\n\u003Cli>\u003Cstrong>Clean structure\u003C\u002Fstrong>: training code organized by module, reproducible and easy to hand over\u003C\u002Fli>\n\u003Cli>\u003Cstrong>Optional abstraction\u003C\u002Fstrong>: from fully automatic Trainer to semi-automatic Fabric, you pick the level\u003C\u002Fli>\n\u003C\u002Ful>\n\u003Ch2 id=\"system-requirements\">System Requirements\u003C\u002Fh2>\u003Ctable>\n\u003Cthead>\n\u003Ctr>\n\u003Cth>Item\u003C\u002Fth>\n\u003Cth>Requirement\u003C\u002Fth>\n\u003C\u002Ftr>\n\u003C\u002Fthead>\n\u003Ctbody>\u003Ctr>\n\u003Ctd>OS\u003C\u002Ftd>\n\u003Ctd>Linux, macOS, Windows\u003C\u002Ftd>\n\u003C\u002Ftr>\n\u003Ctr>\n\u003Ctd>Runtime\u003C\u002Ftd>\n\u003Ctd>Python (depends on PyTorch)\u003C\u002Ftd>\n\u003C\u002Ftr>\n\u003Ctr>\n\u003Ctd>Hardware\u003C\u002Ftd>\n\u003Ctd>Trains on CPU; GPU and multi-node clusters supported\u003C\u002Ftd>\n\u003C\u002Ftr>\n\u003C\u002Ftbody>\u003C\u002Ftable>\n\u003Ch2 id=\"notes\">Notes\u003C\u002Fh2>\u003Cul>\n\u003Cli>\u003Cstrong>Prerequisite\u003C\u002Fstrong>: PyTorch must be installed first; GPU training needs a matching CUDA build\u003C\u002Fli>\n\u003Cli>\u003Cstrong>Two interfaces\u003C\u002Fstrong>: PyTorch Lightning and Fabric differ in usage; pick one per project\u003C\u002Fli>\n\u003C\u002Ful>\n",1793197892866]