From scientific-skills
Organizes PyTorch code into LightningModules and DataModules, configures Trainers for multi-GPU/TPU and distributed training (DDP, FSDP, DeepSpeed), with callbacks and logging for scalable DL.
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PyTorch Lightning is a deep learning framework that organizes PyTorch code to eliminate boilerplate while maintaining full flexibility. Automate training workflows, multi-device orchestration, and implement best practices for neural network training and scaling across multiple GPUs/TPUs.
Organizes PyTorch code into LightningModules and DataModules, configures Trainers for multi-GPU/TPU and distributed training (DDP, FSDP, DeepSpeed), with callbacks and logging for scalable DL.
Organizes PyTorch code into LightningModules and DataModules, configures Trainers for multi-GPU/TPU/distributed training (DDP, FSDP, DeepSpeed), implements callbacks, logging, and data pipelines for scalable neural networks.
Builds and trains PyTorch neural networks including models, training loops, data pipelines, torch.compile optimization, distributed training, and deployment.
Share bugs, ideas, or general feedback.
PyTorch Lightning is a deep learning framework that organizes PyTorch code to eliminate boilerplate while maintaining full flexibility. Automate training workflows, multi-device orchestration, and implement best practices for neural network training and scaling across multiple GPUs/TPUs.
This skill should be used when:
Organize PyTorch models into six logical sections:
__init__() and setup()training_step(batch, batch_idx)validation_step(batch, batch_idx)test_step(batch, batch_idx)predict_step(batch, batch_idx)configure_optimizers()Quick template reference: See scripts/template_lightning_module.py for a complete boilerplate.
Detailed documentation: Read references/lightning_module.md for comprehensive method documentation, hooks, properties, and best practices.
The Trainer automates the training loop, device management, gradient operations, and callbacks. Key features:
Quick setup reference: See scripts/quick_trainer_setup.py for common Trainer configurations.
Detailed documentation: Read references/trainer.md for all parameters, methods, and configuration options.
Encapsulate all data processing steps in a reusable class:
prepare_data() - Download and process data (single-process)setup() - Create datasets and apply transforms (per-GPU)train_dataloader() - Return training DataLoaderval_dataloader() - Return validation DataLoadertest_dataloader() - Return test DataLoaderQuick template reference: See scripts/template_datamodule.py for a complete boilerplate.
Detailed documentation: Read references/data_module.md for method details and usage patterns.
Add custom functionality at specific training hooks without modifying your LightningModule. Built-in callbacks include:
Detailed documentation: Read references/callbacks.md for built-in callbacks and custom callback creation.
Integrate with multiple logging platforms:
Log metrics using self.log("metric_name", value) in any LightningModule method.
Detailed documentation: Read references/logging.md for logger setup and configuration.
Choose the right strategy based on model size:
Configure with: Trainer(strategy="ddp", accelerator="gpu", devices=4)
Detailed documentation: Read references/distributed_training.md for strategy comparison and configuration.
self.device instead of .cuda()self.save_hyperparameters() in __init__()self.log() for automatic aggregation across devicesseed_everything() and Trainer(deterministic=True)Trainer(fast_dev_run=True) to test with 1 batchDetailed documentation: Read references/best_practices.md for common patterns and pitfalls.
Define model:
class MyModel(L.LightningModule):
def __init__(self):
super().__init__()
self.save_hyperparameters()
self.model = YourNetwork()
def training_step(self, batch, batch_idx):
x, y = batch
loss = F.cross_entropy(self.model(x), y)
self.log("train_loss", loss)
return loss
def configure_optimizers(self):
return torch.optim.Adam(self.parameters())
Prepare data:
# Option 1: Direct DataLoaders
train_loader = DataLoader(train_dataset, batch_size=32)
# Option 2: LightningDataModule (recommended for reusability)
dm = MyDataModule(batch_size=32)
Train:
trainer = L.Trainer(max_epochs=10, accelerator="gpu", devices=2)
trainer.fit(model, train_loader) # or trainer.fit(model, datamodule=dm)
Executable Python templates for common PyTorch Lightning patterns:
template_lightning_module.py - Complete LightningModule boilerplatetemplate_datamodule.py - Complete LightningDataModule boilerplatequick_trainer_setup.py - Common Trainer configuration examplesDetailed documentation for each PyTorch Lightning component:
lightning_module.md - Comprehensive LightningModule guide (methods, hooks, properties)trainer.md - Trainer configuration and parametersdata_module.md - LightningDataModule patterns and methodscallbacks.md - Built-in and custom callbackslogging.md - Logger integrations and usagedistributed_training.md - DDP, FSDP, DeepSpeed comparison and setupbest_practices.md - Common patterns, tips, and pitfalls