pytorch-lightning
Streamlined PyTorch training framework with automatic distributed training, callbacks, and minimal boilerplate.
- Organizes PyTorch code into LightningModule with training_step, validation_step, and test_step methods; Trainer class handles device management, mixed precision, checkpointing, and logging automatically
- Supports distributed training strategies including DDP, FSDP, and DeepSpeed with single-line configuration; scales from laptop to multi-node clusters without code changes
- Built-in callbacks system for ModelCheckpoint, EarlyStopping, LearningRateMonitor, and custom extensions; integrates with TensorBoard and popular logging platforms
- Handles gradient accumulation, learning rate scheduling, and precision modes (FP32, FP16, BF16, FP8); works across GPU, TPU, CPU, and Apple MPS accelerators
PyTorch Lightning - High-Level Training Framework
Quick start
PyTorch Lightning organizes PyTorch code to eliminate boilerplate while maintaining flexibility.
Installation:
pip install lightning
Convert PyTorch to Lightning (3 steps):
import lightning as L
import torch
from torch import nn
from torch.utils.data import DataLoader, Dataset
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