PyTorch Lightning is a deep learning framework for pretraining and fine-tuning models with PyTorch. It separates model and research code from much of the repetitive engineering needed to train models at scale.
Lightning can automate training infrastructure such as backpropagation, mixed precision, device management, and distributed execution while leaving the underlying PyTorch model logic under the developer’s control.
This is free and open source software.
Key Features
- Organise PyTorch model training into a structured framework.
- Automates repetitive training infrastructure while retaining access to PyTorch.
- Scale training from CPUs to multi-node GPU environments.
- Automatic management of backpropagation and optimisation loops.
- Supports mixed-precision training.
- Built-in support for multi-GPU and distributed training.
- LightningModule encapsulates model architecture and training logic.
- Trainer handles training, validation, testing, and prediction workflows.
- Supports checkpointing, logging, profiling, and experiment management.
- Lightning Fabric offers lower-level control over distributed training infrastructure.
- Suitable for pretraining and fine-tuning a wide variety of AI models.
Website: https://github.com/Lightning-AI/pytorch-lightning
Support:
Developer: Lightning AI
License: Apache License 2.0
PyTorch Lightning is written in Python. Learn Python with our recommended free books and free tutorials.
Related Software
| Deep Learning with Python | |
|---|---|
| TensorFlow | A very popular Deep Learning framework |
| PyTorch | Tensors and Dynamic neural networks in Python |
| Keras | High-level neural networks API |
| fastai | Simplifies training fast and accurate neural networks |
| tinygrad | Compact, lightweight deep learning framework |
| PyTorch Lightning | Framework for scaling PyTorch training workflows |
| JAX | High-performance numerical computing and ML library |
| PyTensor | Library for fast numerical computation |
| MindSpore | Framework for training and deploying neural networks |
| Elephas | Distributed deep learning with Keras and Spark |
| Chainer | Powerful, flexible, and intuitive framework for neural networks |
| Equinox | Neural network library for building flexible models with JAX |
| Caffe | Convolutional Architecture for Fast Feature Embedding |
| TFlearn | Deep learning library featuring a higher-level API for TensorFlow |
| MXNet | Flexible and efficient library |
| CNTK | Distributed deep learning |
| Neupy | Python library for neural networks and deep learning |
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