FLAML provides a fast Python library for automated machine learning and tuning.
Open source
24 alternativesPyTorch Lightning
PyTorch Lightning supports pretraining and fine tuning AI models across GPU setups.
About PyTorch Lightning
PyTorch Lightning is a deep learning framework for pretraining and fine-tuning AI models with PyTorch. It organizes PyTorch code to automate infrastructure work such as backpropagation, mixed precision, and multi-GPU and distributed training, while leaving model logic under user control and scaling from CPU to multi-node GPUs without changing core code.
It offers two packages, PyTorch Lightning for training and deployment at scale and Lightning Fabric for expert-level control. It can run on user hardware or cloud infrastructure, or through Lightning Cloud, and references LitServe for building inference servers in Python.
Key features
- Automate backpropagation, mixed precision and distributed training setup
- Scale training from CPU to multi-node GPUs without changing core code
- Define models with LightningModule for training and inference logic
- Choose abstraction level via Lightning and Fabric packages
- Run on own hardware, cloud, or Lightning Cloud service
- Build custom inference servers in Python with LitServe
Best for: PyTorch users who want to pretrain and fine-tune models without maintaining distributed training code.
Good for
- Open-source codebase for PyTorch training infrastructure
- Automates repetitive engineering code while keeping model logic control
- Runs on own hardware, cloud infrastructure, or Lightning Cloud
PyTorch Lightning at a glance
- Type
- Open-source project
- Pricing
- Not confirmed. Check the site
- Open source
- Yes · Source code
- Language
- English
- Last checked
- Oct 5, 2026 · Online when checked
- Listed
- Oct 5, 2026
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PyTorch Lightning FAQ
How do I install Lightning?
Install with pip install lightning, or with conda using conda install lightning -c conda-forge. Optional dependencies and source installs are also documented.
What are the two core packages?
PyTorch Lightning for training and deploying PyTorch at scale, and Lightning Fabric for expert-level control with less abstraction.
Can I run it without managing my own infrastructure?
Yes. You can run on your own hardware or cloud, or use Lightning Cloud to access GPUs, autoscaling, and monitoring.
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