# lightning-ai/lightning

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31,189 stars · 3,739 forks · Python · Apache-2.0

## Links

- GitHub: https://github.com/lightning-AI/lightning
- Homepage: https://lightning.ai/pytorch-lightning/?utm_source=ptl_readme&utm_medium=referral&utm_campaign=ptl_readme
- awesome-repositories: https://awesome-repositories.com/repository/lightning-ai-lightning.md

## Description

Lightning is a PyTorch training framework and distributed AI training orchestrator designed to decouple core research logic from the engineering boilerplate required for model training. It functions as a deep learning workflow manager that automates the process of pretraining and finetuning models across diverse compute environments.

The project distinguishes itself by providing a hardware-agnostic training wrapper, allowing the same model code to execute on CPUs, GPUs, or TPUs without modification. It further manages the scaling of workloads from single devices to multi-node clusters and serves as a cloud GPU infrastructure manager with integrated autoscaling and monitoring.

The framework covers a broad range of training capabilities, including distributed data parallelism, automatic mixed precision, and state-based checkpoint automation. It also provides tools for production model export and supports custom training loop primitives for specialized model architectures.

## Tags

### Artificial Intelligence & ML

- [Distributed Deep Learning](https://awesome-repositories.com/f/artificial-intelligence-ml/distributed-deep-learning.md) — Provides a framework for scaling deep learning model training across multiple compute nodes or GPUs.
- [Distributed Training Orchestrators](https://awesome-repositories.com/f/artificial-intelligence-ml/distributed-training-orchestrators.md) — Provides a system for scaling model training across multi-node GPU and TPU clusters without changing the core model architecture.
- [Training Execution](https://awesome-repositories.com/f/artificial-intelligence-ml/cross-hardware-model-inference/training-execution.md) — Runs models on CPU, single-GPU, or multi-node GPU clusters without requiring changes to the core model logic in the project. ([source](https://github.com/lightning-ai/lightning#readme))
- [Deep Learning Research Workflows](https://awesome-repositories.com/f/artificial-intelligence-ml/deep-learning-research-workflows.md) — Separates core mathematical research logic from the repetitive engineering boilerplate required to run experiments on hardware.
- [Data-Parallel Training](https://awesome-repositories.com/f/artificial-intelligence-ml/distributed-training-frameworks/data-parallel-training.md) — Implements distributed data parallelism to split datasets across multiple compute nodes and synchronize gradients.
- [Distributed Training Scaling Utilities](https://awesome-repositories.com/f/artificial-intelligence-ml/distributed-training-scaling-utilities.md) — Distributes training workloads across diverse hardware from single devices to multi-node clusters. ([source](https://github.com/lightning-ai/lightning#readme))
- [Hardware Abstraction Layers](https://awesome-repositories.com/f/artificial-intelligence-ml/hardware-abstraction-layers.md) — Provides a hardware-agnostic wrapper that abstracts tensor operations for CPUs, GPUs, and TPUs.
- [Large-Scale Training Frameworks](https://awesome-repositories.com/f/artificial-intelligence-ml/large-scale-training-frameworks.md) — Orchestrates the training of massive AI models across thousands of GPUs or TPUs without manual code rewrites.
- [Distributed and Scaling Strategies](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/model-training-and-tuning/distributed-and-scaling-strategies.md) — Applies advanced distribution techniques and mixed precision to optimize large-scale model performance. ([source](https://github.com/lightning-ai/lightning#readme))
- [Training Frameworks](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/model-training-and-tuning/training-frameworks.md) — Provides a high-level framework that abstracts engineering boilerplate and hardware-specific code for PyTorch model training.
- [Finetuning Workflows](https://awesome-repositories.com/f/artificial-intelligence-ml/model-pretraining-frameworks/finetuning-workflows.md) — Organizes the deep learning workflow to pretrain and finetune models of any size across diverse hardware configurations. ([source](https://github.com/lightning-ai/lightning#readme))
- [Training Boilerplate Automation](https://awesome-repositories.com/f/artificial-intelligence-ml/training-boilerplate-automation.md) — Automates repetitive engineering tasks like backpropagation and mixed precision to separate research logic from infrastructure code. ([source](https://github.com/lightning-ai/lightning#readme))
- [Custom Training Loops](https://awesome-repositories.com/f/artificial-intelligence-ml/custom-training-loops.md) — Provides low-level primitives for creating specialized training loops for complex model architectures. ([source](https://github.com/lightning-ai/lightning#readme))
- [Mixed Precision Training](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/machine-learning-training/distributed-and-accelerated-compute/training-acceleration-tools/mixed-precision-training.md) — Provides automatic mixed precision training to reduce memory usage and increase compute speed.
- [Model Fine-Tuning](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/model-training-and-tuning/fine-tuning-and-customization/model-fine-tuning.md) — Manages the training process and hardware optimization for adapting pre-trained AI models to specific tasks.
- [Model Checkpointing](https://awesome-repositories.com/f/artificial-intelligence-ml/model-checkpointing.md) — Implements automatic state-based checkpointing and restoration to allow training resumption and early stopping. ([source](https://github.com/lightning-ai/lightning#readme))
- [Training Checkpointing](https://awesome-repositories.com/f/artificial-intelligence-ml/training-checkpointing.md) — Automates the serialization of model weights and optimizer states to allow training resumption.

### Software Engineering & Architecture

- [Logic Decoupling](https://awesome-repositories.com/f/software-engineering-architecture/software-architecture/architectural-patterns/modular-decoupled-design/structural-design-paradigms/decoupled-logic-encapsulation/logic-decoupling.md) — Organizes model code by separating core research logic from hardware requirements and training boilerplate. ([source](https://github.com/lightning-ai/lightning#readme))
- [Research Logic Separation](https://awesome-repositories.com/f/software-engineering-architecture/software-architecture/architectural-patterns/modular-decoupled-design/structural-design-paradigms/decoupled-logic-encapsulation/logic-decoupling/research-logic-separation.md) — Separates the core mathematical model definition from the engineering boilerplate used for training and hardware orchestration.

### Web Development

- [Deep Learning Frameworks](https://awesome-repositories.com/f/web-development/state-management-models/state-space-models/deep-learning-frameworks.md) — Automates model checkpoints, early stopping, and mixed precision training across diverse compute environments.

### DevOps & Infrastructure

- [Managed Infrastructure Deployment](https://awesome-repositories.com/f/devops-infrastructure/cloud-infrastructure-deployment/managed-infrastructure-deployment.md) — Runs training jobs on cloud GPUs with integrated autoscaling and monitoring. ([source](https://github.com/lightning-ai/lightning#readme))
- [GPU Training Clusters](https://awesome-repositories.com/f/devops-infrastructure/cloud-infrastructure-management/gpu-training-clusters.md) — A managed environment for deploying and scaling AI training jobs on cloud hardware with integrated monitoring and autoscaling.
- [Cloud Native GPU Orchestration](https://awesome-repositories.com/f/devops-infrastructure/cloud-native-orchestration/cloud-native-gpu-orchestration.md) — Manages GPU resources through automated scaling and monitoring for cloud-based training jobs.
- [Cluster Job Schedulers](https://awesome-repositories.com/f/devops-infrastructure/cluster-job-schedulers.md) — Coordinates the distribution of training tasks across multi-node GPU clusters with integrated monitoring and autoscaling.

### Part of an Awesome List

- [Deep Learning](https://awesome-repositories.com/f/awesome-lists/ai/deep-learning.md) — Organized, high-level interface for PyTorch workflows.
- [Deep Learning Frameworks](https://awesome-repositories.com/f/awesome-lists/ai/deep-learning-frameworks.md) — Accelerates the training and deployment of deep learning products.