lightning-AI/lightning
Lightning
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.
Features
- Distributed Deep Learning - Provides a framework for scaling deep learning model training across multiple compute nodes or GPUs.
- Distributed Training Orchestrators - Provides a system for scaling model training across multi-node GPU and TPU clusters without changing the core model architecture.
- Training Execution - Runs models on CPU, single-GPU, or multi-node GPU clusters without requiring changes to the core model logic in the project.
- Deep Learning Research Workflows - Separates core mathematical research logic from the repetitive engineering boilerplate required to run experiments on hardware.
- Data-Parallel Training - Implements distributed data parallelism to split datasets across multiple compute nodes and synchronize gradients.
- Distributed Training Scaling Utilities - Distributes training workloads across diverse hardware from single devices to multi-node clusters.
- Hardware Abstraction Layers - Provides a hardware-agnostic wrapper that abstracts tensor operations for CPUs, GPUs, and TPUs.
- Large-Scale Training Frameworks - Orchestrates the training of massive AI models across thousands of GPUs or TPUs without manual code rewrites.
- Distributed and Scaling Strategies - Applies advanced distribution techniques and mixed precision to optimize large-scale model performance.
- Training Frameworks - Provides a high-level framework that abstracts engineering boilerplate and hardware-specific code for PyTorch model training.
- Finetuning Workflows - Organizes the deep learning workflow to pretrain and finetune models of any size across diverse hardware configurations.
- Training Boilerplate Automation - Automates repetitive engineering tasks like backpropagation and mixed precision to separate research logic from infrastructure code.
- Logic Decoupling - Organizes model code by separating core research logic from hardware requirements and training boilerplate.
- Research Logic Separation - Separates the core mathematical model definition from the engineering boilerplate used for training and hardware orchestration.
- Deep Learning Frameworks - Automates model checkpoints, early stopping, and mixed precision training across diverse compute environments.
- Custom Training Loops - Provides low-level primitives for creating specialized training loops for complex model architectures.
- Mixed Precision Training - Provides automatic mixed precision training to reduce memory usage and increase compute speed.
- Model Fine-Tuning - Manages the training process and hardware optimization for adapting pre-trained AI models to specific tasks.
- Model Checkpointing - Implements automatic state-based checkpointing and restoration to allow training resumption and early stopping.
- Training Checkpointing - Automates the serialization of model weights and optimizer states to allow training resumption.
- Managed Infrastructure Deployment - Runs training jobs on cloud GPUs with integrated autoscaling and monitoring.
- GPU Training Clusters - A managed environment for deploying and scaling AI training jobs on cloud hardware with integrated monitoring and autoscaling.
- Cloud Native GPU Orchestration - Manages GPU resources through automated scaling and monitoring for cloud-based training jobs.
- Cluster Job Schedulers - Coordinates the distribution of training tasks across multi-node GPU clusters with integrated monitoring and autoscaling.
- Deep Learning - Organized, high-level interface for PyTorch workflows.
- Deep Learning Frameworks - Framework for training and deploying deep learning models efficiently.
Star history
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