lightning-AI/lightning

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 - Accelerates the training and deployment of deep learning products.

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