Home › Training Tools › PyTorch Lightning

PyTorch Lightning

A deep learning framework for PyTorch that simplifies model training by automating backpropagation, mixed precision, multi-GPU & TPU distributed training, and deployment, all without requiring extensive code modifications.

Key Information

  • Category: Training Tools
  • Source: Github
  • Tags: Python
  • Last updated: January 09, 2026

Structured Metrics

No structured metrics captured yet.

Links

Visit official website

Canonical source: https://github.com/Lightning-AI/pytorch-lightning

Related Tools

AI Toolkit (ostris)

All-in-one training suite for diffusion models (e.g., FLUX.1) with a UI and Modal/Docker workflows; supports LoRA/LoKr and conv training.
AutoTrain

Hugging Face AutoTrain is an automated machine learning (AutoML) tool that allows users to train, evaluate, and deploy state-of-the-art ML models without writing code. It supports a range of tasks including text classification, image classification, token classification, summarization, question answering, translation, tabular data tasks, and LLM finetuning, with seamless integration into the Hugging Face ecosystem.
ColossalAI

An open-source platform that reduces the cost of training and inference for large AI models, enhancing efficiency and scalability.
DeepScaleR

DeepScaleR is an open-source project that democratizes reinforcement learning (RL) for large language models (LLMs). The repository provides training scripts, model checkpoints, detailed hyperparameter configurations, datasets, and evaluation logs to reproduce and scale RL techniques on LLMs, aimed at reproducibility and research in advanced AI training.
Determined AI

Open‑source ML platform for distributed training, hyperparameter search, experiment tracking, and resource management.
FluxGym

A dead simple web UI for training FLUX LoRA models with low VRAM support, built on Gradio UI (forked from AI-Toolkit) and powered by Kohya Scripts. It simplifies the fine-tuning of LoRA models on systems with limited VRAM (12GB/16GB/20GB).