Models Agents Infrastructure Pricing Docs Security

Models Agents Infrastructure Pricing Docs Security

Lightning AI

Platform

Lightning AIDevelop, train and deploy on Lightning AI Python SDKProgrammatic access to the Lightning AI platform CLIAccess the Lightning AI platform from the command line

Open Source Libraries

PyTorch LightningFinetune and pretrain AI models. Focus on science, not engineering.Lightning FabricScale foundation models to 1000s of GPUs with expert-level control TorchMetrics90+ PyTorch metrics, optimized for distributed training ThunderCompiler for PyTorch: Make PyTorch models faster!LitLoggerExperiment tracking built into Lightning AI LitDataStream and process datasets at scale LitServeEasily serve AI models — Lightning fast LitAILLM router and minimal agent framework in one LitGPT20+ high-performance LLMs with recipes to pretrain, finetune and deploy at scale.

Overview

Getting started Why Lightning AI

ML use case Startups Frontier labs Research project Enterprise Admin tools Model development

Idea phase Early stage Growth

Students Classrooms University class Research project Research projects Research labs University lab Academic institutions University Academic conferences

Students The side project Side projects The work project Work projects

Coding interviews Hackathons Workshops

Account creation GitHub vs Lightning AI Billing Get help Security Personal organization

Build

AI Studio (workspace)

Start and stop Studio Collaboration GitHub and GitLab Connect local IDE SSH access Add GPUS Deploy a Studio Add data Base Studios Modify environment Environment persistence Background execution Debug interactively Deploy on public ports Publishing Duplicate Studios Auto sleep Custom Docker images Interruptible machines Environment variables Import models Manage disk size Move clouds On-start actions On-stop actions Managed secrets GPU marketplace Plugins CLI

Run arbitrary code in a sandboxed environment Build a stateful, sandboxed code interpreter Build a coding agent with LangChain

Expose web apps Streamlit apps Gradio apps Host demos

Inference

Overview Model APIs

Lightning Inference

Autoscaling Clouds and regions Ideal workflow Secure communication Multi-node Inference Websockets SDK

Custom inference engine vLLM and others Model registry Compile models

Self-serve vLLM inference server

Submit jobs Job outputs Prototype server APIs Job pipeline (DAG)Common issues SDK CLI Examples

Deploy a container

AWS ECR DockerHub GitHub containers GCP artifact registry Lit container registry

Manage execution order Pipeline environment Available node types Scheduling Versioning Shared filesystem Examples

Turn-key models

Train

Experiment Management

Installation API Reference

Standalone Usage Lightning Integration Logging Artifacts Logging Media Workflows Examples

Quickstart Files, Media, Models Lightning Tutorial Complete Workflow LitServe Tutorial

SDK commands CLI commands

Pretrain models

Multi-GPU training PyTorch Lightning Lightning Fabric TorchMetrics Accelerate library

Finetune models

Finetune LLMs Single GPU training Training guide PyTorch Lightning Lightning Fabric TorchMetrics

Examples (full code)

Build a custom Trainer

GPU Cloud

Overview GPU marketplace

Managed Kubernetes Managed SLURM Networking Storage

Bring your own cloud

Data

Overview Upload data Move data Drive

Create datasets Import datasets Share datasets Version datasets

Data connections

S3 buckets GCS buckets R2 buckets

Cloud folders Artifacts Switch clouds Use LitData

Security

Data processing addendum (DPA)HIPAA SOC 2 Type II Self-serve due diligence Subprocessors

Deployment options Data collection Data retention Data residency Deleting my data

Security features

Audit logs Security Multi-factor authentication Custom encryption keys Disk encryption Configure SSO Secrets RBAC

Trusted platform

Internal access Secure software development Penetration testing Data backup

Team Management

Manage members Team management Manage cloud accounts Manage teamspaces Discoverability Manage costs Share private Studios Role-based access control

Manage members Drive Manage costs Secrets

Developers

Studio Batch jobs Multi-machine jobs Clusters Deployments Virtual Machines

Python Sandbox SDK JS Sandbox SDK

Studios Batch jobs Multi-machine jobs Sandbox Teamspace Drive

Docs/Lightning AI/Train/Experiment Management/Guide

Guide

Standalone UsageUse LitLogger in any Python process without Lightning. For new code, the recommended standalone workflow is the dict-sty…Lightning IntegrationUse lightning.pytorch.loggers.LitLogger for Trainer and Fabric integration. LightningLogger remains available only as a …Logging ArtifactsLitLogger can store and retrieve files and model artifacts associated with your experiments. The modern path is the file…Logging MediaLitLogger supports uploading images and text files that are displayed alongside your metrics in the experiment view. For…WorkflowsThis guide maps the LitLogger feature set to concrete workflows so users can quickly choose the right API surface. Recom…ExamplesA standalone training script using the module-level API through logmetrics(), logmetadata(), and logfile(). """Example o…

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