Ludwig

Ludwig: Open-source declarative deep learning framework for training and deploying models across multiple data types.

Ludwig is an open-source declarative deep learning framework that allows users to build, fine-tune, and deploy custom models across tabular data, text, images, audio, and time series without writing training loops or boilerplate infrastructure code. Users define their entire machine learning pipeline in a single validated YAML file, gaining the simplicity of AutoML with the flexibility of raw PyTorch. It supports fine-tuning large language models with LoRA, QLoRA, DoRA, and GRPO, and handles multiple data modalities like BERT, GPT-2, LLaMA, ResNet, EfficientNet, and Wave2Vec. The framework scales from a local laptop to distributed Ray clusters without changing a line of code, and provides built-in hyperparameter optimization, production-ready serving, and experiment tracking integrations.

Ludwig at a glance

Best for: Software engineer, Researcher
Runs on: Web, API, self-hosted, open source
Pricing: Free
Released: December 2018
Category: AI coding tool
Website: ludwig.ai

Key features

Declarative configuration with single YAML files
Multi-modal and multi-task learning support
LLM fine-tuning suite with LoRA and QLoRA
Scale from laptop to Ray cluster without code changes
Built-in hyperparameter optimization with Ray Tune and Optuna
Production-ready serving as a REST API
Rich experiment tracking integrations

Use it for

LLM instruction tuning on custom datasets
Vision-language model fine-tuning
Named entity recognition and token tagging
Time series forecasting and anomaly detection
Semantic segmentation and visual question answering

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