ludwig-ai/ludwig

Health v2 · maintenance only

Adoption not part of the score

11745 stars · 1217 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

Ludwig is a declarative, low-code deep learning framework for training, fine-tuning, and deploying AI models — from LLMs to tabular, image, audio, and time-series models — using YAML configuration files instead of boilerplate Python. Built on PyTorch with Ray-based distributed training, it supports PEFT/LoRA fine-tuning, multi-task and multimodal learning, and production export to TorchScript and Triton.

Use cases

• fine-tune llama or mistral on my own data with lora
• train a classifier from a csv without writing training code
• build custom llms with a yaml config
• fine-tune a vision-language model like llava
• run distributed deep learning training on ray or kubernetes
• do hyperparameter optimization for a neural network
• train a model on tabular, text, and image features together
• export a trained model to torchscript or triton for serving

When to choose

• you want to train or fine-tune models without writing training loops
• you need LLM fine-tuning with PEFT adapters like LoRA or QLoRA
• you want declarative, reproducible ML pipelines validated by config schemas
• you need multi-task or multimodal models mixing tabular, text, image, and audio features
• you want to scale training across GPUs with Ray, DDP, or DeepSpeed

When to avoid

• you need full low-level control over every training detail and prefer raw PyTorch
• you want a lightweight inference-only library rather than a training framework
• your project requires Python versions below 3.12
• you need a no-code GUI tool rather than config-driven workflows

Facets

framework · maturity active

machine-learning deep-learning llm-training llm-inference nlp computer-vision data-science cli machine-learning deep-learning large-language-models computer-vision data-science python cli declarative-ml yaml-config fine-tuning lora peft qlora pytorch ray distributed-training multimodal low-code huggingface natural-language-processing linux macos docker kubernetes gpu

Member repositories

For agents

markdown · JSON · MCP: product_card(name="ludwig-ai/ludwig")