Ludwig is a low-code, declarative deep learning framework designed for building custom AI models, including LLMs and multimodal systems. It targets researchers and engineers seeking to streamline model development, training, and productionization without extensive boilerplate code. Ludwig offers significant efficiency gains through automated optimizations and distributed training capabilities.

How It Works

Ludwig utilizes a declarative YAML configuration system to define model architecture, data preprocessing, and training parameters. This approach abstracts away complex coding, allowing users to specify model components and their interconnections. It supports multi-modal and multi-task learning by composing different feature types and encoders/decoders, enabling flexible experimentation with state-of-the-art architectures.

Quick Start & Requirements

• Install: pip install ludwig or pip install ludwig[full]
• Prerequisites: Python 3.8+. GPU with at least 12 GiB VRAM recommended for LLM fine-tuning (e.g., Nvidia T4). HuggingFace API token required for Llama-2 access.
• Resources: LLM fine-tuning examples suggest a GPU with 12GB VRAM.
• Docs: Official Getting Started Guide, Examples

Highlighted Details

• Declarative YAML configuration for defining models and training pipelines.
• Supports LLM fine-tuning with PEFT, QLoRA, and distributed training (DDP, DeepSpeed).
• Native integration with HuggingFace Transformers and easy export to Torchscript/Triton.
• Automated hyperparameter optimization, explainability, and metric visualization.

Maintenance & Community

Ludwig is hosted by the Linux Foundation AI & Data. Active community engagement via Discord and X.

Licensing & Compatibility

Licensed under Apache 2.0, permitting commercial use and integration with closed-source projects.

Limitations & Caveats

While designed for ease of use, advanced customization may still require understanding deep learning concepts. The README focuses heavily on LLM fine-tuning and tabular data, with less detail on other modalities like audio.