Introduction
Ludwig is a low-code, declarative deep learning framework that uses YAML configs to define datasets, models, and training pipelines. It supports multi-task and multi-modal learning and integrates with distributed backends for scale.
Key features
- Declarative configuration: build models and pipelines with simple YAML files.
- Distributed & backend integrations: support for DDP, DeepSpeed, Ray/Kubernetes and optimized production exports.
- Automation tools: includes hyperparameter search, preprocessing, visualization, and model export utilities.
Use cases
- Rapid prototyping and benchmarking of ML models without writing boilerplate code.
- Multi-modal research (text, image, audio, tabular) and automated experiment tracking.
- Teams needing a smooth path from local development to cluster production deployment.
Technical details
- Primarily Python-based with modular design; extensive docs and examples (getting started, examples, tutorials).
- Supports model export (TorchScript/Triton), parameter-efficient fine-tuning (PEFT/QLoRA), and various optimization strategies for production.