Declarative YAML configuration
Define preprocessing, encoders, architecture, training, and hyperparameter optimization in one validated YAML file instead of writing training loops by hand.
Ludwig
ReclamarLudwig is an open-source declarative deep learning framework for training, fine-tuning, and deploying models from one YAML config.
Overview
Ludwig is an open-source declarative deep learning framework for building, fine-tuning, and deploying custom models with a single YAML config file. It is positioned for users who want to avoid hand-written training loops while still keeping access to advanced control when needed.
The site says Ludwig supports tabular, text, image, audio, time series, and LLM workflows, and that it can run locally or scale to Ray clusters without changing the model definition. The product documentation also highlights built-in preprocessing, experiment tracking, serving, and export workflows.
Features
Multi-modal, multi-task modeling
Build models that combine tabular, text, image, audio, time series, and other feature types, and train multiple outputs in one run.
LLM fine-tuning stack
Fine-tune LLMs with supervised instruction tuning and alignment methods including DPO, KTO, ORPO, and GRPO, plus LoRA, QLoRA, DoRA, and VeRA.
Distributed training backend
Switch a local job to distributed execution by adding a backend configuration for Ray, with support shown for DDP, FSDP, DeepSpeed, and KubeRay.
Hyperparameter optimization
Run built-in HPO with Ray Tune or Optuna, with samplers such as Auto, TPE, GP, and CMA-ES and persistence via SQLite or PostgreSQL.
Serving and export workflow
Serve models with one command and export to SafeTensors, ONNX, or `torch.export`, with Docker images and Hugging Face Hub upload options.
Use Cases
Train custom models from configuration
Use Ludwig when you want to build a supervised model from a YAML file and keep the training pipeline explicit without coding preprocessing and loop logic from scratch.
Multimodal and multi-task modeling
Use it for projects that combine different input types, such as text with tabular features or images with metadata, while training multiple outputs in one model.
LLM fine-tuning and serving
Use the LLM tooling to instruction-tune open models with 4-bit QLoRA or related alignment methods, then serve the result with Ludwig's serving command.
Distributed training on Ray
Use the Ray backend when a local experiment needs to scale out to distributed execution on a cluster without changing the underlying model definition.
Model export and deployment
Use the built-in export and serving workflow when you need a REST API, Docker image, or model artifact such as ONNX or SafeTensors for deployment.
Pros and Cons
Pros
- Uses a single YAML file to define the model pipeline.
- Supports multiple data modalities and multi-task setups.
- Includes LLM fine-tuning methods such as LoRA, QLoRA, DPO, and GRPO.
- Can scale from local training to Ray-based distributed jobs without rewriting the model definition.
- Offers model serving and export paths alongside training.
Cons
- The pricing page at `ludwig.ai/pricing` is not available in the collected sources, so commercial terms are unclear from this evidence set.
- The source coverage is partial for some practical details such as integrations, team deployment patterns, and pricing.
- Some advanced capabilities are documented at a high level on the homepage and docs, but the extracted sources do not fully cover every workflow in depth.
FAQ
What is Ludwig used for?
Ludwig is a YAML-based framework for defining preprocessing, model architecture, training, hyperparameter optimization, and serving in one config file. The site describes it as an open-source declarative deep learning framework.
How do you work with Ludwig?
The getting-started and homepage pages show a command-line workflow: install Ludwig, prepare a dataset, and run `ludwig train --config ...`. The product also documents prediction, evaluation, hyperparameter optimization, serving, distributed training, and LLM fine-tuning.
What kinds of models and data does Ludwig support?
The site says Ludwig supports tabular data, text, images, audio, time series, and LLM workflows. It also lists multimodal and multi-task modeling.
Can Ludwig be used for deployment?
The homepage and docs indicate Ludwig can serve models as a REST API and export to formats such as SafeTensors, ONNX, and `torch.export`. It also mentions Docker images and upload to Hugging Face Hub.
Quick Facts
- Category
- Developer Tool
- Primary users
- ML and AI practitioners
- Platform
- Open-source framework
- Source domain
- ludwig.ai
- License
- Apache 2 License
- Deployment
- Local, Ray cluster, Kubernetes