Ludwig

What is Ludwig?

Ludwig is an open-source declarative deep learning framework, hosted by the Linux Foundation AI & Data. Instead of writing a training loop and data pipeline, you describe the model in a YAML file listing input and output features, then run ludwig train with a dataset. It covers LLM fine-tuning with supervised training, DPO, KTO, ORPO and GRPO alignment, and LoRA-family adapters with 4-bit QLoRA. It also handles multimodal and tabular models that mix text, numbers, images, audio and timeseries, along with forecasting and image segmentation. Training can scale from a laptop to multi-GPU setups or a Ray cluster by changing the backend type, and hyperparameter search runs on Ray Tune or Optuna. For deployment, ludwig serve starts a REST API, and vLLM, Ray Serve and KServe options exist, with export to SafeTensors or ONNX. The project requires Python 3.12 or newer and installs with pip install ludwig. It is free under the Apache-2.0 license.

Use cases

• Fine-tune an LLM from a config file
• Train tabular classification models
• Build multimodal models without custom code
• Tune hyperparameters at scale
• Forecast timeseries with PatchTST or N-BEATS
• Serve trained models over a REST API

Who it's for

• Machine learning engineers
• Data scientists
• AI researchers

Why use Ludwig

• The YAML config is the whole program, so there is no training loop or evaluation code to maintain.
• One framework covers LLM fine-tuning, tabular models and multimodal inputs, so teams reuse the same workflow.
• Moving from a laptop to multiple GPUs or a Ray cluster is a change to backend.type rather than new code.
• Every run is logged and the full config saved, which helps reproduce and compare experiments.

Ludwig features

• YAML-driven training with ludwig train
• SFT plus DPO, KTO, ORPO and GRPO alignment
• LoRA, DoRA, VeRA and other PEFT adapters
• 4-bit and 8-bit QLoRA and torchao quantization with QAT
• Vision-language model fine-tuning
• Text, image, audio, timeseries and tabular inputs
• Distributed training with Accelerate, DDP, FSDP and DeepSpeed
• Ray Tune and Optuna hyperparameter search
• REST serving plus vLLM, Ray Serve and KServe
• AutoML and LLM-generated configs

Ludwig integrations

• PyTorch
• Hugging Face
• Ray
• Optuna
• TensorBoard
• Weights & Biases
• Comet ML
• MLflow
• Aim
• vLLM
• KServe
• ONNX
• OpenML
• DeepSpeed

How to get started with Ludwig

1. Install it with pip install ludwig, or ludwig[llm] for LLM fine-tuning only.
2. Write a YAML config naming the model type, base model, input features and output features.
3. Run ludwig train --config model.yaml --dataset my_data.csv to train or fine-tune.
4. Use ludwig predict on new data and ludwig visualize to compare experiments.
5. Start a REST endpoint with ludwig serve --model_path <path>, or export the model.

Ludwig limitations

• Requires Python 3.12 or newer and a PyTorch environment
• Training large models needs suitable GPU hardware
• Some features need extra installs such as ludwig[full] or ludwig[llm]

Ludwig pricing

Ludwig is open source, so you can use and self-host it for free.

Prices can change and may vary by country or exclude tax. Check the official pricing page.

Ludwig alternatives

The main Ludwig alternatives listed on Debuly are IBM watsonx.ai, Haystack, Anyscale, Claude Agent SDK, Crusoe Cloud and FastChat. The table compares pricing and platforms; order is A–Z.

1. 

   Anyscale

   Managed platform for running Ray workloads, with pay-as-you-go compute and options to deploy in your own cloud.

   Developer platformsAutomation and agents
   PaidVisit Anyscale (official site)
2. 

   Claude Agent SDK

   Anthropic's Python SDK for building agents on the Claude Code runtime, with built-in tools, custom tools, hooks and MCP servers.

   Developer platformsAutomation and agentsCoding
   Visit Claude Agent SDK (official site)
3. 

   Crusoe Cloud

   AI cloud with on-demand GPU instances, managed Kubernetes and Slurm, and managed inference and fine-tuning for open models.

   Developer platformsCoding
   PaidVisit Crusoe Cloud (official site)
4. 

   FastChat

   Open platform for training, serving and evaluating LLM chatbots, with an OpenAI-compatible API and the code behind Chatbot Arena.

   Developer platformsChat assistantsCoding
   Open sourceVisit FastChat (official site)
5. 

   Haystack

   Open-source Python framework from deepset for building RAG pipelines, AI agents and multimodal apps from modular components.

   Developer platformsCodingAutomation and agents
   Open sourceVisit Haystack (official site)
6. 

   IBM watsonx.ai

   IBM's AI development studio for building generative AI apps and agents with foundation models, prompt tools, and machine learning, billed by usage.

   Developer platformsCodingAutomation and agents
   FreemiumVisit IBM watsonx.ai (official site)

Ludwig FAQ

Sources & verification

Facts on this page come from the official sources below, each with the date we read it. Written by Debuly; the vendor did not review this page.

Image credits

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