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Ludwig
What it is
Ludwig is an open-source framework for building, fine-tuning, and deploying custom deep learning models across data types such as tabular, text, images, audio, time series, geospatial, and vector data. Users describe preprocessing, encoders, architecture, training, and hyperparameter optimization in a validated YAML file, avoiding the need to write training loops. It supports multimodal and multi-task models, as well as LLM tuning methods including SFT, DPO, KTO, ORPO, and GRPO, with parameter-efficient options such as LoRA and QLoRA. Distributed training runs through Ray, with DDP, FSDP, DeepSpeed, Kubernetes, and KubeRay support listed. Hyperparameter optimization integrates Ray Tune and Optuna, with SQLite or PostgreSQL persistence. Models can be served as a REST API or exported to SafeTensors, ONNX, or torch.export. Ludwig also provides experiment tracking integrations, generated reports and visualizations, feature importance, and explainability features. The open-source project uses the Apache 2.0 License. Its FAQ notes that Unsloth may be faster for users focused only on LLM fine-tuning who need maximum throughput.
For and against
For
- Open source under the Apache 2.0 License.
- YAML configuration avoids writing training loops.
- Supports multimodal and multi-task models.
- Distributed training uses Ray and related backends.
- Offers model export and REST API serving.
Against
- Unsloth may be faster for maximum-throughput LLM fine-tuning.
The verdict
Ludwig brings model configuration, training, tuning, and deployment into one framework, with options for both YAML users and deeper customization. Its own FAQ identifies maximum-throughput LLM-only tuning as a case where Unsloth may be faster.
Who it suits
Ludwig suits people building or tuning models across varied data types, from beginners using YAML or auto_train() to experts customizing PyTorch components. It may be less suitable for users who only fine-tune LLMs and prioritize maximum throughput.
Plans and prices
Open source
Apache 2.0 license · no paid plans listed on the official site
Read 9 Oct 2026 · ludwig.ai
On the record
- Configuration
- Users define preprocessing, encoders, architecture, training, and hyperparameter optimization in a validated YAML file.ludwig.ai · read 9 Oct 2026
- Customization
- Users can plug in custom encoders, decoders, combiners, loss functions, and metrics, and use HuggingFace models as backbones.ludwig.ai · read 9 Oct 2026
- Data and tasks
- The framework supports tabular, text, image, audio, time series, geospatial, vector, date/time, sequence, and anomaly data tasks.ludwig.ai · read 9 Oct 2026
- Distributed training
- A Ray backend enables distributed training using DDP, FSDP, or DeepSpeed, and the site also lists Kubernetes and KubeRay support.ludwig.ai · read 9 Oct 2026
- Experiment tracking
- The site says Ludwig integrates with W&B, MLflow, TensorBoard, Comet ML, and Aim, and generates training reports and visualizations.ludwig.ai · read 9 Oct 2026
- Explainability
- The site lists automatic baseline training, feature importance, model explainability, and visualizations.ludwig.ai · read 9 Oct 2026
- Extensibility
- Users can plug in custom encoders, decoders, combiners, loss functions, and metrics, and use HuggingFace models as backbones.ludwig.ai · read 8 Oct 2026
- Formats
- Supported data formats include CSV, TSV, JSON, Parquet, Feather, HDF5, Pandas DataFrames, and Dask DataFrames.ludwig.ai · read 9 Oct 2026
- Hyperparameter optimization
- Built-in HPO integrates Ray Tune and Optuna, with SQLite or PostgreSQL persistence.ludwig.ai · read 9 Oct 2026
- Integrations
- Listed integrations include HuggingFace Transformers, Ray, PyTorch, W&B, MLflow, TensorBoard, Optuna, Ray Tune, Docker, Kubernetes, vLLM, DeepSpeed, ONNX, SafeTensors, Dask, PyArrow, Comet ML, and Aim.ludwig.ai · read 9 Oct 2026
- License
- The site identifies Ludwig as open source under the Apache 2 License.ludwig.ai · read 8 Oct 2026
- License and hosting
- The project is described as open source under the Apache 2.0 License and hosted by Linux Foundation AI & Data.ludwig.ai · read 9 Oct 2026
- LLM fine-tuning
- The site lists SFT, DPO, KTO, ORPO, and GRPO, plus LoRA, QLoRA, DoRA, and VeRA methods.ludwig.ai · read 8 Oct 2026
- LLM tuning
- Ludwig supports SFT, DPO, KTO, ORPO, and GRPO, with parameter-efficient methods including LoRA and QLoRA.ludwig.ai · read 9 Oct 2026
- Modalities
- The framework supports multimodal and multi-task models combining features such as text, images, audio, tabular data, and time series.ludwig.ai · read 8 Oct 2026
- Notable limitation
- The FAQ says Unsloth may be faster when a user only fine-tunes LLMs and needs maximum throughput.ludwig.ai · read 9 Oct 2026
- Optimization
- Built-in hyperparameter optimization integrates Ray Tune and Optuna and supports SQLite or PostgreSQL persistence.ludwig.ai · read 8 Oct 2026
- Scaling
- Ludwig supports distributed training with Ray, including DDP, FSDP, DeepSpeed, and KubeRay deployment.ludwig.ai · read 8 Oct 2026