Ludwig
Low-code framework for building custom LLMs, neural networks, and other AI models
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What is Ludwig?
Ludwig is an open-source declarative deep learning framework that enables users to train, fine-tune, and deploy deep learning models using a simple YAML configuration file without writing custom code. It supports a wide range of data types including text, images, tabular data, audio, and large language models (LLMs), making it a versatile tool for end-to-end machine learning pipelines. The framework uses a low-code approach where users define input and output features in a declarative config, and Ludwig handles preprocessing, model architecture, training loops, and evaluation automatically.
Key features include automatic batch size selection, distributed training support (DDP, DeepSpeed), parameter-efficient fine-tuning (PEFT), 4-bit quantization (QLoRA), multi-adapter PEFT, GRPO reward-based alignment, and support for larger-than-memory datasets. Ludwig integrates natively with HuggingFace Transformers, supports AutoML for automated model training, and offers rich model exporting to Torchscript and MLflow. It also provides prebuilt Docker containers, native Ray on Kubernetes support, and one-command HuggingFace upload capabilities.
Ludwig is designed for data scientists, machine learning researchers, software engineers, and analysts who want to build custom AI models like LLMs and neural networks without extensive coding. It is particularly useful for those who need to rapidly prototype models, experiment with different architectures, or scale training from local machines to multi-GPU/multi-node clusters. The framework is hosted by the Linux Foundation AI & Data and has over 11.6k stars on GitHub with an active community of 154 contributors.
Ludwig pricing
Pricing model: Freemium
Ludwig is completely free and open-source under the Apache-2.0 license. It can be installed via pip (pip install ludwig) and run locally without any cost. There are no paid tiers or subscription plans for the framework itself. For users seeking a hosted cloud infrastructure solution with reliable SLAs, Predibase offers a commercial hosted solution. The framework includes all features for free including AutoML, distributed training, model exporting, and serving capabilities.
Ludwig pros
- Open-source and free to use under Apache-2.0 license
- Low-code interface requiring only YAML config files
- Supports multiple modalities: text, image, tabular, audio, and LLMs
- Native integration with HuggingFace Transformers
- AutoML capability for automated model training
- Distributed training support with DDP and DeepSpeed
- Parameter-efficient fine-tuning (PEFT) including QLoRA
- Automatic batch size selection optimizes training
- Prebuilt Docker containers for easy deployment
- Native Ray on Kubernetes support for scaling
- Export models to Torchscript and MLflow
- One-command upload to HuggingFace
- 500+ built-in datasets available for benchmarking
- Rich metric visualizations and experiment tracking
- Multi-task and multi-modality learning out-of-the-box
- Full config validation prevents runtime failures
- REST API serving via ludwig serve command
- Hyperparameter optimization with Ray Tune integration
Ludwig cons
- Requires Python 3.12 or higher
- Does not support self-supervised learning yet
- Does not support reinforcement learning yet
- No support for generative image models
- No support for generative audio models
- Steep learning curve for beginners unfamiliar with ML
- Limited flexibility compared to writing raw PyTorch code
- GPU with at least 12 GiB VRAM needed for LLM fine-tuning
- Active development means breaking changes between versions
- Documentation scattered across multiple version pages
Frequently asked questions about Ludwig
What is Ludwig?
Ludwig is an open-source declarative deep learning framework that makes it easy to train, fine-tune, and deploy deep learning models using a simple YAML configuration file. It supports text, image, tabular, audio, and LLMs without requiring custom code. Users define input and output features in a declarative config, and Ludwig automatically handles data preprocessing, model architecture selection, training loops, hyperparameter optimization, and evaluation.
How do I install Ludwig?
Install Ludwig from PyPI using pip install ludwig. Ludwig requires Python 3.12 or higher. For all optional dependencies, use pip install ludwig[full]. The framework is built on Python 3.12, PyTorch 2.6, Pydantic 2, Transformers 5, and Ray 2.54.
Can I use Ludwig for my project?
Yes, Ludwig is open-source under the Apache-2.0 license and can be used for any project. The creators are veterans of research and open source. If your work using Ludwig gets published, they encourage citing Ludwig and submitting improvements back to the project. The GitHub repository has 11.6k stars and 154 contributors.
Can I use Ludwig models in production?
Yes, Ludwig models can be exported to MLflow and Torchscript for production use. The ludwig serve command provides basic POST/GET serving endpoints powered by FastAPI. For more sophisticated hosted cloud infrastructure with reliable SLAs, Predibase offers a commercial cloud solution.
What data types does Ludwig support?
Ludwig supports text, images, tabular data (numbers, categories, binary), audio, sequences, sets, bags, time series, and large language models. It enables multi-modal learning by allowing users to mix and match different data types in a single model configuration without writing code.
Does Ludwig support distributed training?
Yes, Ludwig supports distributed training through DDP (Distributed Data Parallel) and DeepSpeed. It can automatically scale training from a local machine to multi-GPU and multi-node clusters without code or config changes. Ludwig also has native support for running with Ray on Kubernetes for large-scale distributed training.
What does Ludwig not support yet?
Ludwig does not yet support self-supervised learning, reinforcement learning, or generative image and audio models. Generative text models are supported. The team is actively working on supporting self-supervised learning.
How does Ludwig handle large datasets?
Ludwig uses a Parquet-based caching pipeline. Image features are loaded dynamically from disk to save memory, while other feature types are loaded in memory for speed. For very large datasets, Ludwig supports training on Ray using Ray Datasets, enabling larger-than-memory dataset training.
Who develops Ludwig?
Ludwig was created in 2019 by Piero Molino, Yaroslav Dudin, and Sai Sumanth Miryala while at Uber AI. Today, it is open-source and hosted by the Linux Foundation AI & Data. The framework is actively developed and maintained by Ludwig Maintainers (mostly staff at Predibase) and community contributors.
How can I contribute to Ludwig?
You can join the Community Discord to contribute. The team sometimes organizes community fixit, documentation, and bug bash efforts. Beginners can start by taking on easy bugs. Larger projects are tracked in GitHub Projects, and smaller feature requests are tracked in GitHub Issues. All contributors are listed in Ludwig's release notes.