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Ludwig AI

Ludwig is an open-source Python framework for building, fine-tuning and serving deep-learning models from declarative YAML configurations. It supports tabular, text, image and audio inputs, supervised learning and language-model fine-tuning, with options for hyperparameter optimization, distributed training, model export and REST serving.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Ludwig AI — OpenViST project profile in AI Developer Tools & SDKs.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use Ludwig when a model workflow can be expressed through its configuration system and you want built-in support for data preparation, training and related infrastructure. It is also suited to experimentation across model architectures and data modalities, with documented options for scaling and serving.

Who it is for

  • Developers and research teams who want configuration-driven model training.
  • Practitioners fine-tuning pretrained language models on custom instruction data.
  • Teams building models that combine tabular, text, image or audio features.
  • ML teams that need documented paths to distributed training, experiment tracking, export or serving.

Documented uses

  • Define input and output features and training settings in YAML.
  • Train supervised models, such as a binary classifier from review data.
  • Fine-tune pretrained language models with custom datasets.
  • Combine multiple data modalities or learning tasks in a model configuration.
  • Run hyperparameter optimization and distributed training.
Before you choose

Requirements & limits

The official documentation lists Python 3.12 or later and installation from PyPI. The documented LLM fine-tuning example requires a Hugging Face API token, approved access to the chosen base model and a GPU with at least 12 GiB of VRAM.

  • The core workflow is configured in YAML, while detailed model, preprocessing and training options remain configurable.
  • The documentation describes distributed training with DDP and DeepSpeed, and running Ray on Kubernetes.
  • Serving and export paths include a REST API, TorchScript and Triton; Hugging Face upload is also documented.
  • The documentation lists integrations for tracking experiments with TensorBoard, Weights & Biases and MLflow.
  • Requirements for fine-tuning a large language model are more specific than those for the general framework.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Fine-tune an instruction-following language model

Configure and start the documented Alpaca instruction-tuning example.

Before you start

  • Python 3.12 or later and Ludwig installed from PyPI.
  • A Hugging Face API token and approved access to the selected base model.
  • A GPU with at least 12 GiB of VRAM for the documented example.
  • A YAML configuration file and the Alpaca dataset identified by Ludwig.

Steps

  1. Create a YAML configuration defining the LLM base model, quantization, LoRA adapter, prompt template, text features and fine-tuning settings.

  2. Run ludwig train --config model.yaml --dataset "ludwig://alpaca".

Expected outcome

Starts training an instruction-tuned model using the configured base model and Alpaca data.

Guide limitations

  • The documented hardware requirement is specific to this LLM example; resource needs can vary with the model and configuration.
  • The source documents training here, but not the subsequent prediction or serving steps for this particular run.
Documentation 1 ↗
Source-based practical guide

Train a review classifier from a CSV

Define and start the documented Rotten Tomatoes binary classification workflow.

Before you start

  • Python 3.12 or later and Ludwig installed from PyPI.
  • The Rotten Tomatoes CSV dataset, available from the official documentation's data link.

Steps

  1. Create a YAML configuration describing the CSV columns as input features and recommended as a binary output feature.

  2. Run ludwig train --config model.yaml --dataset rotten_tomatoes.csv.

Expected outcome

Starts training a model to predict whether a movie review is recommended.

Guide limitations

  • The documented example provides training instructions; prediction, evaluation and serving are covered in the separate getting-started guide.
Documentation 1 ↗Documentation 2 ↗
COMPARE OPTIONS

Compare options for Model inference and serving

These projects document a common task and share a project role. This is a source-based comparison, not an execution benchmark or a guarantee of interchangeability.

ProjectDocumented purposePlatformAccess and requirements
Ludwig AIDefine input and output features and training settings in YAML.Linux, macOS, WindowsThe official documentation lists Python 3.12 or later and installation from PyPI. The documented LLM fine-tuning example requires a Hugging Face API token, approved access to the chosen base model and a GPU with at least 12 GiB of VRAM.
HyperposeCustomize pose-estimation architectures and datasets.Docker, Linux, macOS, Windows
Multi Task NlpDefine multiple tasks and their data and model settings in a YAML file.Linux, macOS, WindowsThe project is an open-source library under the Apache-2.0 license, distributed through its public GitHub repository.
Evidence and transparency

Sources & review

ludwig.ai ↗Guide documentation ↗Guide documentation ↗
Source record details
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Sources checked
12 October 2026
Original website title
Ludwig — Declarative Deep Learning Framework | Train Any Model with YAML · Official homepage title · retained in its source language
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