Skip to content


Use pip

For users familiar with Python, we recommend installing with [pip][pip] within an isolated virtual environment. If not, you can use our pre-built docker images.

pip install ludwig

For large or long-running workloads, Ludwig can be run remotely in the cloud or on a private compute cluster using Ray.

Install additional packages

Optional Ludwig functionality is separated out into subpackages. Install what you need:

  • ludwig[llm] for LLM dependencies.
  • ludwig[serve] for serving dependencies.
  • ludwig[viz] for visualization dependencies.
  • ludwig[hyperopt] for hyperparameter optimization dependencies.
  • ludwig[distributed] for distributed training on Ray using Dask.
  • ludwig[explain] for prediction explanations.
  • ludwig[tree] for LightGBM and tree-based models.
  • ludwig[test] for running ludwig's integration and unit tests.
  • ludwig[benchmarking] for Ludwig model benchmarking.
  • ludwig[full] for the full set of dependencies.

Install from git

pip install git+

Install from source

git clone
cd ludwig
pip install -e .

Use devcontainers

Ludwig supports development on VSCode devcontainers. See Ludwig's devcontainer files.

Use pre-build docker images

See Ludiwg's docker docs.