ludwig 0.10.3


Declarative machine learning: End-to-end machine learning pipelines using data-driven configurations

Declarative machine learning: End-to-end machine learning pipelines using data-driven configurations.

Stars: 10750, Watchers: 10750, Forks: 1162, Open Issues: 363

The ludwig-ai/ludwig repo was created 5 years ago and the last code push was 22 hours ago.
The project is extremely popular with a mindblowing 10750 github stars!

How to Install ludwig

You can install ludwig using pip

pip install ludwig

or add it to a project with poetry

poetry add ludwig

Package Details

Piero Molino
Apache 2.0
GitHub Repo:
No  ludwig  pypi packages just yet.


A list of common ludwig errors.

Code Examples

Here are some ludwig code examples and snippets.

GitHub Issues

The ludwig package has 363 open issues on GitHub

  • Update comment for predict to update Ludwig docs
  • [bug] Support preprocessing date features
  • Add effective_batch_size to auto-adjust gradient accumulation
  • Lamma2 training on dataset downloaded from Huggingface.
  • Implement batch size tuning in for None type LLM trainer (used for batch inference)
  • [WIP] Enable strict schema enforcement
  • Bug in the Tutorial of Tabular Data Classification
  • ValueError: Unexpected keyword arguments: top_k
  • Re-enable Horovod installation and unit tests for torch nightly.
  • Missing documentation for Ludwig Explainer
  • [llm_text_generation] RuntimeError: Expected all tensors to be on the same device,
  • Image Classification: Config
  • Initial implementation of DaftDataFrameEngine
  • refactor: Remove dict support for initializer fields. (2/2)
  • Not uploading confusion_matrix (and others) figure to Comet ML

See more issues on GitHub

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