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ludwig

v0.17.6 Feature

This release adds 1 notable feature for engineering teams evaluating rollout.

Published 1mo LLM Frameworks
✓ No known CVEs patched
Read the diff → Tool health → What is this tool? →

✓ No known CVEs patched in this version

Topics

computer-vision data-centric data-science deep machine-learning deeplearning
+11 more
fine-tuning learning llama llama2 llm llm-training machinelearning mistral natural-language natural-language-processing pytorch

Summary

AI summary

Added preprocessing progress callback and fixed MLflow filesystem tracking plus updated GPU Docker images to include cu126 PyTorch wheels.

Full changelog

What's new

Preprocessing progress callback — implement in your subclass to receive live progress updates (0.0 to 1.0) during feature preprocessing. Works with pandas, Dask, and Ray backends out of the box.

class MyCallback(Callback):
    def on_preprocess_progress(self, progress: float, **kwargs):
        print(f"Preprocessing: {progress:.0%}")

Fixes

  • MLflow 3.x filesystem tracking store compatibility in CI
  • GPU Docker images now correctly ship CUDA-enabled PyTorch wheels (cu126)
View diff on GitHub

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About ludwig

Low-code framework for building custom LLMs, neural networks, and other AI models

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Related context

Beta — feedback welcome: [email protected]