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Example programs using Uber's ludwig deep learning framework

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Uber ludwig examples

Example code illustrating using Uber's ludwig deep learning framework.

Objectives:

  • Define Docker images for the Ludwig software stack, including both Tensorflow (cpu-enabled) and Tensorflow (gpu-enabled)
  • Demonstrate running ludwig using command line execution in a Docker container
  • Demonstrate running ludwig using Python api in a Docker container
  • Generate modeling assessment visualizations, e.g., learning curves, confusion matrix, etc.
  • Provide samples for various types of models: image classification, text analytics, sentiment analysis, time series forecasting, etc.

Repo Contents

Directory Description
bin bash scripts for various function
containers Docker containers for ludwig software stack
kaggle_titanic Kaggle Titanic predictive competition data set
mnist Use of ludwig with mnist data set
text_classification Text classification model
time_series Time series forecasting temperature

Preparatory steps:

  • Create docker images with ludwig software stack. Run the following bash script.
bin/build_images tf_cpu
bin/build_images tf_gpu

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