This directory contains example programs demonstrating Ludwig's Python APIs.
| Directory | Examples Provided |
|---|---|
| hyperopt | Demonstrates Ludwig's to hyper-parameter optimization capability. |
| kfold_cv | Provides two examples for performing a k-fold cross validation analysis. One example uses the ludwig experiment cli. The other example uses the ludwig.experiment.kfold_cross_validate() api function. |
| mnist | Creates a model config data structure from a yaml file and trains a model. Programmatically modify the model config data structure to evaluate several different neural network architectures. Jupyter notebook demonstrates using a hold-out test data set to visualize model performance for alternative model architectures. |
| titanic | Trains a simple model with model config contained in a yaml file. Trains multiple models from yaml files and generate visualizations to compare training results. Jupyter notebook demonstrating how to programmatically create visualizations. |
| serve | Demonstrates running Ludwig http model server. A sample Python program illustrates how to invoke the REST API to get predictions from input features. |
| class_imbalance | Demonstrates using our class balancing feature to over-sample an imbalanced dataset. |