ludwig-ai--ludwig/examples

Examples

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.
ray/job_submission	Submit Ludwig training to a remote Ray cluster via Ray Job Submission. Avoids Ray Client issues with ray.data. Works with KubeRay, Anyscale, or any Ray cluster.