README.md

⏭ Gradient Boosting with XGBoost and ZenML

XGBoost is an optimized distributed gradient boosting library designed to be highly efficient, flexible and portable. It implements machine learning algorithms under the Gradient Boosting framework. XGBoost provides a parallel tree boosting (also known as GBDT, GBM) that solve many data science problems in a fast and accurate way.

This example showcases how to train a XGBoost Booster model in a ZenML pipeline. The ZenML XGBoost integration includes a custom materializer that persists the trained xgboost.Booster model to and from the artifact store. It also includes materializers for the custom XGBoost.DMatrix data object.

The data used in this example is the quickstart XGBoost data and is available in the demo directory of the XGBoost repository.

🖥 Run it locally

⏩ SuperQuick xgboost run

If you're really in a hurry and just want to see this example pipeline run without wanting to fiddle around with all the individual installation and configuration steps, just run the following:

zenml example run xgboost

👣 Step-by-Step

📄 Prerequisites

In order to run this example, you need to install and initialize ZenML:

# install CLI
pip install zenml

# install ZenML integrations
zenml integration install xgboost

# pull example
zenml example pull xgboost
cd zenml_examples/xgboost

# initialize
zenml init

# Start the ZenServer to enable dashboard access
zenml up

▶️ Run the Code

Now we're ready. Execute:

python run.py

🧽 Clean up

In order to clean up, delete the remaining ZenML references.

rm -rf zenml_examples