xgboost

XGBoost Example

This example, trains a simple XGBoost model and then loads the model for inference in subsequent steps. This example showcases the usage of the @model decorator to save and load the model

Setup

1. If you are running OSS Metaflow install pip install metaflow; If you are runnning it on the Outerbounds Platform, run pip install outerbounds

2. Install other dependencies

   pip install -r requirements.txt

Executing the Flow

python flow.py --environment=fast-bakery run

Salient Features

• Model Storage/Loading: This flow uses the @model decorator to save and load the model. The @model decorator exposes a current.model.save function that can be used to save models created as a part of the @step. These models are all given unique identity and stored as a part of the @step's metadata information. The saved model is loaded in another @step using the @model(load="xgboost_model")