Machine Learning

Do machine learning with XGBoost, LightGBM, or scikit-learn on Saturn Cloud

If you’d like to learn more about working in Dask before you incorporate it into your workflow, we have a reference that can help.


LightGBM Training with Dask

LightGBM is an open source library which implements a custom gradient-boosted decision tree (GBDT) algorithm. It has built-in distributed training which can be used to decrease training time or to train on more data. This article describes distributed LightGBM training with Dask.

Train a Model with XGBoost and Dask - Tutorial

XGBoost is an open source library which implements a custom gradient-boosted decision tree (GBDT) algorithm. It has built-in distributed training which can be used to decrease training time or to train on more data. This article describes distributed XGBoost training with Dask.

Train a Model with scikit-learn and Dask

This example gives an end-to-end script showing how a scikit-learn pipeline can run with Dask parallelization. Run it yourself, or use as a starting point for your own code.

Grid Search with Scikit-learn and Dask

This example demonstrates how scikit-learn grid search can be accelerated with Dask parallelization. Run it yourself, or use as a starting point for your own code.

Train Random Forest on GPU with RAPIDS

This example provides a working end-to-end script demonstrating how a Random Forest can be trained on a GPU using RAPIDS. Run it yourself, or use as a starting point for your own code.