notebooks

cuML Notebooks

These notebooks provide examples of how to use cuML. These notebooks are designed to be self-contained with the runtime version of the RAPIDS Docker Container and RAPIDS Nightly Docker Containers and can run on air-gapped systems. You can quickly get this container using the install guide from the RAPIDS.ai Getting Started page

For a good overview of how cuML works, see the introductory notebook on estimators in the documentation tree.

Notebook Title	Status	Description
ARIMA Demo	Working
Forest Inference Demo	Working
KMeans Demo	Working
KMeans Multi-Node Multi-GPU Demo	Working
Linear Regression Demo	Working
Nearest Neighbors Demo	Working
Random Forest Demo	Working
Random Forest Multi-Node Multi-GPU Demo	Working

Many more examples can be found in the RAPIDS Notebooks Contrib repository, which contains community-maintained notebooks.

Name		Name	Last commit message	Last commit date
parent directory ..
data		data
tools		tools
README.md		README.md
arima_demo.ipynb		arima_demo.ipynb
forest_inference_demo.ipynb		forest_inference_demo.ipynb
kmeans_demo.ipynb		kmeans_demo.ipynb
kmeans_mnmg_demo.ipynb		kmeans_mnmg_demo.ipynb
linear_regression_demo.ipynb		linear_regression_demo.ipynb
nearest_neighbors_demo.ipynb		nearest_neighbors_demo.ipynb
random_forest_demo.ipynb		random_forest_demo.ipynb
random_forest_mnmg_demo.ipynb		random_forest_mnmg_demo.ipynb
target_encoder_walkthrough.ipynb		target_encoder_walkthrough.ipynb