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RAPIDS Extended Notebooks

Intro

Welcome to the extended notebooks repo!

The purpose of this collection of notebooks is to help users understand what RAPIDS has to offer, learn why, how, and when including RAPIDS in a data science pipeline makes sense, and contain community contributions of RAPIDS knowledge.

Many of these notebooks use additional PyData ecosystem packages, and include code for downloading datasets, thus they require network connectivity. If running on a system with no network access, please use the core notebooks repo.

Installation

Please use the BUILD.md to check the pre-requisite packages and installation steps.

Contributing

Please see our guide for contributing to notebooks-extended.

Exploring the Repo

  • getting_started_notebooks - “how to start using RAPIDS”. Contains notebooks showing "hellow worlds", getting started with RAPIDS libraries, and tutorials around RAPIDS concepts.
  • intermediate - “how to accomplish your workflows with RAPIDS”. Contains notebooks showing algorthim and workflow examples, benchmarking tools, and some complete end-to-end (E2E) workflows.
  • advanced - "how to master RAPIDS". Contains notebooks showing kernal customization and advanced end-to-end workflows.
  • colab_notebooks - contains colab versions of popular notebooks to quickly try out in browser
  • blog notebooks - contains shared notebooks mentioned and used in blogs that showcase RAPIDS workflows and capabilities
  • conference notebooks - contains notebooks used in conferences, such as GTC
  • competition notebooks - contains notebooks used in competitions, such as Kaggle

/data contains small data samples used for purely functional demonstrations. Some notebooks include cells that download larger datasets from external websites.

The /data folder is also symlinked into /rapids/notebooks/extended/data so you can browse it from JupyterLab's UI.

RAPIDS Notebooks-extended

Industry Topical Notebooks

Please view our Industry Topics README to see which notebooks align with which industries (coming soon!)

Getting Started Notebooks:

Folder Notebook Title Description
basics Dask_Hello_World This notebook shows how to quickly setup Dask and run a "Hello World" example.
basics Getting_Started_with_cuDF This notebook shows how to get started with GPU DataFrames using cuDF in RAPIDS.
intro_tutorials 01_Introduction_to_RAPIDS This notebook shows at a high level what each of the packages in RAPIDS are as well as what they do.
intro_tutorials 02_Introduction_to_cuDF This notebook shows how to work with cuDF DataFrames in RAPIDS.
intro_tutorials 03_Introduction_to_Dask This notebook shows how to work with Dask using basic Python primitives like integers and strings.
intro_tutorials 04_Introduction_to_Dask_using_cuDF_DataFrames This notebook shows how to work with cuDF DataFrames using Dask.
intro_tutorials 05_Introduction_to_Dask_cuDF This notebook shows how to work with cuDF DataFrames distributed across multiple GPUs using Dask.
intro_tutorials 06_Introduction_to_Supervised_Learning This notebook shows how to do GPU accelerated Supervised Learning in RAPIDS.
intro_tutorials 07_Introduction_to_XGBoost This notebook shows how to work with GPU accelerated XGBoost in RAPIDS.
intro_tutorials 08_Introduction_to_Dask_XGBoost This notebook shows how to work with Dask XGBoost in RAPIDS.
intro_tutorials 09_Introduction_to_Dimensionality_Reduction This notebook shows how to do GPU accelerated Dimensionality Reduction in RAPIDS.
intro_tutorials 10_Introduction_to_Clustering This notebook shows how to do GPU accelerated Clustering in RAPIDS.

Intermediate Notebooks:

Folder Notebook Title Description
examples DBSCAN_Demo_FULL This notebook shows how to use DBSCAN algorithm and its GPU accelerated implementation present in RAPIDS.
examples Dask_with_cuDF_and_XGBoost In this notebook we show how to quickly setup Dask and train an XGBoost model using cuDF.
examples Dask_with_cuDF_and_XGBoost_Disk In this notebook we show how to quickly setup Dask and train an XGBoost model using cuDF and read the data from disk using cuIO.
examples One_Hot_Encoding In this notebook we show how to use dask and cudf to use xgboost on a dataset.
examples PCA_Demo_Full In this notebook we will show how to use PCA and its GPU accelerated implementation present in RAPIDS.
examples linear_regression_demo.ipynb In this notebook we will show how to use linear regression and its GPU accelerated implementation present in RAPIDS.
examples ridge_regression_demo Demonstration of using both NetworkX and cuGraph to compute the the number of Triangles in our test dataset.
examples umap_demo In this notebook we will show how to use UMAP and its GPU accelerated implementation present in RAPIDS.
examples rf_demo Demonstration of using both cuml and sklearn to train a RandomForestClassifier on the Higgs dataset.
E2E-> mortgage mortgage_e2e This is an end to end notebook consisting of ETL, data conversion and machine learning for training operations performed on the mortgage dataset.
E2E-> mortgage mortgage_e2e_deep_learning This notebook combines the RAPIDS GPU data processing with a PyTorch deep learning neural network to predict mortgage loan delinquency.
E2E-> taxi NYCTaxi Demonstrates multi-node ETL for cleanup of raw data into cleaned train and test dataframes. Shows how to run multi-node XGBoost training with dask-xgboost
E2E-> synthetic_3D rapids_ml_workflow_demo A 3D visual showcase of a machine learning workflow with RAPIDS (load data, transform/normalize, train XGBoost model, evaluate accuracy, use model for inference). Along the way we compare the performance gains of RAPIDS [GPU] vs sklearn/pandas methods [CPU].
E2E-> census census_education2income_demo In this notebook we se 50 yeras of centus data to see how education affects income.
benchmarks cuml_benchmarks The purpose of this notebook is to benchmark all of the single GPU cuML algorithms against their skLearn counterparts, while also providing the ability to find and verify upper bounds.
benchmarks-> cugraph_benchmarks louvain_benchmark This notebook benchmarks performance improvement of running the Louvain clustering algorithm within cuGraph against NetworkX.
benchmarks-> cugraph_benchmarks pagerank_benchmark This notebook benchmarks performance improvement of running PageRank within cuGraph against NetworkX.

Advanced Notebooks:

Folder Notebook Title Description
tutorials rapids_customized_kernels This notebook shows how create customized kernels using CUDA to make your workflow in RAPIDS even faster.

Blog Notebooks:

Folder Notebook Title Description
cyber -> flow_classification flow_classification_rapids This notebook demonstrates how to load netflow data into cuDF and create a multiclass classification model using XGBoost.
cyber ->network_mapping lanl_network_mapping_using_rapids This notebook demonstrates how to parse raw windows event logs using cudf and uses cuGraph's pagerank model to build a network graph.
cyber ->raw_data_generator run_raw_data_generator The notebook is used showcase how to generate raw logs from the parsed LANL 2017 json data. The intent is to use the raw data to demonstrate parsing capabilities using cuDF.
databricks RAPIDS_PCA_demo_avro_read This notebooks purpose is to showcase RAPIDS on Databricks use thier sample datasets and show the CPU vs GPU comparison for the PCA algorithm. There is also an accompanying HTML file for easy Databricks import.
regression regression_blog_notebook This notebook showcases an end to end notebook using the try_this dataset and cuML's implementation of ridge regression.

Conference Notebooks:

Folder Notebook Title Description
GTC_SJ_2019 GTC_tutorial_instructor Description comming soon!
GTC_SJ_2019 GTC_tutorial_student Description comming soon!

Competition Notebooks:

Folder Notebook Title Description
kaggle-> landmark cudf_stratifiedKfold_1000x_speedup Description comming soon!
kaggle-> malware malware_time_column_explore Description comming soon!
kaggle-> malware rapids_solution_gpu_only Description comming soon!
kaggle-> malware rapids_solution_gpu_vs_cpu Description comming soon!
kaggle-> plasticc-> notebooks rapids_lsst_full_demo Description comming soon!
kaggle-> plasticc-> notebooks rapids_lsst_gpu_only_demo Description comming soon!
kaggle-> santander cudf_tf_demo Description comming soon!
kaggle-> santander E2E_santander_pandas Description comming soon!
kaggle-> santander E2E_santander Description comming soon!

Additional Information

  • The cuml folder also includes a small subset of the Mortgage Dataset used in the notebooks and the full image set from the Fashion MNIST dataset.

  • utils: contains a set of useful scripts for interacting with RAPIDS

  • For additional, community driven notebooks, which will include our blogs, tutorials, workflows, and more intricate examples, please see the Notebooks Extended Repo