CUDA-X Data Science

Install and use NVIDIA cuML and NVIDIA cuDF to accelerate UMAP, HDBSCAN, pandas and more with zero code changes

Overview
Instructions

Overview

Basic Idea

This playbook includes two example notebooks that demonstrate the acceleration of key machine learning algorithms and core pandas operations using CUDA-X Data Science libraries:

NVIDIA cuDF: Accelerates operations for data preparation and core data processing of 8GB of strings data, with no code changes.
NVIDIA cuML: Accelerates popular, compute intensive machine learning algorithms in sci-kit learn (LinearSVC), UMAP, and HDBSCAN, with no code changes.

CUDA-X Data Science (formally RAPIDS) is an open-source library collection that accelerates the data science and data processing ecosystem. These libraries accelerate popular Python tools like scikit-learn and pandas with zero code changes. On DGX Spark, these libraries maximize performance at your desk with your existing code.

What you'll accomplish

You will accelerate popular machine learning algorithms and data analytics operations GPU. You will understand how to accelerate popular Python tools, and the value of running data science workflows on your DGX Spark.

Prerequisites

Familiarity with pandas, scikit-learn, machine learning algorithms, such as support vector machine, clustering, and dimensionality reduction algorithms.
Install conda
Generate a Kaggle API key

Time & risk

Duration:
- 20-30 minutes setup time.
- 2-3 minutes to run each notebook.

Instructions

Step 1. Verify system requirements

Verify the system has CUDA 13 installed
Verify the python version is greater than 3.10
Install conda using these instructions
Create Kaggle API key using these instructions and place the kaggle.json file in the same folder as the notebook

Step 2. Installing Data Science libraries

Use the following command to install the CUDA-X libraries (this will create a new conda environment)

  conda create -n rapids-test -c rapidsai-nightly -c conda-forge -c nvidia  \
  rapids=25.10 python=3.12 'cuda-version=13.0' \
  jupyterlab hdbscan umap-learn

Step 3. Activate the conda environment

Activate the conda environment
```
  conda activate rapids-test
```

Step 4. Cloning the playbook repository

Clone the github repository and go the assets folder place in cuda-x-data-science folder

  git clone https://gitlab.com/nvidia/dgx-spark/temp-external-playbook-assets/dgx-spark-playbook-assets

Place the kaggle.json created in Step 1 in the assets folder

Step 5. Run the notebooks

There are two notebooks in the GitHub repository. One runs an example of a large strings data processing workflow with pandas code on GPU.

Run the cudf_pandas_demo.ipynb notebook

  jupyter notebook cudf_pandas_demo.ipynb

The other goes over an example of machine learning algorithms including UMAP and HDBSCAN.

Run the cuml_sklearn_demo.ipynb notebook

  jupyter notebook cuml_sklearn_demo.ipynb

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