# CUDA-X Data Science

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

## Table of Contents

- [Overview](#overview)
- [Instructions](#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 and 2-3 minutes to run each notebook. 
* **Risk level:** 
  * Data download slowness or failure due to network issues
  * Kaggle API generation failure requiring retries
* **Rollback:** No permanent system changes made during normal usage.

## Instructions

## Step 1. Verify system requirements
- Verify the system has CUDA 13 installed using `nvcc --version` or `nvidia-smi` 
- Install conda using [these instructions](https://docs.anaconda.com/miniconda/install/)
- Create Kaggle API key using [these instructions](https://www.kaggle.com/discussions/general/74235) 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)
  ```bash
    conda create -n rapids-test -c rapidsai-nightly -c conda-forge -c nvidia  \
    rapids=25.10 python=3.12 'cuda-version=13.0' \
    jupyter hdbscan umap-learn
  ```
## Step 3. Activate the conda environment
- Activate the conda environment
  ```bash
    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
  ```bash
    git clone https://github.com/NVIDIA/dgx-spark-playbooks
  ```
- 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 and use `localhost:8888` in your browser to access the notebook
  ```bash
    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 and use `localhost:8888` in your browser to access the notebook
  ```bash
    jupyter notebook cuml_sklearn_demo.ipynb
  ```
If you are remotely accessing your DGX-Spark then make sure to forward the necesary port to access the notebook in your local browser. Use the below   instruction for port fowarding
```bash
   ssh -N -L YYYY:localhost:XXXX username@remote_host 
```
- `YYYY`: The local port you want to use (e.g. 8888)
- `XXXX`: The port you specified when starting Jupyter Notebook on the remote machine (e.g. 8888)
- `-N`: Prevents SSH from executing a remote command
- `-L`: Spcifies local port forwarding
