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  • View All Playbooks
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    onboarding

    • Set Up Local Network Access
    • Open WebUI with Ollama

    data science

    • Single-cell RNA Sequencing
    • Portfolio Optimization
    • CUDA-X Data Science
    • Build Knowledge Graphs with txt2kg
    • Optimized JAX

    tools

    • DGX Dashboard
    • RAG Application in AI Workbench
    • Set up Tailscale on Your Spark
    • VS Code
    • Connect Three DGX Spark in a Ring Topology
    • Connect Multiple DGX Spark through a Switch

    fine tuning

    • FLUX.1 Dreambooth LoRA Fine-tuning
    • LLaMA Factory
    • Fine-tune with NeMo
    • Fine-tune with Pytorch
    • Unsloth on DGX Spark

    use case

    • Run Hermes Agent with a Local LLM
    • cuTile Kernels
    • CLI Coding Agent
    • Run NemoClaw with a Local LLM
    • 🦞 Set Up Example NemoClaw Agents 🦞
    • Live VLM WebUI
    • Install and Use Isaac Sim and Isaac Lab
    • Vibe Coding in VS Code
    • Build and Deploy a Multi-Agent Chatbot
    • Connect Two Sparks
    • NCCL for Multiple Sparks
    • Build a Video Search and Summarization (VSS) Agent
    • Spark & Reachy Photo Booth
    • Secure AI Agents with OpenShell
    • Run OpenClaw with a Local LLM

    inference

    • Generate Images and Videos with ComfyUI
    • Serve LLMs with vLLM
    • Speculative Decoding
    • Run models with llama.cpp on DGX Spark
    • Nemotron Model Family on DGX Spark
    • Serve LLMs with SGLang
    • TRT LLM for Inference
    • Quantize Models to NVFP4 with NVIDIA Model Optimizer
    • Multi-modal Inference
    • NIM on Spark
    • LM Studio on DGX Spark

    CUDA-X Data Science

    30 MIN

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

    • DGX
    • Spark
    • clustering
    • data analytics
    • data science
    • dimensionality reduction
    • machine learning
    • pandas
    View on GitHub
    OverviewOverviewInstructionsInstructions

    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.
    • Risks:
      • Data download slowness or failure due to network issues
      • Kaggle API generation failure requiring retries
    • Rollback: No permanent system changes made during normal usage.
    • Last Updated: 11/07/2025
      • Minor copyedits

    Resources

    • NVIDIA RAPIDS Documentation
    • DGX Spark Documentation
    • DGX Spark DevZone Forum
    • DGX Spark User Performance Guide
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