{"id":1786,"date":"2026-08-19T13:08:05","date_gmt":"2026-08-19T13:08:05","guid":{"rendered":"https:\/\/blog.openzeka.com\/en\/?p=1786"},"modified":"2026-08-19T13:08:05","modified_gmt":"2026-08-19T13:08:05","slug":"gpu-accelerated-data-science-and-machine-learning-on-dgx-spark","status":"publish","type":"post","link":"https:\/\/blog.openzeka.com\/en\/gpu-accelerated-data-science-and-machine-learning-on-dgx-spark\/","title":{"rendered":"GPU-Accelerated Data Science and Machine Learning on DGX Spark"},"content":{"rendered":"

In this tutorial, you will accelerate data science and machine learning workflows on a DGX Spark using its GPU.<\/p>\n

The Spark can be used as a standalone computer by connecting a monitor and keyboard, or as a remote server accessed from another computer. In this tutorial, we will connect to the Spark remotely and install the necessary software to run our workloads.<\/p>\n

We will use pandas as our data processing library and scikit-learn as our machine learning library. These two libraries are foundational to the data science and machine learning ecosystem. In this tutorial, we will accelerate their workflows on the GPU using CUDA-X.<\/p>\n

CUDA-X is the umbrella name for NVIDIA’s collection of GPU acceleration libraries. Within this collection, the set containing data science libraries is called RAPIDS. In this tutorial, we will use cuDF from the RAPIDS library suite to accelerate pandas, and cuML to accelerate scikit-learn.<\/p>\n

This tutorial consists of three parts:<\/p>\n

    \n
  • Setup:<\/strong> Installing Miniconda, creating conda environments, and cloning the playbook repository<\/li>\n
  • Running the Notebooks:<\/strong> Running two notebooks in both CPU and GPU environments<\/li>\n
  • Comparison:<\/strong> Comparing CPU and GPU performance cell by cell<\/li>\n<\/ul>\n
    \n

    Setup<\/h2>\n

    Connecting to the Spark<\/h3>\n

    If you are connecting to the Spark remotely for the first time, you need to find its IP address. Connect a monitor and keyboard to the Spark, log in, and run the following command in the terminal:<\/p>\n<\/div>