Nvidia Fundamentals of Accelerated Data Science (FADS)
Classroom Schulung | Deutsch | Anspruch
Schulungsdauer: 1 Tag
Ziele
Learn how to perform multiple analysis tasks on large datasets using NVIDIA RAPIDS™, a collection of data science libraries that allows end-to-end GPU acceleration for data science workflows. Please note that once a booking has been confirmed, it is non-refundable. This means that after you have confirmed your seat for an event, it cannot be cancelled and no refund will be issued, regardless of attendance.
Zielgruppe
This course is designed for data scientists, analysts, engineers, and researchers who want to efficiently process large datasets and apply machine learning with GPU acceleration. It is ideal for professionals in fields such as data science, artificial intelligence, healthcare, traffic analysis, and high-performance computing (HPC) who work with massive amounts of data and seek to accelerate their analysis using GPUs.
Voraussetzungen
- Basic knowledge of Python and data processing
- Experience with data science and machine learning workflows
- Understanding of Pandas and NumPy (prior experience with RAPIDS or CUDA is not required)
- No GPU programming experience required, but beneficial
Agenda
Introduction
- Meet the instructor.
- Create an account at courses.nvidia.com/join
GPU-Accelerated Data Manipulation
Ingest and prepare several datasets (some larger-than-memory) for use in multiple machine learning exercises later in the workshop: Read data directly to single and multiple GPUs with cuDF and Dask cuDF. Prepare population, road network, and clinic information for machine learning tasks on the GPU with cuDF
GPU-Accelerated Machine Learning
- Apply several essential machine learning techniques to the data that was prepared in the first section:
- Use supervised and unsupervised GPU-accelerated algorithms with cuML.
- Train XGBoost models with Dask on multiple GPUs.
- Create and analyze graph data on the GPU with cuGraph.
Project: Data Analysis to Save the UK
- Apply new GPU-accelerated data manipulation and analysis skills with population-scale data to help stave off a simulated epidemic affecting the entire UK population:
- Use RAPIDS to integrate multiple massive datasets and perform real-world analysis.
- Pivot and iterate on your analysis as the simulated epidemic provides new data for each simulated day.
Ziele
Learn how to perform multiple analysis tasks on large datasets using NVIDIA RAPIDS™, a collection of data science libraries that allows end-to-end GPU acceleration for data science workflows. Please note that once a booking has been confirmed, it is non-refundable. This means that after you have confirmed your seat for an event, it cannot be cancelled and no refund will be issued, regardless of attendance.
Zielgruppe
This course is designed for data scientists, analysts, engineers, and researchers who want to efficiently process large datasets and apply machine learning with GPU acceleration. It is ideal for professionals in fields such as data science, artificial intelligence, healthcare, traffic analysis, and high-performance computing (HPC) who work with massive amounts of data and seek to accelerate their analysis using GPUs.
Voraussetzungen
- Basic knowledge of Python and data processing
- Experience with data science and machine learning workflows
- Understanding of Pandas and NumPy (prior experience with RAPIDS or CUDA is not required)
- No GPU programming experience required, but beneficial
Agenda
Introduction
- Meet the instructor.
- Create an account at courses.nvidia.com/join
GPU-Accelerated Data Manipulation
Ingest and prepare several datasets (some larger-than-memory) for use in multiple machine learning exercises later in the workshop: Read data directly to single and multiple GPUs with cuDF and Dask cuDF. Prepare population, road network, and clinic information for machine learning tasks on the GPU with cuDF
GPU-Accelerated Machine Learning
- Apply several essential machine learning techniques to the data that was prepared in the first section:
- Use supervised and unsupervised GPU-accelerated algorithms with cuML.
- Train XGBoost models with Dask on multiple GPUs.
- Create and analyze graph data on the GPU with cuGraph.
Project: Data Analysis to Save the UK
- Apply new GPU-accelerated data manipulation and analysis skills with population-scale data to help stave off a simulated epidemic affecting the entire UK population:
- Use RAPIDS to integrate multiple massive datasets and perform real-world analysis.
- Pivot and iterate on your analysis as the simulated epidemic provides new data for each simulated day.
