GPU Programming with CUDA and Python Training in Auckland

GPU Programming with CUDA and Python Training in Auckland, New Zealand

Attendees will learn how to write parallel programs, offload computations to the GPU, and optimize performance using Numba, CuPy, and other CUDA-Python tools.

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GPU Programming with CUDA and Python Training is a professional training program delivered by ProgNXT in Auckland, New Zealand. ProgNXT, a globally recognized corporate training provider in 55+ countries, offers this GPU Programming with CUDA and Python Training course with hands-on, instructor-led sessions. This course introduces participants to GPU programming concepts using NVIDIA CUDA and Python. Through hands-on examples, attendees will learn how to...

Expert Panel

Designed and delivered by the ProgNXT Programming Expert Panel — a vetted team of industry professionals with 14+ years of hands-on experience in Python, Java, C++, and full-stack application development

ProgNXT Programming Expert Panel

Course Code: FTU88
21 Hrs
Last Updated: April 2026

Course Rating5/5

This course introduces participants to GPU programming concepts using NVIDIA CUDA and Python. Through hands-on examples, attendees will learn how to write parallel programs, offload computations to the GPU, and optimize performance using Numba, CuPy, and other CUDA-Python tools. The course is designed for Python developers who want to accelerate numerical computations and data processing using GPU resources.

Welcome to the official GPU Programming with CUDA and Python Training certification program in Auckland, New Zealand. This comprehensive training is designed to elevate your professional skills and provide you with practical, industry-relevant knowledge locally. As a globally recognized corporate training provider operating in 55+ countries, ProgNXT ensures that our curriculum meets the highest standards of excellence.

Whether you are looking to upskill your team or advance your personal career, our expert-led sessions will guide you through the core concepts of this domain. Upon successful completion of the 21 Hrs program, participants will receive a globally accepted certification, demonstrating their proficiency and readiness to tackle complex challenges in the field.

Solid understanding of Python programming
Basic knowledge of arrays and numerical computing (NumPy)
Familiarity with Linux and command-line interface
No prior GPU programming experience required

By the end of this training, participants will:

Write and run Python code that leverages GPU acceleration
Understand how to offload and parallelize tasks using CUDA
Use libraries like Numba and CuPy for high-performance computing
Optimize memory usage and computation for large datasets
Be prepared to use GPUs in machine learning, image processing, and scientific simulations

Understand GPU computing and CUDA architecture
Write parallel code using Python and Numba CUDA
Accelerate NumPy-based computations with CuPy
Optimize GPU kernels for memory and performance
Debug and profile GPU workloads

Kernel definition and execution with Numba
GPU memory management and data transfer
Vectorized operations with CuPy
Profiling and optimization techniques
Comparing CPU vs GPU performance

ProgNXT Certificate of Completion will be awarded to participants who complete the course and pass the evaluations.

INTRODUCTION TO GPU COMPUTING AND CUDA-PYTHON

Introduction to GPU Programming Concepts

CPU vs GPU architecture

What is CUDA and how it fits into Python workflows

Use cases: image processing, scientific computing, ML acceleration

Python Tools for CUDA Programming

Overview of Numba, CuPy, PyCUDA

Installing and verifying CUDA environment

Running basic GPU-accelerated code in Python

Getting Started with Numba CUDA

Writing CUDA kernels using Numba

Launch configuration: blocks, threads, grid

Data transfer between CPU and GPU

GPU MEMORY, VECTORIZATION, AND ADVANCED KERNELS

Memory Management and Performance Optimization

Types of GPU memory: global, shared, constant

Optimizing memory access patterns

Minimizing CPU-GPU data transfer overhead

Vectorization and CuPy for NumPy Users

Migrating NumPy code to CuPy

Universal functions (ufuncs) and element-wise operations

Broadcasting, slicing, and array math on GPU

APPLICATIONS, DEBUGGING, AND PROFILING

Debugging and Profiling CUDA-Python Code

Using cuda.profile and nvprof with Python

Measuring kernel execution time and memory usage

Common performance bottlenecks and solutions

Real-World Applications and Mini-Projects

Case studies: matrix multiplication, convolution, or financial simulations

Implementing and optimizing a sample GPU-accelerated project in Python

Comparing CPU-only vs GPU-enhanced solutions

Final Wrap-Up and Next Steps

Best practices for GPU Python workflows

When to use GPU and when not to

Resources to continue learning CUDA and parallel programming

Participants will receive course materials, slides, reference materials, exercises and access to resources for further learning.

Participants will be evaluated based on:
Daily quizzes, mock questions and query resolution. Participation in hands-on exercises and labs. Performance in the final review and hands-on exercises will be evaluated.
ProgNXT Certificate of Completion will be awarded to participants who complete the course and pass the evaluations.

GPU Programming with CUDA and Python Training Events in auckland

Online Events

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More Upcoming Session Dates

October 2026

🌐 Online Instructor-Led

22nd Oct 2026→24th Oct 2026

November 2026

📍 Auckland, New Zealand

3rd Nov 2026→5th Nov 2026

🌐 Online Instructor-Led

9th Nov 2026→11th Nov 2026

📍 Auckland, New Zealand

22nd Nov 2026→24th Nov 2026

🌐 Online Instructor-Led

23rd Nov 2026→25th Nov 2026

December 2026

🌐 Online Instructor-Led

6th Dec 2026→8th Dec 2026

📍 Auckland, New Zealand

12th Dec 2026→14th Dec 2026

📍 Auckland, New Zealand

23rd Dec 2026→25th Dec 2026

🌐 Online Instructor-Led

24th Dec 2026→26th Dec 2026

Events for other courses in Auckland, New Zealand

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GPU Programming with CUDA and Python Training in Auckland