GPU Computing with CUDA and Python Training in Gurgaon, Delhi

GPU Computing with CUDA and Python

Become an expert of Multi-GPU programming with CUDA. CuPy & NVLink

Duration : 6 months Classes : 36 Days : Weekdays / Weekends

Overview
Curriculum
Pre-requisite
Review

Unlocking Massively Parallel Power Traditional CPU processing has become the bottleneck for modern data science, machine learning, and high-performance computing (HPC). GPU Computing, powered by NVIDIA's CUDA platform, offers a paradigm shift, enabling massively parallel processing that can accelerate computations by orders of magnitude. Our intensive training dives deep into using CUDA with Python - specifically the Numba and CuPy libraries-to harness this exponential speed increase. This course is essential for engineers and researchers who need to overcome computational limits and drastically reduce the time required for training complex models, running simulations, and processing vast datasets.

Practical Mastery of Python-Accelerated Libraries This program is centered on practical implementation, teaching you how to integrate GPU acceleration directly into your existing Python workflows. You will gain hands-on expertise with Numba for kernel creation and function compilation, and master CuPy for NumPy-like array manipulation directly on the GPU. We focus on techniques for efficient memory management, data transfer optimization (minimizing CPU-GPU communication), and identifying bottlenecks in parallel code. By mastering these tools, you will be able to refactor sequential code into parallel kernels, transforming slow routines into high-speed operations without leaving the Python ecosystem.

Career Acceleration in AI and High-Performance Fields Proficiency in GPU programming is a highly specialized and incredibly valuable skill demanded by leading companies in AI, quantitative finance, scientific research, and autonomous systems. This training serves as a significant career accelerator, preparing you for roles like AI/ML Engineer, HPC Specialist, or Quant Developer. You will leave the course equipped not only with theoretical knowledge but with the ability to design, implement, and optimize highly efficient, GPU-accelerated algorithms, positioning you at the forefront of high-performance technical computing.

Target Audience:-
- Machine Learning/AI Engineers
- Data Scientists & Analysts
- HPC Specialists & Scientific Programmers
- Experienced Python Developers

Learning Outcomes:-
- Understand GPU Architecture
- Accelerate with Numba
- Utilize CuPy
- Optimize Data Transfer
- Refactor Sequential Code
- Measure and Analyze Performance

Course Format:-
✔ The course shall be delivered through a combination of lectures, interactive discussions & case studies
✔ Participants are exposed to practical exercises and new-age projects, where they learn by doing
✔ Participants shall have access to online resources, including reading materials, videos & business simulations
✔ Students shall receive all the study material
✔ Guest speakers from the industry may be invited to share insights and experiences
✔ Regular assessments and quizzes will be conducted to reinforce learning
✔ This is a Classroom only training
✔ Corporates: We understand your specific needs and goals. Contact us for customizations to this training

Trainers:-
✔ Equipped with multidisciplinary backgrounds
✔ Experts from the field of Maths, Financial Markets, AIML, Data Science & Management
✔ Each with over 25+ years of International experience working in EU / US / Australia
✔ All our trainers are Highly Qualified and Certified, in their respective subject areas

:- Experience with C/C++ and Python
:- You have theoretical knowledge of TensorFlow platform
:- You have a genuine interest in CUDA computing

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NB: All our trainings are always tailored to adopt to the Individual's Pace and Learning Depth.

NB: As a stepping stone, providing foundational knowledge, Bridge Courses are conducted periodically, to help students transition between different levels by closing knowledge gaps. These classes can be attended ad hoc, and are 'complimentary' for our bonafide students.

Kindly fill the DownloadPDF Form for the Brouchre with latest curriculum and full Training details.
Or you may Book an Appointment to collect your Brouchre and complete your registration.

This syllabus provides a structured, module-by-module breakdown of this comprehensive training program focused on participants overall performance, retention, and engagement, covering foundational theory, implementation, best industry practices and advanced techniques in the subject.

Module 1: Introduction to GPU Computing
✔ CPU vs GPU architecture and parallelism
✔ Applications of GPU computing in AI, simulations, and data science
✔ Overview of CUDA and NVIDIA GPU ecosystem
✔ Setting up the CUDA development environment with Python

Module 2: Python for Parallelism
✔ Python performance limitations and the need for acceleration
✔ Introduction to Numba and JIT compilation
✔ CPU/GPU acceleration
✔ Benchmarking and profiling Python code

Module 3: CUDA Programming Fundamentals
✔ CUDA programming model
✔ Memory hierarchy
✔ Writing and launching basic CUDA kernels
✔ Synchronization and thread management

Module 4: GPU Programming with Numba
✔ Writing GPU kernels in Python using Numba
✔ Memory management
✔ Parallelizing loops and vector operations
✔ Debugging and optimizing Numba kernels

Module 5: CuPy for GPU-Accelerated Arrays
✔ CuPy vs NumPy
✔ GPU-based array operations and broadcasting
✔ FFTs, linear algebra, and random number generation on GPU
✔ Interoperability with Numba and PyTorch

Module 6: Real-World Applications
✔ Accelerating matrix multiplication and convolution
✔ Image processing with GPU
✔ GPU-accelerated simulations
✔ Case study: speeding up a machine learning pipeline

Module 7: Performance Optimization
✔ Profiling tools
✔ Memory coalescing and shared memory usage
✔ Reducing divergence and optimizing occupancy
✔ Comparing CPU vs GPU performance

Module 8: Advanced Topics & Deployment
✔ Multi-GPU programming and peer-to-peer memory
✔ CUDA streams and asynchronous execution
✔ Integrating GPU code into Python applications
✔ Deploying GPU-accelerated apps on cloud platforms

Module 9: Capstone Project & Certification Prep
✔ End-to-end GPU computing project
✔ Best practices
✔ Preparing for NVIDIA DLI or CUDA Developer certifications

NB:The curriculum is regularly subjected to updates, reflecting the latest industry trends & current technological advancements.

At Vyom Data Sciences, we aspire to provide the latest curriculum and most recent technology, as a standard component of all our trainings. Experts, with 25+ years of experience from USA, Europe and Australia, bring the best industry practices while designing and executing these trainings. All our trainers are Highly Qualified and Certified in their respective subject areas.

Kindly fill the DownloadPDF Form for the Brouchre with latest curriculum and full Training details.
Or you may Book an Appointment to collect your Brouchre.

Bhawana

Fabulous NLP + ML course

I have eleven plus years of experience taking training courses. I do not usually complete surveys.
Your instructor was excellent, the best I've experienced on a software subject, and I couldn't imagine him doing a better job of seamlessly walking students through a breadth of information for such complex subject like AI and ML. he did a fabulous job pacing everything and addressing student questions. I am very impressed.

Harish

Excellent ML course!

The course was well structured and easy to understand. Good pace of learning.
The institute believes to provide knowledge as well as guidance in detail to each & every student.
I completed my ML course from the institute. Their international exp does help a lot !
Thanks for the training sir.