Tag: UF Research training

Data Parallelism: How to Train Deep Learning Models on Multiple GPUs

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The NVIDIA AI Technology Center at the University of Florida is offering an instructor-led, deep learning institute workshop in April: Data Parallelism: How to Train Deep Learning Models on Multiple GPUs.

Workshop Dates: April 11-12, 2024 (Thursday and Friday), from 1:00-5:00 p.m.

Registration Link: https://forms.gle/KiNxdjqxJ7AZCZFk6

The workshop will be held over two days (four hours each day) in Malachowsky Hall’s NVIDIA Auditorium. Its focus is on techniques for data-parallel deep learning training on multiple GPUs to shorten the training time required for data-intensive applications. Working with deep learning tools, frameworks, and workflows to perform neural network training, attendees will learn how to decrease model training time by distributing data to multiple GPUs, while retaining the accuracy of training on a single GPU. The full course outline may be found on this NVIDIA website page.

The course is FREE and open to the university community, but pre-registration is required. Also required is experience with Python. Technologies used in the workshop are PyTorch, PyTorch Distributed Data Parallel, and NCCL.

If you have any questions about this workshop, please email the instructor, NVIDIA Data Scientist Yungchao Yang (yunchaoyang@ufl.edu).

 

Spring 2023 HiPerGator Training

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UFIT Research Computing is hosting a variety of trainings and workshops throughout the Spring 2023 semester. The options include HiPerGator user training, panel events, in-person training, and networking opportunities for UF’s research community.

The robust schedule features multiple virtual NVIDIA Deep Learning Institute (DLI) workshops on the fundamentals for deep learning and for accelerated computing with CUDA Python. The always popular Birds-of-a-Feather sessions (BOF), facilitated by Research Computing staff, are for current and potential HiPerGator users to introduce high performance computing and AI resources and services available, such as accelerated genomics and MLFlow. There are also two AI panels scheduled. The first panel is for promoting women in HPC&AI, and the second will discuss the use of AI in arts and humanities research.

All UFIT Research Computing training, panels, and BOF sessions are free. To register for any of the offerings, visit https://rc.ufl.edu/calendar/. Faculty and staff can also request group, department, or 1-on-1 training consultations. For assistance with custom training needs, please contact UFIT’s Training and Biocomputing Specialist, Dr. Matt Gitzendanner.

Medical Imaging for AI Research Inquiries

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UFIT is hosting two MONAI-focused tutorials in July. Both tutorials will be held via Zoom:

Tutorial Name: MONAI Core
Date: Tuesday, July 12, 12:00–1:00 p.m.
Registration Link
Description: MONAI Core is a PyTorch based and GPU-accelerated deep learning framework, specifically designed for medical imaging. This tutorial will cover:
• Why MONAI Core: the unique and impactful features of MONAI Core
• MONAI Core on HiPerGator: end-to-end demo on HiPerGator

Tutorial Name: MONAI Label for Medical Imaging with NVIDIA
Date: Tuesday, July 26, 12:00–1:00 p.m.
Registration Link
Description: MONAI Label is an open-source medical-imaging-specific tool for both AI-assisted annotation and building your own AI annotation models. This tutorial has two parts:
• An in-depth MONAI Label introduction
• A step-by-step demo on HiPerGator

MONAI is a freely available, community-supported, PyTorch-based framework for deep learning in healthcare imaging. MONAI provides domain-optimized foundational capabilities for developing healthcare imaging training workflows in a native PyTorch paradigm. Anyone with questions about the MONAI tutorials or other training opportunities offered by UFIT Research Computing may contact Dr. Matt Gitzendanner.