To support developers, researchers, and engineers in deepening their technical skills, the PyTorch Foundation is running a dedicated Introduction Track at PyTorch Conference North America in San Jose, California, featuring the following speakers:

@NVIDIA - Aastha Jhunjhunwala, Mark Moyou @RedHat- Lucas Wilkinson, Matthew Bonanni @HuggingFace - Aritra Roy Gosthipaty, Suvaditya Mukherjee @CrusoeAI - JanakiRam Goteti, Suman Debnath ETH Zurich - Andrea Mattia Garavagno @Harvard University - Vijay Janapa Reddi @CloudNativeFdn - Yashasvi Misra

Additionally, an official, full-day PyTorch Associate Training session led by instructor Faradawn Yang will take place on Monday, October 19, 2026, offering hands-on labs, real-world projects, and a voucher for the PyTorch Certified Associate exam. Seats are limited, so register by Tuesday, October 13th to secure a spot and elevate your AI development skills.

Read our latest blog to learn which sessions will feature at the Introduction track and how to sign up to the PyTorch Associate Training session: https://bit.ly/4dE9899

Accelerate Your AI Journey with new Introduction Track at PyTorch Conference NA 2026 and PyTorch Associate Training – PyTorch

Source: https://pytorch.org/blog/accelerate-your-ai-journey-with-new-introduction-track-at-pytorch-conference-na-2026-and-pytorch-associate-training/

Featured projects

As deep learning models move rapidly from research prototypes into core enterprise infrastructure, the demand for practical, end-to-end PyTorch expertise has never been higher. Building robust neural networks requires more than just high-level understanding. It demands hands-on experience in training, optimizing, and deploying models effectively.

To support developers, researchers, and engineers looking to deepen their technical skills, the PyTorch Foundation is running a dedicatedIntroduction Trackduring the PyTorch Conference North America, as well as an official, full-dayPyTorch Associate Trainingsession onMonday, October 19, 2026, 9am – 5pm in San Jose, California.

In this blog we share what developers can expect from the technical sessions and training.

What to Expect from the Introduction Track

All attendees, from students, first time attendees all the way to seasoned AI engineers that want to learn about new areas are invited to check out the brand new Introduction Track at PyTorch Conference NA. Learn about areas of the PyTorch library, PyTorch ecosystem or AI stack that you are not familiar with. Explore foundational concepts, common workflows, project orientation, and approachable ways to begin building with the PyTorch ecosystem. Sessions include:

From Math Panic to PyTorch Confidence Yashasvi Misra, 3:25 PM PDT, Room LL20AB

Designed for Python developers, students, and educators new to machine learning, this session uses visual explanations and interactive exercises to demystify tensors, gradients, and dynamic computation graphs. Attendees will build intuitive mental models before implementing a foundational neural network and training loop in PyTorch.

Practical GPU Programming with Triton for PyTorch Developers Suman Debnath, JanakiRam Goteti, 11:45 AM PDT, Room LL20CD

PyTorch developers rely on tools liketorch.compileto generate GPU kernels, but writing custom kernels can feel daunting. This session introduces Triton, an open-source Python-embedded programming model for authoring high-performance GPU code without CUDA or C++. Attendees will learn GPU memory movement fundamentals, explore thread parallelization, and write custom vector addition and matrix multiplication kernels.

What You Cannot Profile, You Cannot Optimize: Learning to Read PyTorch Traces Aritra Roy Gosthipaty, Suvaditya Mukherjee, 2:50 PM PDT, Room LL20AB

Modern profiling tools generate complex traces that can be difficult to interpret. This talk builds practical mental models around CPU-to-GPU dispatch chains, compute-bound versus overhead-bound workloads, and kernel timing variations. Attendees will evaluate workloads usingtorch.compile, fused Triton kernels, and Liger kernels to make accurate performance comparisons.

From Scratch to PyTorch: Demystifying ML Frameworks by Building Your Own Andrea Mattia Garavagno, Vijay Janapa Reddi, 2:15 PM PDT, Room LL20AB

Understanding framework internals is critical for debugging complex logic, optimizing hardware, and contributing to backend infrastructure. This session introduces TinyTorch, an open-source CLI project that guides developers through rebuilding core framework components, including tensors, autograd, optimizers, and transformers, in pure Python.

Understanding Modern Vision Language Models Aastha Jhunjhunwala, Mark Moyou, 12:20 PM PDT, Room LL20AB

Vision-language models (VLMs) present unique serving and scaling challenges. Deconstructing five open source architectures, this talk examines image-to-token encoding, vision-language fusion strategies, multi-GPU fine-tuning, and production serving overheads such as KV-cache pressure and image-token expansion.

A Developer’s Guide to Attention in vLLM Lucas Wilkinson, Matthew Bonanni, 11:45 AM PDT, Room LL20AB

Modern state-of-the-art models rely on hybrid, sliding-window, sparse, and linear attention mechanisms to manage KV-cache memory over long context windows. This talk details how the vLLM engine represents and optimizes these diverse patterns through attention backends, KV-cache connectors, and hybrid memory allocators.

Elevate your AI Skills: PyTorch Associate Training

The PyTorch Foundation is excited to host an official, full-day, instructor-ledPyTorch Associate TrainingonMonday, October 19, 2026.

What to expect from the training

The PyTorch Associate Training is an in-person, instructor-led program, designed to equip participants with foundational and practical skills identified across the key domains of the PyTorch Certified Associate exam. Delivered through a structured series of sequential modules, the curriculum bridges the gap between core concepts and production-ready implementation.

Participants in this full-day workshop will work with:

• **Interactive Lectures & Live Demos:**Gain clear technical insights directly from experienced instructors as they demonstrate real-time model construction and training.
• **Guided Jupyter Notebook Labs:**Build, train, and optimize deep learning models from scratch in interactive lab environments.
• **Comprehension Checkpoints:**Solidify your understanding of key PyTorch ecosystem components through structured quizzes and reviews.
• **Practical Industry Projects:**Work on real-world use cases that mirror production scenarios and deployment challenges.

By learning alongside peers in an interactive setting, attendees receive direct feedback and real-time guidance from instructors.

To get the most out of this hands-on course, attendees should have:

• Proficiency in Python programming
• Familiarity with Jupyter Notebooks and basic Google Colab environment workflows
• A basic understanding of machine learning concepts
• An active Google Account for accessing practical exercises

Meet your Instructor

Faradawn Yang is an engineer on the AI platform software team at NVIDIA, where he focuses on AI inference products. Faradawn holds a master’s degree in computer science and a bachelor’s degree in mathematics and computer science from the University of Chicago. Before joining NVIDIA, he worked as a data engineer at a marketing measurement company.

Receive a Certification Voucher

To support your long-term professional development, every attendee who completes the training will receive a voucher (valued at $250) for the official**PyTorch Certified Associate (PTCA)**certification exam. Taking the PTCA exam provides a clear, industry-recognized credential to validate your practical skills in deep learning workflows using PyTorch.

Register now to elevate your AI skills

Pre-registration is required to secure your seat in the classroom, as space is limited to maintain an optimal student-to-instructor ratio.You can add thePyTorch Associate Trainingdirectly to your PyTorch Conference North America registration throughTuesday, October 13th.

Learn more and register for thePyTorch Associate Training at PyTorch Conference NA.