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PyTorch Conference North America 2026: 7 Tracks and a 40% Discount

PyTorch Conference North America 2026: 7 Tracks and a 40% Discount

PyTorch Conference North America 2026 - October 20-21, San Jose

After KubeCon + CloudNativeCon North America and AGNTCon, the Linux Foundation is back with PyTorch Conference North America 2026, running October 20-21 in San Jose. If you’re building with PyTorch, this is where the maintainers, researchers, and production engineers behind the ecosystem show up to share what actually works.

What your pass includes

  • All conference content, including keynotes, sessions, and panels
  • Social events and networking activities
  • Community Expo entry
  • Lunch, plus morning and afternoon coffee and snacks
  • Event swag

Program at a glance

  • 7 technical tracks
  • 3 co-located events
  • Evening events
  • Demo theater
  • Poster sessions

Co-located events

Three events run alongside the main conference this year:

  • Arm Create - October 19, The Tech Interactive. Hands-on demos and an Arm-powered digital badge.
  • Jupyter Day - October 19, San Jose Convention Center. $99 standalone, or add it to your PyTorch Conference registration.
  • AGNTCon + MCPCon - October 22-23, San Jose Convention Center. Add it to your PyTorch Conference registration for 20% off. We covered AGNTCon + MCPCon North America separately, with its own techbeatly discount code.

Session highlights

  • Observability Tooling for Cudagraph Workloads - Natalia Gimelshein and Driss Guessous, Meta
  • Unlocking the Full Potential of TorchDynamo: Accelerating, Comparing, and Debugging ML Systems - Yi Pan, UC Berkeley, and Megan Frisella and Stephanie Wang, University of Washington
  • Scaling Foundation Models: From Broken to Near-Linear Multi-Node Training - Sheng Huang, Pinterest

40% off for the techbeatly community

Note

Discount applies to current pricing only and does not apply to academic pricing.

Use code TECHBEATLY at checkout for 40% off.

Why it’s worth your time

Two days, seven tracks, and sessions on the hard parts of running PyTorch at scale - GPU observability, dynamo internals, and multi-node training that doesn’t fall apart past a few nodes. If any of that is on your plate this year, worth carving out the time.

Gineesh Madapparambath

Gineesh Madapparambath

Gineesh Madapparambath is the founder of techbeatly. He is the co-author of The Kubernetes Bible, Second Edition and the author of Ansible for Real Life Automation. He has worked as a Systems Engineer, Automation Specialist, and content author. His primary focus is on AI, Ansible Automation, Containerization (OpenShift & Kubernetes), and Infrastructure as Code (Terraform). (Read more: gineesh.com)


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