NVIDIA GTC 2021 Hosts Over 50 PyTorch Sessions

NVIDIA GTC 2021 is a week-long event that shares breakthroughs in AI, data center, accelerated computing, healthcare, gaming technology, and more. This year, GTC 2021 is hosting over 50 different sessions related to PyTorch.

See below for the list of sessions that mention or include PyTorch. You can register for the event for free and view the full listing of the session catalogue. Make sure you are logged in and the links below will take you directly to the relevant session.

PyTorch-based Sessions

1. PyTorch Performance Tuning Guide [S31831]
2. Profiling PyTorch Models for NVIDIA GPUs [S31644]
3. Dynamic Shapes First: Advanced GPU Fusion in PyTorch [S31952]
4. The State of PyTorch [S31223]
5. Scaling Large Models Using PyTorch RPC [S31630]
6. New Features in TRTorch, a PyTorch/TorchScript Compiler Targeting NVIDIA GPUs Using TensorRT [S31864]
7. Differentially Private Deep Learning on PyTorch [S31336]
8. Best Practices for Distributed Training Using PyTorch [S31626]
9. Learn to Train Complex GAN (StyleGANv2) 1.7x Faster on Tensor Cores and Upgrade from Previous PyTorch APEX to New PyTorch-Native Automatic Mixed Precision [S31203]
10. Slash Time Spent on Model Training and Tuning — Unleash Multi-Node GPUs! (Presented by Domino Data Lab) [SS33062]
11. Debugging and Understanding Deep Learning Models with Captum [S31581]
12. Introduction to TensorRT and Triton: A Walkthrough of Optimizing Your First Deep Learning Inference Model [SE2690]
13. Differentiable Physics Simulation for Learning and Robotics [S31838]
14. Fast, Accurate, Scalable: Building a Benchmark to Test Neural Time Series Models [S31950]
15. GTN: A Framework for Automatic Differentiation with Weighted Finite-State Automata [S32095]
16. Reducing Communication in Graph Neural Network Training [S31377]
17. Take Medical AI from Concept to Production using Clara Imaging [S32482]
18. Deep Learning Performance Optimization with Profiling Tools [S31228]
19. GPU Application in Simulation of VLSI Digital Circuits [S31263]

PyTorch Community Project Talks

1. Medical Imaging AI with MONAI Bootcamp [SE2684] by NVIDIA
2. Dinner with Strangers: MONAI-Powered [DWS2685] by NVIDIA
3. Presenting AdaptDL: An Open-Source Resource-Adaptive Deep Learning Training and Scheduling Framework [S31561] by Petuum Inc.
4. Curator: A No-Code, Self-Supervised Learning and Active Labeling Tool to Create Labeled Image Datasets from Petabyte-Scale Imagery [S32235] — based on PyTorch Lightning — Pinterest
5. How to Re-Create Google Duplex in 20 Minutes [S31287] — uses PyTorch Lightning by Dasha
6. How to Train Hundreds of Machine Learning Models in the Cloud from a Laptop [S32153] uses PyTorch Lightning by NVIDIA
7. Half The Memory with Zero Code Changes: Sharded Training with PyTorch Lightning [S31453] Grid AI/PyTorch Lightning
8. PyTorch-Direct: Introducing Deep Learning Framework with GPU-Centric Data Access for Faster Large GNN Training [S32038] — University of Illinois Urbana Champaign

PyTorch Case Study Sessions

1. Deep Learning for Anomaly Detection [S32090] — Morgan Stanley
2. Reinforcement Learning and Intralogistics: Soft Actor Critic for Maples Navigation in Warehouses [E31467] — Kion Group/University of Luebeck
3. Geological Interpretation with Open AI and CUDA Tools Outperforms Human Experts and Delivers Profits for the Oil & Gas Industry in Turbulent Times [E31440] — Data Analysis Center
4. Zero to COVID-19 Treatments in Under Four Weeks with Deep-Learning-Driven Drug Screens [S31542] — Recursion Pharmaceuticals
5. Machine Learning for VFX with Nuke and CopyCat [S31519] — Foundry

PyTorch Poster Sessions

1. Scaling Distributed PyTorch and DeepSpeed with MVAPICH2-GDR [P31678] — Ohio State University

Generic Topics Spanning All Frameworks

1. Real-time AI for Video-Conferencing with Maxine [S31787] — Headroom, examples include PyTorch and TensorFlow
2. Data Science for Adaptive Testing [S31494] — uses PyTorch (GPyTorch) or TensorFlow (GPFlow), NVIDIA
3. TensorRT Quick Start Guide [S31828] — generic to all frameworks — NVIDIA
4. Real-Time Inferencing at the Edge [S32829] — generic to all frameworks — GMAC Intelligence
5. Interpretability for Deep Learning Models [S32019] — generic to TensorFlow/Keras and PyTorch — Wells Fargo/Hong Kong University
6. Easily Deploy AI Deep Learning Models at Scale with Triton Inference Server [S31114] — generic to all frameworks, NVIDIA
7. ONNX Runtime: Accelerating PyTorch and TensorFlow Inferencing on Cloud and Edge [S32240] NVIDIA/Microsoft
8. Dive into Deep Learning: Code Side-by-Side with MXNet, PyTorch, and TensorFlow [S31692] — AWS
9. Optimizing, Profiling, and Scaling Deep Learning Training [CWES1186] — NVIDIA