Talk: Kernels Deep Dive (Ben Burtenshaw)
In this talk, Ben Burtenshaw from Hugging Face breaks down why optimized kernels are critical for real-world deep learning performance and how the Hugging Face Kernels ecosystem makes them easier to build and use. He covers memory-bound bottlenecks, the kernel-builder workflow, reproducible multi-hardware builds with Nix, and practical PyTorch/Transformers integration patterns that reduce setup time from hours to seconds. ## Chapters 0:00 Intro and speaker background 1:35 Why Hugging Face Kernels matters 2:05 Compute vs memory bottlenecks in deep learning 3:30 Fused kernels and why they speed things up 5:05 Talk agenda and ecosystem overview 5:35 Kernel pain points: fragmentation and long installs 7:12 Supporting older, cheaper hardware for the community 8:18 Goal: from CMake errors to one-line kernel usage 8:54 Kernels + kernel-builder architecture 10:00 Reproducible builds with Nix and support matrix 11:45 Kernel project structure (`build.toml`, sources, torch extension) 12:23 Publishing kernels to the Hugging Face Hub 13:25 Real-world gain: faster FlashAttention setup 14:18 Docs, repos, and how to get started 16:00 Verifying compatibility and loading kernels in Python 17:20 Managing local cache with `hf cache ls` 17:55 Kernelizing PyTorch layers with hub mappings 19:32 Transformers integration (`use_kernels=True`) 20:48 Performance chart and closing resources Learn more: https://aifoundry.org/ https://huggingface.co/docs/kernels https://github.com/huggingface/kernels https://huggingface.co/kernels-community The video was created by @AIFoundry Org
