From 6e92a53c19330c9802c9c121913c8f7cc3a340b9 Mon Sep 17 00:00:00 2001 From: Anerudhan Gopal Date: Tue, 17 Feb 2026 22:18:54 -0800 Subject: [PATCH] Update README to remove open-source kernel details Removed sections about open-sourcing kernels and related implementations. --- README.md | 11 ----------- 1 file changed, 11 deletions(-) diff --git a/README.md b/README.md index 6a956f442..2616f208d 100644 --- a/README.md +++ b/README.md @@ -1,18 +1,7 @@ - # cuDNN FrontEnd(FE) **cuDNN FE** is the modern, open-source entry point to the NVIDIA cuDNN library and high performance open-source kernels. It provides a C++ header-only library and a Python interface to access the powerful cuDNN Graph API and open-source kernels. -## 🚀 Embracing Open Source - -We will begin open-sourcing kernels based on customer needs, with the goal to educate developers and enable them to customize as needed. - -We are now shipping **OSS kernels**, allowing you to inspect, modify, and contribute to the core logic. Check out our latest implementations: - -* **[GEMM + Amax](https://github.com/NVIDIA/cudnn-frontend/tree/main/python/cudnn/gemm_amax):** Optimized FP8 matrix multiplication with absolute maximum calculation. -* **[GEMM + SwiGLU](https://github.com/NVIDIA/cudnn-frontend/tree/main/python/cudnn/gemm_swiglu):** High-performance implementation of the SwiGLU activation fused with GEMM. -* **[NSA](https://github.com/NVIDIA/cudnn-frontend/tree/main/python/cudnn/native_sparse_attention/):** Native Sparse attention as described in the Native Sparse Attention: Hardware-Aligned and Natively Trainable Sparse Attention. - ## Key Features * **Unified Graph API:** Create reusable, persistent `cudnn_frontend::graph::Graph` objects to describe complex subgraphs.