.. # SPDX-FileCopyrightText: Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved. # SPDX-License-Identifier: Apache-2.0 # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. .. _v0.11.0-beta: v0.11.0-beta ============ Release Highlights ------------------ CV-CUDA v0.11.0 includes critical bug fixes alongside the following changes:​ * **New Features**:​ * Enable NVCV to be built as static library​ * Improve Python doc generation and structure ​ * **Bug Fixes**:​ * Update pybind11 2.10.0 to 2.13.1. Fixes rare race conditions with Python garbage collector, adds compatibility with numpy2​ Compatibility and Known Limitations ----------------------------------- * **Pre-existing limitations**: * We note a bug in the YUV(420) color conversion API (NVCV_COLOR_RGB2YUV_I420) which incorrectly computes the U and V plane index​ * This persists through this release and we intend to address this bug in CV-CUDA v0.12.0​ For the full list, see main README on `CV-CUDA GitHub `_. License ------- CV-CUDA is licensed under the `Apache 2.0 `_ license. Resources --------- 1. `CV-CUDA GitHub `_ 2. `CV-CUDA Increasing Throughput and Reducing Costs for AI-Based Computer Vision with CV-CUDA `_ 3. `NVIDIA Announces Microsoft, Tencent, Baidu Adopting CV-CUDA for Computer Vision AI `_ 4. `CV-CUDA helps Tencent Cloud audio and video PaaS platform achieve full-process GPU acceleration for video enhancement AI `_ Acknowledgements ---------------- CV-CUDA is developed jointly by NVIDIA and the ByteDance Machine Learning team. .. [1] These fixes and features add micro-second-level overhead to Python operator calls. Based on the performance analysis of our Python samples, we expect the production- and pipeline-level impact to be negligible. CUDA kernel and C++ call performance is not affected. We aim to investigate and reduce this overhead further in a future release.​