v0.10.0-beta

Release Highlights

CV-CUDA v0.10.0 includes a critical bug fix (cache growth management) alongside the following changes:

  • New Features:

    • Added mechanism to limit and manage cache memory consumption (includes new “Best Practices” documentation) 1.

    • Performance improvements of color conversion operators (e.g., 2x faster RGB2YUV).

    • Refactored codebase to allow independent build of NVCV library (data structures).

  • Bug Fixes:

    • Fixed unbounded cache memory consumption issue 1.

    • Improved management of Python-created object lifetimes, decoupled from cache management 1.

    • Fixed potential crash in Resize operator’s linear and nearest neighbor interpolation from non-aligned vectorized writes.

    • Fixed Python CvtColor operator to correctly handle NV12 and NV21 outputs.

    • Fixed Resize and RandomResizedCrop linear interpolation weight for border rows and columns.

    • Fixed missing parameter in C API for fused ResizeCropConvertReformat.

    • Fixed several minor documentation and error output issues.

    • Fixed minor compiler warning while building Resize operator.

Compatibility and Known Limitations

  • New limitations:

    • Cache/resource management introduced in v0.10 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.​

    • Sporadic Pybind11-deallocation crashes have been reported in long-lasting multi-threaded Python pipelines with externally allocated memory (eg wrapped Pytorch buffers). We are evaluating an upgrade of Pybind11 (currently using 2.10) as a potential fix in an upcoming release.

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(1,2,3)

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.​