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  • Numpy 2. 0 support status? · Issue #2671 · catboost catboost - GitHub
    Problem: ValueError: numpy dtype size changed, may indicate binary incompatibility Expected 96 from C header, got 88 from PyObject when reaching import catboost as cb with numpy 2 0 catboost version: version not specified during install
  • catboost - PyPI
    CatBoost is a fast, scalable, high performance gradient boosting on decision trees library Used for ranking, classification, regression and other ML tasks
  • Python package installation - CatBoost
    As of CatBoost 1 2 8, devices with CUDA compute capability >= 3 5 are supported in released packages All necessary CUDA libraries are statically linked in the released Linux and Windows binaries, the only installation necessary is the appropriate version of the CUDA driver
  • CatBoost 1. 2. 8版本发布：全面支持Python 3. 13与NumPy 2. x - GitCode博客
    本次1 2 8版本最重要的改进之一是全面支持最新的Python 3 13环境，这确保了开发者可以在最新的Python环境中无缝使用CatBoost。 同时，该版本也正式支持NumPy 2 x系列，为数据科学工作流提供了更好的兼容性。 值得注意的是，随着Python生态的发展，1 2 8版本已经放弃了对Python 3 7的支持，建议仍在使用旧版本Python的用户考虑升级到3 8及以上版本。 在GPU计算方面，本次更新有两项重要改进： 当在GPU上计算自定义指标时，现在会正确使用实现类的名称作为字符串ID进行存储。 这一改进使得在分布式训练或模型保存 加载场景下，自定义指标的处理更加可靠。 改进了GPU上自定义指标代码的异常传播机制。
  • 1. 2. 8 · catboost catboost · Discussion #2855 · GitHub
    Fix race condition when loading sparse datasets This discussion was created from the release 1 2 8
  • catboost 1. 2. 8 on PyPI - Libraries. io - security maintenance data for . . .
    Main advantages of CatBoost: Superior quality when compared with other GBDT libraries on many datasets Best in class prediction speed Support for both numerical and categorical features Fast GPU and multi-GPU support for training out of the box Visualization tools included Fast and reproducible distributed training with Apache Spark and CLI
  • Download - CatBoost
    The built versions of CatBoost have GPU support out-of-the-box As of CatBoost 1 2 8, devices with CUDA compute capability >= 3 5 are supported in released packages
  • Releases · catboost catboost - GitHub
    [Performance]: Make features data initialization from C-order numpy ndarray s with float32 data type multithreaded Significant speedups of 5x up to 10x (on CPUs with many cores) can be expected #385, #2542


















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