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  2. Anaconda (Python distribution) - Wikipedia

    en.wikipedia.org/wiki/Anaconda_(Python_distribution)

    The Conda package manager's historical differentiation analyzed and resolved these installation conflicts. [ 39 ] Anaconda is a distribution of the Python and R programming languages for scientific computing ( data science , machine learning applications, large-scale data processing , predictive analytics , etc.), that aims to simplify package ...

  3. Conda (package manager) - Wikipedia

    en.wikipedia.org/wiki/Conda_(Package_Manager)

    Conda checks everything that has been installed, any version limitations that the user specifies (for example, the user wants a specific package to be at least version 2.1.3), and determines a set of versions for all requested packages and their dependencies that makes the total set compatible with one another.

  4. PyTorch - Wikipedia

    en.wikipedia.org/wiki/PyTorch

    In September 2022, Meta announced that PyTorch would be governed by the independent PyTorch Foundation, a newly created subsidiary of the Linux Foundation. [ 24 ] PyTorch 2.0 was released on 15 March 2023, introducing TorchDynamo , a Python-level compiler that makes code run up to 2x faster, along with significant improvements in training and ...

  5. Torch (machine learning) - Wikipedia

    en.wikipedia.org/wiki/Torch_(machine_learning)

    Torch is used by the Facebook AI Research Group, [8] IBM, [9] Yandex [10] and the Idiap Research Institute. [11] Torch has been extended for use on Android [12] [better source needed] and iOS. [13] [better source needed] It has been used to build hardware implementations for data flows like those found in neural networks. [14]

  6. pip (package manager) - Wikipedia

    en.wikipedia.org/wiki/Pip_(package_manager)

    pip (also known by Python 3's alias pip3) is a package-management system written in Python and is used to install and manage software packages. [4] The Python Software Foundation recommends using pip for installing Python applications and its dependencies during deployment. [5]

  7. CatBoost - Wikipedia

    en.wikipedia.org/wiki/Catboost

    [11] along with TensorFlow, Pytorch, XGBoost and 8 other libraries. Kaggle listed CatBoost as one of the most frequently used machine learning (ML) frameworks in the world. It was listed as the top-8 most frequently used ML framework in the 2020 survey [12] and as the top-7 most frequently used ML framework in the 2021 survey. [13]

  8. CuPy - Wikipedia

    en.wikipedia.org/wiki/CuPy

    CuPy is a part of the NumPy ecosystem array libraries [7] and is widely adopted to utilize GPU with Python, [8] especially in high-performance computing environments such as Summit, [9] Perlmutter, [10] EULER, [11] and ABCI. [12] CuPy is a NumFOCUS sponsored project. [13]

  9. PyTorch Lightning - Wikipedia

    en.wikipedia.org/wiki/PyTorch_Lightning

    PyTorch Lightning is an open-source Python library that provides a high-level interface for PyTorch, a popular deep learning framework. [1] It is a lightweight and high-performance framework that organizes PyTorch code to decouple research from engineering, thus making deep learning experiments easier to read and reproduce.