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        "description": "# BxTorch\n\nBxTorch is a high-level library for large-scale machine learning in [PyTorch](https://pytorch.org).\nIt is engineered both to cut obsolete boilerplate code while preserving the flexibility of PyTorch to create just about any deep learning model.\n\n## Installation\n\nBxTorch is available on PyPi, so simply run the following command:\n\n```bash\npip install bxtorch\n```\n\nThe latest version of BxTorch is `1.0.0`. The package will install all required dependencies.\n\n## Features\n\nGenerally, BxTorch provides an object-oriented approach to abstracting\nPyTorch's API. The core design objective is to provide an API both as simple\nand as extensible as possible. The goal of this library is to be able to iterate between different models easily instead of squeezing out milliseconds\nwhere it is not required.\n\nStill, being focused on large-scale machine learning, BxTorch aims to make it\nas easy as possible working with large datasets. This includes out-of-the-box\nmulti-GPU support where the user *does not need to write a single line of\ncode*. Currently, BxTorch only provides means for running training/inference\non a single machine. In case this is insufficient, you might be better off\nusing PyTorch's `distributed` package directly.\n\nIt must be emphasized that BxTorch is not meant to be a wrapper for PyTorch as\nKeras is for TensorFlow - it only provides *extensions*.\n\n## Documentation\n\nExamples of the usage of BxTorch can be found in the [docs folder](docs).\nMethod documentation is currently only available as [docstrings](bxtorch).\n\n## License\n\nBxTorch is licensed under the [MIT License](LICENSE).\n\n\n",
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        "license": "License :: OSI Approved :: MIT License",
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