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PyTorch is not a Python binding into a monolithic C++ framework.
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It is built to be deeply integrated into Python.
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You can use it naturally like you would use NumPy / SciPy / scikit-learn etc.
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You can use it naturally like you would use [NumPy](http://www.numpy.org/) / [SciPy](https://www.scipy.org/) / [scikit-learn](http://scikit-learn.org) etc.
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You can write your new neural network layers in Python itself, using your favorite libraries
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and use packages such as Cython and Numba.
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Our goal is to not reinvent the wheel where appropriate.
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### Fast and Lean
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PyTorch has minimal framework overhead. We integrate acceleration libraries
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such as Intel MKL and NVIDIA (cuDNN, NCCL) to maximize speed.
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such as [Intel MKL](https://software.intel.com/mkl) and NVIDIA (cuDNN, NCCL) to maximize speed.
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At the core, its CPU and GPU Tensor and neural network backends
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(TH, THC, THNN, THCUNN) are mature and have been tested for years.
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### Building the Documentation
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To build documentation in various formats, you will need Sphinx and the
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To build documentation in various formats, you will need [Sphinx](http://www.sphinx-doc.org) and the
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