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.. fairscale documentation master file, created by
   sphinx-quickstart on Tue Sep  8 16:19:17 2020.
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   You can adapt this file completely to your liking,
   but it should at least contain the root `toctree`
   directive.
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Welcome to fairscale's documentation!
=====================================

.. toctree::
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   :maxdepth: 3
   :caption: Contents:
   :hidden:
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   tutorials/index
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   api/index
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*fairscale* is a PyTorch extension library for high performance and
large scale training for optimizing training on one or across multiple
machines/nodes. This library extend basic pytorch capabilities while
adding new experimental ones.
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Components
----------

* Parallelism:
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   * `Pipeline parallelism <../../en/latest/api/nn/pipe.html>`_
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* Sharded training:
    * `Optimizer state sharding <../../en/latest/api/optim/oss.html>`_
    * `Sharded grad scaler - automatic mixed precision <../../en/latest/api/optim/grad_scaler.html>`_
    * `Sharded distributed data parallel <../../en/latest/api/nn/sharded_ddp.html>`_

* Optimization at scale:
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   * `AdaScale SGD <../../en/latest/api/optim/adascale.html>`_
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* `Tutorials <../../en/latest/tutorials/index.html>`_


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.. warning::
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    This library is under active development.
    Please be mindful and create an
    `issue <https://github.com/facebookresearch/fairscale/issues>`_
    if you have any trouble and/or suggestion.
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Reference
=========

:ref:`genindex` | :ref:`modindex` | :ref:`search`