All rights reserved.

Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:

1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.

2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.

3. Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission.

THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

This is a repo is designed to hold PyTorch modules and utilities that are under active development and experimental. This repo is not designed as a long term solution or a production solution. Things placed in here are intended to be eventually moved to upstream PyTorch.

Python 3

PyTorch 0.3 or newer

CUDA 9

To build the extension run the following command in the root directory of this project

To use the extension simply run

and optionally (if required for your use)

Current version of apex contains:

1. Mixed precision utilities can be found [here](https://nvidia.github.io/apex/fp16_utils) examples of using mixed precision utilities can be found for the [PyTorch imagenet example](https://github.com/csarofeen/examples/tree/apex/imagenet) and the [PyTorch word language model example](https://github.com/csarofeen/examples/tree/apex/word_language_model).

2. Parallel utilities can be found [here](https://nvidia.github.io/apex/parallel) and an example/walkthrough can be found [here](https://github.com/csarofeen/examples/tree/apex/distributed)

- apex/parallel/distributed.py contains a simplified implementation of PyTorch's DistributedDataParallel that's optimized for use with NCCL in single gpu / process mode

- apex/parallel/multiproc.py is a simple multi-process launcher that can be used on a single node/computer with multiple GPU's

3. Reparameterization function that allows you to recursively apply reparameterization to an entire module (including children modules).

4. An experimental and in development flexible RNN API.