Metadata-Version: 1.2
Name: sockeye
Version: 1.8.3
Summary: Sequence-to-Sequence framework for Neural Machine Translation
Home-page: https://github.com/awslabs/sockeye
Author: Amazon
Author-email: sockeye-dev@amazon.com
License: Apache License 2.0
Description: # Sockeye
        
        [![Documentation Status](https://readthedocs.org/projects/sockeye/badge/?version=latest)](http://sockeye.readthedocs.io/en/latest/?badge=latest) [![Build Status](https://travis-ci.org/awslabs/sockeye.svg?branch=master)](https://travis-ci.org/awslabs/sockeye)
        
        This package contains the Sockeye project,
        a sequence-to-sequence framework for Neural Machine Translation based on Apache MXNet Incubating.
        It implements state-of-the-art encoder-decoder architectures, such as 
        - Deep Recurrent Neural Networks with Attention [[Bahdanau, '14](https://arxiv.org/abs/1409.0473)]
        - Transformer Models with self-attention [[Vaswani et al, '17](https://arxiv.org/abs/1706.03762)]
        - Fully convolutional sequence-to-sequence models [[Gehring et al, '17](https://arxiv.org/abs/1705.03122)]
        
        If you are interested in collaborating or have any questions, please submit a pull request or issue.
        You can also send questions to *sockeye-dev-at-amazon-dot-com*.
        
        Recent developments and changes are tracked in our [changelog](https://github.com/awslabs/sockeye/blob/master/CHANGELOG.md).
         
        ## Dependencies
        
        Sockeye requires:
        - **Python3**
        - [MXNet-0.10.0](https://github.com/dmlc/mxnet/tree/v0.10.0)
        - numpy
        
        ## Installation
        
        There are several options for installing Sockeye and it's dependencies. Below we list several alternatives and the
        corresponding instructions.
        
        ### Either: AWS DeepLearning AMI
        
        [AWS DeepLearning AMI](https://aws.amazon.com/amazon-ai/amis/) users only need to run the following line to install sockeye:
        
        ```bash
        > sudo pip3 install sockeye --no-deps
        ```
        
        For other environments, you can choose between installing via pip or directly from source. Note that for the
        remaining instructions to work you will need to use `python3` instead of `python` and `pip3` instead of `pip`.
        
        
        ### Or: pip package
        
        #### CPU
        
        ```bash
        > pip install sockeye
        ```
        
        #### GPU
        
        If you want to run sockeye on a GPU you need to make sure your version of Apache MXNet Incubating contains the GPU
        bindings.
        Depending on your version of CUDA you can do this by running the following for CUDA 8.0:
        
        ```bash
        > wget https://raw.githubusercontent.com/awslabs/sockeye/master/requirements.gpu-cu80.txt
        > pip install sockeye --no-deps -r requirements.gpu-cu80.txt
        > rm requirements.gpu-cu80.txt
        ```
        or the following for CUDA 7.5:
        ```bash
        > wget https://raw.githubusercontent.com/awslabs/sockeye/master/requirements.gpu-cu75.txt
        > pip install sockeye --no-deps -r requirements.gpu-cu75.txt
        > rm requirements.gpu-cu75.txt
        ```
        
        ### Or: From Source
        
        #### CPU
        
        If you want to just use sockeye without extending it, simply install it via
        ```bash
        > python setup.py install
        ```
        after cloning the repository from git.
        
        #### GPU
        
        If you want to run sockeye on a GPU you need to make sure your version of Apache MXNet
        Incubating contains the GPU bindings. Depending on your version of CUDA you can do this by
        running the following for CUDA 8.0:
        
        ```bash
        > python setup.py install -r requirements.gpu-cu80.txt
        ```
        or the following for CUDA 7.5:
        ```bash
        > python setup.py install -r requirements.gpu-cu75.txt
        ```
        
        ### Optional dependencies
        In order to track learning curves during training you can optionally install dmlc's tensorboard fork
         (````pip install tensorboard````).
        If you want to create alignment plots you will need to install matplotlib (````pip install matplotlib````).
        
        In general you can install all optional dependencies from the Sockeye source folder using:
        ```bash
        > pip install -e '.[optional]'
        ```
        
        
        ### Running sockeye
        
        After installation, command line tools such as *sockeye-train, sockeye-translate, sockeye-average* 
        and *sockeye-embeddings* are available. Alternatively, if the sockeye directory is on your
        PYTHONPATH you can run the modules 
        directly. For example *sockeye-train* can also be invoked as
        ```bash
        > python -m sockeye.train <args>
        ```
        
        ## First Steps
        
        ### Train
        
        In order to train your first Neural Machine Translation model you will need two sets of parallel files: one for training 
        and one for validation. The latter will be used for computing various metrics during training. 
        Each set should consist of two files: one with source sentences and one with target sentences (translations). Both files should have the same number of lines, each line containing a single
        sentence. Each sentence should be a whitespace delimited list of tokens.
        
        Say you wanted to train a RNN German-to-English translation model, then you would call sockeye like this:
        ```bash
        > python -m sockeye.train --source sentences.de \
                               --target sentences.en \
                               --validation-source sentences.dev.de \
                               --validation-target sentences.dev.en \
                               --use-cpu \
                               --output <model_dir>
        ```
        
        After training the directory *<model_dir>* will contain all model artifacts such as parameters and model 
        configuration. 
        
        
        ### Translate
        
        Input data for translation should be in the same format as the training data (tokenization, preprocessing scheme).
        You can translate as follows: 
         
        ```bash
        > python -m sockeye.translate --models <model_dir> --use-cpu
        ```
        
        This will take the best set of parameters found during training and then translate strings from STDIN and 
        write translations to STDOUT.
        
        For more detailed examples check out our user documentation.
        
        
        ## Step-by-step tutorial
        
        More detailed step-by-step tutorials can be found in the
        [tutorials directory](https://github.com/awslabs/sockeye/tree/master/tutorials).
        
Platform: UNKNOWN
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Programming Language :: Python :: 3 :: Only
Requires-Python: >=3
