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hamelsmu / Seq2Seq_Tutorial

Code For Medium Article "How To Create Data Products That Are Magical Using Sequence-to-Sequence Models"
Apache License 2.0
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data-science deep-learning deeplearning keras keras-tutorials machine-learning medium-article nlp-machine-learning rnn-encoder-decoder seq2seq-tutorial sequence-to-sequence

readme

GitHub license

Sequence-to-Sequence Tutorial with Github Issues Data

Code For Medium Article: "How To Create Data Products That Are Magical Using Sequence-to-Sequence Models"

Installation

pip install -r requirements.txt

If you are using the AWS Deep Learning Ubuntu AMI, many of the required dependencies will already be installed, so you only need to run:

source activate tensorflow_p36
pip install ktext annoy nltk pydot

See #4 below if you wish to run this tutorial using Docker.

Resources:

  1. Tutorial Notebook: The Jupyter notebook that coincides with the Medium post.

  2. seq2seq_utils.py: convenience functions that are used in the tutorial notebook to make predictions.

  3. ktext: this library is used in the tutorial to clean data. This library can be installed with pip.

  4. Nvidia Docker Container: contains all libraries that are required to run the tutorial. This container is built with Nvidia-Docker v1.0. You can install Nvidia-Docker and run this container like so:

curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey |   sudo apt-key add -
distribution=$(. /etc/os-release;echo $ID$VERSION_ID)
curl -s -L https://nvidia.github.io/nvidia-docker/$distribution/nvidia-docker.list |   sudo tee /etc/apt/sources.list.d/nvidia-docker.list
sudo apt-get update
sudo apt-get install nvidia-docker

sudo nvidia-docker run hamelsmu/seq2seq_tutorial

This should work with both Nvidia-Docker v1.0 and v2.0.