Introduction to LSTMs with TensorFlow

In this tutorial, we will build a Long Short Term Memory (LSTM) Network to predict the stock market sentiment based on a comments about the market from "StockTwits.com".

This repository contains source code corresponding to our article "Introduction to LSTMs with TensorFlow".

Setup

Download via Git

Go to your home directory by opening your terminal and entering cd ~

Clone the repository by entering

git clone https://github.com/dmonn/lstm-oreilly.git

Option 1: Dockerfiles (Recommended)

After cloning the repo to your machine, enter
```
docker build -t lstm_<image_type> -f ./dockerfiles/Dockerfile.<image_type> ./dockerfiles/
```
where <image_type> is either gpu or cpu. (Note that, in order to run these files on your GPU, you'll need to have a compatible GPU, with drivers installed and configured properly as described in TensorFlow's documentation.)
Run the Docker image by entering
```
docker run -it -p 8888:8888 -v <path to repo>:/root lstm_<image_type>
```
where <image_type> is either gpu or cpu, depending on the image you built in the last step.
After building, starting, and attaching to the appropriate Docker container, run the provided Jupyter notebooks by entering
```
jupyter notebook --ip 0.0.0.0 --allow-root
```
and navigate to the specified URL http://0.0.0.0:8888/?token=<JUPYTER NOTEBOOK ACCESS TOKEN> in your browser.
Choose Stock Market Sentiment with LSTMs and TensorFlow.ipynb to open the Notebook.

Debugging docker

If you receive an error of the form:

WARNING: Error loading config file:/home/rp/.docker/config.json - stat /home/rp/.docker/config.json: permission denied
Got permission denied while trying to connect to the Docker daemon socket at unix:///var/run/docker.sock: Get http://%2Fvar%2Frun%2Fdocker.sock/v1.26/images/json: dial unix /var/run/docker.sock: connect: permission denied

It's most likely because you installed Docker using sudo permissions with a packet manager such as brew or apt-get. To solve this permission denied simply run docker with sudo (ie. run docker commands with sudo docker <command and options> instead of just docker <command and options>).

Option 2: Local setup using Miniconda

If you don't have or don't want to use Docker, you can follow these steps to setup the notebook.

Install miniconda using one of the installers and the miniconda installation instructions. Use Python3.6.
After the installation, create a new virtual environment, using this command.
```
$ conda create -n lstm
$ source activate venv
```
You are now in a virtual environment. Next up, install TensorFlow by following the instructions.
To install the rest of the dependenies, navigate into your repository and run
```
$ pip install -r dockerfiles/requirements.txt
```
Now you can run
```
jupyter notebook
```
to finally start up the notebook. A browser should open automatically. If not, navigate to http://127.0.0.1:8888 in your browser.
Choose Stock Market Sentiment with LSTMs and TensorFlow.ipynb to open the Notebook.

Notes

The checkpoints/ directory files with the saved model was not uploaded to github due to size constraints. If you run the code in your docker container or Miniconda virtual environtment, the model will rerun and save at that time.