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README.md

Neuronaming

License: MIT Commitizen friendly Build Status

Table of Contents

About

ML powered business names generator on Torch with Flask web server and UI. Proof of concept.

3 million active UK companies data points were used to train the neural network. Industry classification is based on UK Standard industrial classification of economic activities (SIC). For some industries better results are produced because of a larger amount of companies analysed.

Based on Torch LSTM implementation: torch-rnn by Justin Johnson. See Wiki for technical in-depth on the models.

Pre-trained model checkpoints included as separate download (See here).

All training data is available in Contribution guide.

Installation

Debian / Ubuntu (14.04/16.04). CPU-only.

Keep in mind, that initial version was create in 2015, so there could be outdates dependencies and such. Please contribute to update the code.

Step 1. Python and virtualenv

Install sudo and git if it's not on the system yet:

apt-get install sudo
sudo apt-get install git-core

Also nano can be a good starter choice for a file editor (used in this guide): sudo apt-get install nano


  1. Clone this repo:
git clone https://github.com/vladzima/neuronaming-dev
cd neuronaming-dev
sudo apt-get -y install python2.7-dev
sudo apt-get install libhdf5-dev

Debian: apt-get install python-h5py

  1. Add user neuronaming and include in sudoers:
adduser neuronaming
usermod -aG sudo neuronaming
  1. Login as new user:
su - neuronaming
cd torch-rnn
  1. Open nano ~/.bashrc and add:
export WORKON_HOME=$HOME/.virtualenvs
export PROJECT_HOME=$HOME/devel/python
export VIRTUALENVWRAPPER_PYTHON=/usr/bin/python
source ~/.local/bin/virtualenvwrapper.sh
  1. Re-login and create virtual env:
su - neuronaming
mkvirtualenv -p python2 neuronaming
  1. Install deps:
cd ~/torch-rnn
pip install -r requirements.txt

In case h5py installation fails, open requirements.txt and remove version number: h5py==2.5.0 > h5py

Step 2. Torch and luarocks

(Manual: http://torch.ch/docs/getting-started.html)

  1. In short:
git clone https://github.com/torch/distro.git ~/torch
cd ~/torch; bash install-deps;

# Takes forever!
  1. Works better with lua53 instead of standard luajit:
TORCH_LUA_VERSION=LUA53  ./install.sh

# Answer yes about .bashrc
  1. Latest lua-cjson fails so we need a fixed version:
su - neuronaming
luarocks install torch
luarocks install nn
luarocks install optim
luarocks install lua-cjson 2.1.0

# Takes time!

Attention! You always need to initiate th from the same directory where torch-rnn/LanguageModel.lua is. The easiest way is to add cd ABSOLUTE_PATH/torch-rnn before exec to torch/install/bin/th.

Testing

  1. Copy and extract pre-trained model checkpoints directory:
cd ~/torch-rnn
wget https://storage.googleapis.com/nnnet_storage/cv.cpgz
gzip -cd cv.cpgz | cpio -idmv
  1. Now test:
workon neuronaming
th /home/neuronaming/torch-rnn/sample.lua -checkpoint /home/neuronaming/cv/C/checkpoint.t7 -length 400 -gpu -1

Step 3. Nginx and wsgi

  1. Login as your root user and install nginx:
sudo apt-get update
sudo apt-get install nginx
  1. Install virtualenv и virtualenvwrapper again, as Flask likes Python3:
sudo apt-get install python3-pip
pip3 install virtualenv virtualenvwrapper
su - neuronaming
  1. Open nano ~/.bashrc and add:
export WORKON_HOME=$HOME/.virtualenvs
export VIRTUALENVWRAPPER_PYTHON=/usr/bin/python3
  1. Create new env-name:
su - neuronaming
mkvirtualenv env-name
pip3 install uwsgi flask

If ufw firewall is on: ufw allow 5000

  1. Create socket:
mkfifo /home/neuronaming/site/server.sock
  1. Create autostart script:
For Ubuntu 14.04
touch /etc/init/server.conf
nano /etc/init/server.conf

Copy in server.conf:

description "uWSGI instance to serve neuronaming project"
start on runlevel [2345]
stop on runlevel [!2345]

# Place actual nginx uid instead of 2345; lookup: "top b|less"

setuid www-data
setgid neuronaming
env PATH=/home/neuronaming/.virtualenvs/neuronaming/bin
chdir /home/neuronaming/site
exec uwsgi --ini server.ini
For Ubuntu 16.04
touch /etc/systemd/system/server.service
nano /etc/systemd/system/server.service

Copy in server.service:

[Unit]
Description=uWSGI instance to serve neuronaming project
After=network.target
[Service]
User=www-data
Group=neuronaming
WorkingDirectory=/home/neuronaming/site
Environment="PATH=/home/neuronaming/.virtualenvs/neuronaming/bin"
ExecStart=/home/neuronaming/.virtualenvs/neuronaming/bin/uwsgi --ini server.ini
[Install]
WantedBy=multi-user.target
  1. Start wsgi:
For Ubuntu 14.04
sudo start server
For Ubuntu 16.04
sudo systemctl start server
sudo systemctl enable server
  1. Open nginx conf: nano /etc/nginx/sites-available/default and copy:
server {
    listen 80 default_server;
    listen [::]:80 default_server ipv6only=on;
    root /home/neuronaming/site/static;
    index index.html;
    server_name DOMAIN;
    location / {
            try_files $uri $uri/ =404;
    }
    location /api01 {
        include uwsgi_params;
        uwsgi_pass unix:/home/neuronaming/site/server.sock;
    }
}

# Insert actual domain in place of DOMAIN
  1. Restart nginx:
For Ubuntu 14.04
sudo service nginx restart
For Ubuntu 16.04
sudo systemctl restart nginx

If ufw firewall is active:

ufw delete allow 5000
ufw allow 'Nginx Full'

Please check that the resulting project structure is relevant:

.
├── torch-rnn
│   └── cv
│       └── ...
└── site
    ├── static
    │   └── ...
    ├── server.ini
    ├── server.py
    ├── storage.dat
    └── wsgi.py

Customizing output

The sampling script sample.lua accepts command-line flags. You can utilize these:

  • -checkpoint: Path to a different .t7 checkpoint file (if you downloaded full trained data)
  • -length: The length of the generated text, in characters.
  • -start_text: You can optionally start off the generation process with a string; if this is provided the start text will be processed by the trained network before we start sampling. Without this flag, the first character is chosen randomly.
  • -sample: Set this to 1 to sample from the next-character distribution at each timestep; set to 0 to instead just pick the argmax at every timestep. Sampling tends to produce more interesting results.
  • -temperature: Softmax temperature to use when sampling; default is 1. Higher temperatures give noiser samples. Not used when using argmax sampling (sample set to 0).
  • -verbose: By default just the sampled text is printed to the console. Set this to 1 to also print some diagnostic information.

Note: GPU related flags mentioned in the Wiki are not applicable since we compiled torch without GPU support.

The sample request script looks like this:

th /home/neuronaming/torch-rnn/sample.lua -checkpoint /home/neuronaming/cv/C/checkpoint.t7 -length 400 -gpu -1

You can modify the following sampling code in server.py in case you want to change the output on the frontend:

result = check_output(['/home/neuronaming/torch/install/bin/th', 'sample.lua', '-checkpoint', 'cv/'+category+'/checkpoint.t7', '-length', '400', '-gpu', '-1'])

Custom models

To train your own model and use in to generate new text, please consult with the original torch-rnn manual: https://github.com/jcjohnson/torch-rnn#usage.

Contribute

See the Contribution guide. Please make sure to use conventional changelog, customized for this project.

Todo

[x] Explain different parameters in sampling

License

MIT License

Copyright (c) 2015 Neuronaming

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

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