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README.md

A simple production setup with Keras

After you've figured out how to model a classification problem in Keras and put it to use, how to use it in production? There are sophisticated ways to do this with clusters, but sometimes you need just something basic. This project provides a simple setup for training a neural network, and exposing it as a REST API.

Dependencies

Make sure you have Python 3+ with Keras and a backend like Tensorflow. In addition to that, you'll need Flask.

When installing TensorFlow, using an optimized version can improve performance, see this repo's issues for builds. You may also need the native protobuf package.

Train

$ python3 train.py
Epoch  1/12 11228/11228 [==============================] - 1s - loss: 1.6777 - acc: 0.6348      
Epoch  2/12 11228/11228 [==============================] - 1s - loss: 0.9494 - acc: 0.7829     
Epoch  3/12 11228/11228 [==============================] - 1s - loss: 0.7223 - acc: 0.8319     
Epoch  4/12 11228/11228 [==============================] - 1s - loss: 0.5781 - acc: 0.8636     
Epoch  5/12 11228/11228 [==============================] - 1s - loss: 0.4809 - acc: 0.8836     
Epoch  6/12 11228/11228 [==============================] - 1s - loss: 0.4068 - acc: 0.8991     
Epoch  7/12 11228/11228 [==============================] - 1s - loss: 0.3599 - acc: 0.9084     
Epoch  8/12 11228/11228 [==============================] - 1s - loss: 0.3183 - acc: 0.9182     
Epoch  9/12 11228/11228 [==============================] - 1s - loss: 0.2814 - acc: 0.9271     
Epoch 10/12 11228/11228 [==============================] - 1s - loss: 0.2602 - acc: 0.9298     
Epoch 11/12 11228/11228 [==============================] - 1s - loss: 0.2385 - acc: 0.9347     
Epoch 12/12 11228/11228 [==============================] - 1s - loss: 0.2229 - acc: 0.9386

This results in several files in model/ with the result of the training.

Test

$ python3 test.py
Input: {"text": "local chamber of commerce takes action on legislation"}
Features: ["on", "action", "local", "commerce", "legislation"]
Prediction: 3
Input: {"text": "consumption of food is estimated to have increased twofold in the past year"}
Features: ["to", "in", "the", "is", "year", "have", "increased", "estimated", "past", "food", "consumption"]
Prediction: 13
Input: {"text": "banking company offers settlement in long running financial case"}
Features: ["in", "company", "financial", "long", "banking", "offers", "case"]
Prediction: 1
Input: {"text": "negotiations on foreign affairs between china and australia enter into a new phase"}
Features: ["and", "a", "on", "new", "foreign", "between", "into", "china", "negotiations", "australia"]
Prediction: 28

It is unknown what the predicted classes actually mean, but it serves as an example.

REST API

To use predictions in an separate system, a REST API can be useful.

$ python3 api.py
 * Running on http://127.0.0.1:5000/ (Press CTRL+C to quit)

Then visit, for example http://127.0.0.1:5000/api/v1/predict?text=and+the+result+is to see the output:

{
  "result": 3
}

Running on Amazon EC2

Launch an AWS Deep Learning AMI Ubuntu image on EC2 (as explained here). For classification, 1 CPU with 2GB of memory will suffice (t2.small - let's see how it goes). Give it a public IP address with the FQDN DNS entry. Open port 80 and 4002.

FQDN=predictor.example.com
USERNAME=predictor
PASSWORD=s3cret

# Install dependencies
apt-get install -y python3-flask uwsgi-core uwsgi-plugin-python3

# Setup service
usermod ubuntu -G www-data

touch /etc/default/predictor
chmod 0600 /etc/default/predictor
cat >/etc/systemd/system/predictor.service <<EOF
[Unit]
Description=Predictor service
After=network.target

[Service]
EnvironmentFile=/etc/default/predictor
WorkingDirectory=/home/ubuntu/app
# login shell because important variables are set in ~/.bashrc
ExecStart=/bin/bash -l -c '/usr/bin/uwsgi -s ~/uwsgi.sock --chmod=660 --chown-sock=ubuntu:www-data --manage-script-name --mount /=api:app --plugin python3'
PIDFile=/home/ubuntu/uwsgi.pid
KillSignal=SIGQUIT
Restart=always
User=ubuntu
Type=notify
NotifyAccess=all

[Install]
WantedBy=multi-user.target
EOF

# Setup webserver front-end
# @todo figuring out how to bootstrap certificates
apt-get install -y nginx-core apache2-utils
service nginx stop
cat >/etc/nginx/sites-enabled/default <<EOF
server {
  listen 4002 default_server;
  ssl on;
  ssl_session_timeout 5m;
  ssl_protocols TLSv1.2;
  ssl_ciphers "HIGH:!aNULL:!MD5 or HIGH:!aNULL:!MD5:!3DES";
  ssl_prefer_server_ciphers on;
  ssl_certificate /etc/letsencrypt/live/${FQDN}/fullchain.pem;
  ssl_certificate_key /etc/letsencrypt/live/${FQDN}/privkey.pem;

  location / {
    uwsgi_pass unix:/home/ubuntu/uwsgi.sock;
    include /etc/nginx/uwsgi_params;
    auth_basic "Restricted";
    auth_basic_user_file /etc/nginx/htpasswd;
  }
}
EOF
htpasswd -b -c /etc/nginx/htpasswd "$USERNAME" "$PASSWORD"
chown :www-data /etc/nginx/htpasswd
chmod 0640 /etc/nginx/htpasswd

# Letsencrypt
apt-get -y install software-properties-common
apt-add-repository -y ppa:certbot/certbot
apt-get update
apt-get -y install python-certbot-nginx

cat >/etc/nginx/sites-enabled/http <<EOF
server {
  listen 80;
  server_name $FQDN;
  root /var/www/html;

  location / {
    return 302 http://www.example.com/;
  }

  # keep this for letsencrypt
  location /.well-known/ {
  }
}
EOF

# get first-time certificate, after which we can start nginx
certbot certonly --preferred-challenges http --standalone -d "$FQDN"
cat >>/etc/crontab <<EOF
# letsencrypt update check daily
23 4 * * * /usr/bin/certbot renew --deploy-hook "service nginx reload"
EOF

# and get going
service nginx start
service predictor start

Before starting the predictor, however, make sure to put the code in /home/ubuntu/app/.

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