README.md

DL Pipeline

Introduction

Brief tutorial on developing a pipeline for deploying deep learning capabilities onto AWS. Suggestions are welcome - this is a work in progress. The steps which I'll go through are as follows:

• Deploying deep learning algorithm on EC2
• Setting up AWS lambda to read from and write to S3

It's assumed that you have set up your VPCs, security groups, IAM roles, and environment in general. A CloudFormation script is also provided in the cf directory. If using that, please follow the instructions provided in that directory.

Setting up EC2

I started with the Ubuntu 16.04 LTS (HVM) AMI running on a t2.xlarge (probably overkill for my example), and the size will depend on the demands of your model. For exposing the web service, I used Apache Web Server with mod_wsgi and Flask - thanks to Data Science Bytes for their tutorial on this. Although you can follow their instructions, I include the steps that I followed here to get things installed for completeness:

$ sudo apt-get -y update
$ sudo apt-get -y install apache2 libapache2-mod-wsgi
$ sudo apt-get -y install python python-pip
$ sudo pip install flask

To allow for dynamic content onto the page, add the following lines to /etc/apache2/sites-enabled/000-default.conf after DocumentRoot /var/www/html:

        WSGIDaemonProcess flaskapp threads=1
        WSGIScriptAlias / /var/www/html/flaskapp/flaskapp.wsgi

        <Directory flaskapp>
            WSGIProcessGroup flaskapp
            WSGIApplicationGroup %{GLOBAL}
            Order deny,allow
            Allow from all
        </Directory>

flaskapp can be replaced with whatever you'd like to call your application. In the /var/www/html area, create the flaskapp directory, which is where your python code will eventually go. You'll need to add the flaskapp.wsgi file containing the following:

import sys
sys.path.insert(0,'/var/www/html/flaskapp')
from flaskapp import app as application

Train your favorite Deep Learning algorithm

Begin with your favorite deep learning algorithm. For starters, I borrowed one from a JoostWare. Whereas I'm a big advocate of PyTorch, it would mysteriously hang on me when deployed with Apache with mod_wsgi (works fine locally with Flask alone). Because of that, I created a Keras version as well. I'm including both in case someone could shed insights as to why the PyTorch one hangs. The versions I'm using are PyTorch 0.2.0.1, Tensorflow 1.2.1, and Keras 2.0.6.

Install Pandas, Tensorflow, Keras (and optionally PyTorch) with the following commands:

$ sudo pip install pandas tensorflow keras h5py
$ pip install http://download.pytorch.org/whl/cu75/torch-0.2.0.post1-cp27-cp27mu-manylinux1_x86_64.whl
$ pip install torchvision

Each model takes about ~15 min to train on a t2.xlarge (~3 min on a p2.xlarge) and achieves ~93-97% accuracy on the test set. To generate models, go into the train/ directory and run the following for Tensorflow:

$ python tf_name_test.py

which will create tf_charcnn.h5 or for PyTorch:

$ python name_test.py

which will create charcnn.pth.

Deploying your Deep Learning algorithm onto EC2

Copy all the files from flaskapp/ and the tf_charcnn.h5 model into /var/www/html/flaskapp (and charcnn.pth if you're testing out the PyTorch version). flaskapp.py contains the web service calls, which can be modified as necessary. In its present state, it contains the following:

from flask import Flask, render_template
import namecnn
import tf_predict
from tools import *

app = Flask(__name__)

@app.route('/name/<input_str>')
def predict(input_str):
    return namecnn.predict(input_str)

@app.route('/name_tf/<input_str>')
def predict_tf(input_str):
    return tf_predict.predict(input_str)

if __name__ == "__main__":
    app.run()

Then run the following command to start your web server:

$ apachectl restart

and your web server should be live. Look at /var/log/apache2/error.log should something require debugging. At this point, you should be able to test your code (assuming you can get past the security group) by using your EC2 external DNS hostname (e.g. http://ec2-XXX-XXX-XXX-XXX.compute-1.amazonaws.com/name_tf/sanchez) and substuting "sanchez" for the name of your choice.

Setting up Lambda

I'm assuming you are familiar with creating a Lambda function. For my example, I created one based on the s3-get-object-python blueprint. Note, you will also need to create an S3 bucket for this example. Also if you are working within a VPC, be sure to create a VPC endpoint so that you can communicate with S3 (the reason explained here). I used the "ObjectCreated" trigger, adding a prefix condition of name_ and a suffix of .txt to control kicking off the Lambda function (particularly since I was adding the results back into the same S3 bucket). Much of the boiler plate exists in the blueprint, but my final Lambda script looks as follows:

from __future__ import print_function

import json
import urllib
import boto3

print('Loading function')
s3 = boto3.client('s3')

def lambda_handler(event, context):
    bucket = event['Records'][0]['s3']['bucket']['name']
    key = urllib.unquote_plus(event['Records'][0]['s3']['object']['key'].encode('utf8'))
    try:
        response = s3.get_object(Bucket=bucket, Key=key)
        name = response['Body'].read().strip()
        nationality = urllib.urlopen("http://ec2-XXX-XXX-XXX-XXX.compute-1.amazonaws.com/name_tf/"+name).read()
        s3.put_object(Bucket=bucket, Key='out_'+key, Body=nationality)
        return nationality
    except Exception as e:
        print(e)
        print('Error getting object {} from bucket {}. Make sure they exist and your bucket is in the same region as this function.'.format(key, bucket))
        raise e

When you add a file into your S3 bucket called name_X.txt with a name in it, it will return a file called outname_X.txt with a guess to the nationality.