Deploy Flask apps using Anaconda on Ubuntu Server

You're a data scientist.

You've built an elaborate ensamble model that consists of various machine learning algorithms using scipy, scikit-learn, xgboost.

Now you need to make it available to others for testing.

A simple RESTful API seems to be a good way to expose the functionality. Flask is a popular and lightweight web framework so you decide to go with that. So you make a simple web app with a couple of endpoints and it works on your machine.

But how do you deploy it?

Development

We are going to need this system level package.

sudo apt install build-essential -y

Ever since you made a mess using sudo pip install that caused you to reinstall your Ubuntu, you're using Anaconda because it can install packages in your home directory and everything always seems to work.

Here's how to download & install Anaconda into your home folder.

wget https://repo.continuum.io/archive/Anaconda2-4.1.1-linux-x86_64.sh bash Anaconda2-4.1.1-Linux-x86_64.sh -b -p $HOME/anaconda2 rm Anaconda* echo 'export PATH="$PATH:$HOME/anaconda2/bin"' >> .bashrc bash conda --version

Next, you create a new environment for your project and install all dependencies.

conda create -n app python -y source activate app conda install scipy numpy scikit-learn flask -y pip install xgboost uwsgi

Create your web app that loads your model and does the work. In this tutorial, I'm just returning Hello world! .

mkdir app/ cd app/ cat > app.py <<EOF import scipy, numpy, sklearn, xgboost from flask import Flask app = Flask(__name__) @app.route('/') def hello_world(): return 'Hello world!\n' if __name__ == '__main__': app.run() EOF

Make sure that it runs

$ python app.py $ curl localhost:5000 Hello world!

If you read the docs, you'll see a bolded sentence that the Flask's built-in server is not suitable for production.

So let's use uWSGI instead.

$ uwsgi --http localhost:8080 --wsgi-file app.py --callable app $ curl localhost:8080 Hello world!

Great. But we can do better:

cat > uwsgi.ini << EOF [uwsgi] http = localhost:8080 wsgi-file = app.py callable = app EOF

Instead of typing all command line arguments or copy&pasting them all the time, we store then in a configuration file.

There are a lot of things you can tune with this file (the number of processes running at the same time, running user and group IDs, etc.) that I am not going to go into here.

$ uwsgi --ini uwsgi.ini $ curl localhost:8080 Hello world!

Sweet!

The final step is to save a list of all the packages and their versions that you are using so that we can replicate this environment later.

conda env export > environment.yml

It'll look something like

name: app dependencies: - click=6.6=py27_0 - flask=0.11.1=py27_0 - itsdangerous=0.24=py27_0 - jinja2=2.8=py27_1 - libgfortran=3.0.0=1 - markupsafe=0.23=py27_2 - mkl=11.3.3=0 - numpy=1.11.1=py27_0 - openssl=1.0.2j=0 - pip=8.1.2=py27_0 - python=2.7.12=1 - readline=6.2=2 - scikit-learn=0.17.1=np111py27_2 - scipy=0.18.1=np111py27_0 - setuptools=27.2.0=py27_0 - sqlite=3.13.0=0 - tk=8.5.18=0 - werkzeug=0.11.11=py27_0 - wheel=0.29.0=py27_0 - zlib=1.2.8=3 - pip: - uwsgi==2.0.13.1 - xgboost==0.6a2 prefix: /home/ubuntu/anaconda2/envs/app

You can exit the environment with

source deactivate

Deployment

As before, we are going to need this system level package. Or packages installed by pip may not be built properly.

sudo apt install build-essential -y

Since we're deploying on a potentially shared server, we're not going to use our $HOME directory.

Let's use /opt instead.

For convenience, let's give ourselves write permissions to /opt .

sudo mkdir -p /opt sudo chown $USER /opt

Download & install Anaconda into /opt/anaconda .

wget https://repo.continuum.io/archive/Anaconda2-4.1.1-Linux-x86_64.sh bash Anaconda2-4.1.1-Linux-x86_64.sh -b -p /opt/anaconda rm Anaconda*

For convenience, add Anaconda to $PATH for this terminal session.

export PATH=/opt/anaconda/bin:$PATH conda --version

We're going to store our web app code and models in /opt/apps/app .

mkdir -p /opt/apps/app

Recreate the environment for our app. Then test that it works.

conda env create -f /opt/apps/app/environment.yml -n app source activate app cd /opt/apps/app uwsgi --ini uwsgi.ini curl localhost:8080 source deactivate

We want to make sure that our app is started at boot if the server gets rebooted for any reason.

sudo tee /etc/systemd/system/app.service << EOF [Unit] Description=uWSGI instance to serve app [Service] ExecStart=/bin/bash -c 'cd /opt/apps/app && source /opt/anaconda/bin/activate app && uwsgi --ini uwsgi.ini' [Install] WantedBy=multi-user.target EOF sudo systemctl start app.service

As always, make sure that it's working:

$ curl localhost:8080 Hello world!

One last optional step is to add a reverse web proxy server in front of your app. It can be useful if you don't want to use port 80 and not run as root or if you are going to host several apps on your server under the same domain.

export DOMAIN=domain.com

Install & configure nginx, a proper web server.

sudo apt install -y nginx sudo tee /etc/nginx/sites-available/$DOMAIN <<EOF server { listen 80; server_name $DOMAIN www.$DOMAIN; location / { proxy_pass http://localhost:8080; } } EOF sudo ln -s /etc/nginx/sites-available/$DOMAIN /etc/nginx/sites-enabled/$DOMAIN sudo systemctl restart nginx

Test that it works:

$ curl $DOMAIN Hello world!

Voila!

Docker

Did that seem messy or a lot of work?

There is another way.

You could set everything up in a virtual machine and give it to someone to deploy. But virtual machines have a lot of overhead (large image sizes as well as CPU/memory overhead).

Or you could use containers which are isolated like virtual machines but virtually no overhead.

Install Docker

sudo apt-key adv --keyserver hkp://p80.pool.sks-keyservers.net:80 --recv-keys 58118E89F3A912897C070ADBF76221572C52609D echo "deb https://apt.dockerproject.org/repo ubuntu-xenial main" | sudo tee /etc/apt/sources.list.d/docker.list sudo apt update sudo apt install docker-engine docker --version

There are two ways to do this. One is to create a script that will build a Docker image that has everything installed. The other way is to work inside the container and then just ship it for deployment.

We're going to take a look at the second option.

Create a new Docker container based on Ubuntu 16.04 and go into it.

apt update apt install python python-pip -y pip install scipy numpy scikit-learn flask xgboost uwsgi

You could still install Anaconda inside the container, this time I didn't and just installed pip packages globally.

To be continued...