Python Cloud Foundry Examples

License: See LICENSE.txt in each example code folder

First presented at PyData London 2015.

This tutorial is an introduction to deploying Python applications to Cloud Foundry and covers:

Pushing your first application to Cloud Foundry
Using a custom buildpack to use PyData packages in your app
Adding data services like Redis to your app

Each example app is self-contained and can be deployed to a Cloud Foundry instance with

$ cf push

The slides are available on Speakerdeck.

A shorter version of these notes, which can be used by assistants in the tutorial is also provided.

Getting Started

If you do not have an account on a Cloud Foundry installation you can register for a free trial at Pivotal Web Services (PWS).

Download the Cloud Foundry Command Line Interface from the CF management console or the CF Github repo. This provides the cf command which you will use to interact with a CF installation.

Target the CF installation API with cf api. For example for PWS:

$ cf api https://api.run.pivotal.io

Pushing your first app

The first app is in the folder 01-simple-python-app. Have a look at the code and then deploy using cf push.

$ cd 01-simple-python-app
$ cf push

You can inspect the app's streaming logs using cf logs myapp.

You can scale your app by changing the number of instances and the available RAM:

$ cf scale myapp -i 5
$ cf scale myapp -m 256M

Pushing an app with PyData dependencies

The second app is in the folder 02-pydata-spyre-app. This app is built using Spyre by Adam Hajari.

This application uses the Python Conda Buildpack to provision dependencies using conda. This buildpack is specified in the manifest.yml file.

The environment.yml file contains the conda environment specification.

Push the app using cf push and visit the app URL to see a simple interactive visualisation.

$ cd ../02-pydata-spyre-app
$ cf push

Using data services

The third app is in the folder 03-services-redis.

Services provide persistent storage and other useful resources for your applications.

First, create a free Redis service provided by Rediscloud:

$ cf create-service rediscloud 30mb myredis

You can bind this to your apps making it available via the VCAP_SERVICES environmental variable.

$ cf bind-service myapp myredis

Look at the environmental variables your app has with cf env myapp.

Now you can push the third app which uses Redis as its backing store.

$ cd ../03-services-redis
$ cf push

The manifest specifies that this app should be bound to the myredis service instance that you just created.

Putting it all together

The fourth app is in the folder 04-learning-api.

We can put everything we've learned into practice by building our own simple machine learning API.

$ cd ../04-learning-api
$ cf push

This is a simplified version of a project by Alex Kagoshima.

Don't forget to stop all your apps when you are finished by doing the following:

$ cf stop myapp
$ cf stop sinewave
$ cf stop testredis
$ cf stop learning-api