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Deploying Python for Data Analytics and Jupyter Notebook in the Cloud.

Jupyter Notebook 74.65% Shell 5.09% Python 9.91% CSS 3.29% HTML 7.06%

cloud-python's Introduction

Interactive Python in the Cloud

This repository contains a few small files that allow to easily deploy Python (cf. http://python.org) and Jupyter Notebook (cf. http://jupyter.org) in the cloud.

It all works (apart from the parallel computation example) even on the smallest DigitalOcean droplet (cf. https://www.digitalocean.com/?refcode=fbe512dd3dac).

When setting up such a droplet it is recommended to use the latest version of Ubuntu.

I assume that you have cloned the repository to your local machine (Linux or Mac):

git clone --depth=1 https://github.com/yhilpisch/cloud-python

DigitalOcean Droplet

If you do not have a DigitalOcean account yet, generate one here

https://www.digitalocean.com/?refcode=fbe512dd3dac

You will start with 10 USD worth of compute power (= e.g. 2 monthly fees for the smallest droplet).

Now create a droplet giving it a name like "cloud-python" and chosing the size, location and operating system (e.g. Ubuntu 14.04).

I recommend to post a public key for easy ssh access (cf. the tutorial under http://hilpisch.com/rpi/00_basic_config.html).

When you have created the droplet, you are redirected to the droplet overview page which shows, among others, the IP address of the droplet which you should copy.

Then navigate to the repository folder and do:

cd path-to/cloud-python
bash setup_server.sh THE-IP-ADDRESS

The setup might take a while. The last step in the setup fires up a Jupyter Notebook server on the port 8888. You can access it in the browser under

http://THE-IP-ADDRESS:8888

You can now click on the example notebooks and play around.

Flask Web app

In Jupyter Notebook open a new terminal and navigate to the stock_int folder:

cd stock_int

Start the example Flask application as follows:

python stock_interactive.py &

The app should now be reachable under

http://THE-IP-ADDRESS:7777

Security

Note that all this is really insecure. All is run as root user, no password protection or encryption is in place. It is only for illustration purposes. However, security features can easily be added to the set-up.

datapark.io

The easiest way to securely use Python, R, Julia, etc. in the cloud is to register under http://datapark.io.

With a single registration you have a comprehensive set of techonlogies, libraries and tools available to do data science in the browser.

Copyright, License & Disclaimer

© Dr. Yves J. Hilpisch | The Python Quants GmbH

The code of this repository is BSD licensed (cf. http://opensource.org/licenses/BSD-3-Clause).

The code in this repository comes with no representations or warranties, to the extent permitted by applicable law.

http://tpq.io | [email protected] | http://twitter.com/dyjh

datapark.io | http://datapark.io

Quant Platform | http://quant-platform.com

Derivatives Analytics with Python (Wiley Finance) | http://derivatives-analytics-with-python.com

Python for Finance (O'Reilly) | http://python-for-finance.com

For Python Quants Conference | http://fpq.io

cloud-python's People

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