How to manage cloud resources using Python? Raise clusters on demand through a few lines of code

The Simple Azure Library lets you manage your cloud resources, including creating, managing, and deleting virtual machines in a cloud environment. You can use this library for any purpose: from deploying a sandbox for Dev & Test purposes to hosting and managing solutions in industrial operation.

Using Simple Azure, you can easily raise ipython notebook and ipython clusters in a cloud environment, deploy ready-made virtual machines to choose from hundreds presented in the VMDepot directory .

The following is a brief introduction and examples of using Simple Azure for simple tasks and the tasks of deploying an IPython cluster in the cloud.

Installation

$ pip install simpleazure 
$ simpleazure-cluster start mycluster

Virtual Machine Deployment

from simpleazure.simpleazure import SimpleAzure as saz 
azure = saz() 
azure.get_config() 
azure.create_vm()

Video :



Change the virtual machine to choose from:

azure.set_image(label="Ubuntu Server 12.04.2 LTS")

Placement of several cars

azure = saz() 
azure.get_config() 
azure.create_cluster(num=5) 
my-cluster-vm-0-87412 
{'request_id': '88c94c00288d42acaf877783f09c4558'} 
my-cluster-vm-1-61293 
{'request_id': 'abfd563c2c4f4926872b6b1dba27a93b'} 
my-cluster-vm-2-96085 
{'request_id': '29b55f6cb5e94cfdbf244a7c848c854d'} 
my-cluster-vm-3-46927 
{'request_id': 'b1a3446ebafe47a295df4c9d1b7d743c'}

Hosting machines from the VMDepot community directory

In this code example, an OpenSUSE-based Azure Data Science Core virtual machine is selected from the VMDepot community directory with a customized environment for working with big data, HPC and the following packages: cython, ipython, matplotlib, networkx, nltk, nodejs, numpy, pandas, pytables, redis, scikit-image, scikit-learn, scipy, statsmodels, sympy.

azure = saz() 
azure.get_config() 
q = azure.get_registered_image(name="Azure-Data-Science-Core") 
azure.set_image(image=q) 
azure.create_vm()

Command line support

Simple Azure supports commands in the Linux shell. For example, you can create virtual machine clusters like StarCluster with the following command:

$ simpleazure-cluster start mycluster

Enabling IPython Cluster

This guide describes how to deploy an IPython cluster in the cloud in a few steps.

Step one:

from simpleazure.simpleazure import SimpleAzure as saz 
azure = saz() 
azure.get_config()

Step Two - Create Three Nodes for a Cluster with ADSC:

adsc = azure.get_registered_image(name="Azure-Data-Science-Core") 
azure.set_image(image=adsc) 
azure.set_location("West Europe") 
azure.create_cluster(3)

Step Three - Import the IPython Plugin:

from simpleazure.plugin import ipython 
ipy = ipython.IPython()

SSH setup:

ipy.set_username(azure.get_username()) 
ipy.set_private_key(azure.get_pkey())

Step four - configure the master and engine nodes:

from simpleazure import config 
master = config.get_azure_domain(azure.results['master']) 
engines = [ config.get_azure_domain(x) for x in azure.results.keys()]

Name binding in IPython plugin:

ipy.set_master(master) 
ipy.set_engines(engines)

Setting up an SSH connection to hosts:

ipy.init_ssh()
ipy.connect_nodes()

Create IPython Profile:

ipy.create_profile()

Running IPController on master:

ipy.run_ipcontroller()

Configuring engine nodes:

ipy.copy_pkey_to_nodes() # <- Temporary function to distribute id_rsa private key to node(s) 
ipy.copy_json2engines()

The final step is to run ipengine on each engine node so that they start interacting with master:

ipy.run_ipengine()
ipy.apply_ipcluster(azure)

Additional Information

You can find more tutorials at the following link . The subtleties of installing and configuring the library are described here . You can get the source code of the library and join the development of the project on GitHub . You can always find the latest news and information about the project on the official Simple Azure website .

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