red hat – Page 3 – DECISION STATS

Running R on Amazon EC2

On my second day of bludering about high technology, I came across http://rgrossman.com/2009/05/17/running-r-on-amazons-ec2/ which describes how to run R on Amazon EC2.

I tried it out and have subsequently added some screenshots to this tutorial so as to help you run R. My intention of course was to run a R GUI preferable Revolution Enterprise on the Amazon EC 2- and crunch uhm a lot of data.

Now go through the steps as follows-

0) Logging onto Amazon Console

http://aws.amazon.com/ec2/

Note you need your Amazon Id (even the same id which you use for buying books).

Note we are into Amazon EC2 as shown by the upper tab. Click upper tab to get into the Amazon EC2

2) Choosing the right AMI-

On the left margin, you can click AMI -Images.

Now you can search for the image-

I chose Ubuntu images (linux images are cheaper) and opendata in the search as belows- I get two images.

You can choose whether you want 32 bit or 64 bit image. Thumb rule- 64 bit images are preferable for data intensive tasks.

Click on launch instance in the upper tab ( near the search feature)

2) A pop up comes up, which shows the 5 step process to launch your computing.

Choose the right compute instance- As the screenshot shows- there are various compute instances and they all are at different multiples of prices or compute units.

After choosing the compute instance of your choice (extra large is highlighted)- click on continue-

3) Instance Details-

I did not choose cloudburst monitoring as it has a extra charge- and I am just trying out things.So I simply clicked continue.

4) Add Tag Details- If you are running a lot of instances you need to create your own tags to help you manage them. Advisable if you are running many instances.

Since I am going to run just one- I clicked continue with adding just two things OS and Stats Package.

5) Create a key pair- A key pair is an added layer of encryption. Click on create new pair and name it (note the name will be handy in coming steps)

After clicking and downloading the key pair- you come into security groups. Security groups is just a set of instructions to help keep your data transfer secure. So I created a new security group.

And I added some ways in security group to connect (like SSH using Port 22)

7) Last step- Review Details and Click Launch

8) On the Left margin click on instances ( you were in Images.>AMI earlier)

It will take some 3-5 minutes to launch an instance. You can see status as pending till then.

9) Pending instance as shown by yellow light-

10) Once the instance is running -it is shown by a green light.

Click on the check box, and on upper tab go to instance actions. Click on connect-

you see a popup with instructions like these-

Open the SSH client of your choice (e.g., PuTTY, terminal).

Locate your private key file, decisionstats2.pem

Use chmod to make sure your key file isn’t publicly viewable, ssh won’t work otherwise:
chmod 400 decisionstats.pem

Connect to your instance using instance’s public DNS [ec2-75-101-182-203.compute-1.amazonaws.com].

Example

Enter the following command line:

ssh -i decisionstats2.pem root@ec2-75-101-182-203.compute-1.amazonaws.com

IMPORTANT-

If you are choosing Ubuntu Terminal to connect- you need to change the word root from above to Ubuntu above.

12) To launch R, just type R at the terminal

If all goes well you should be able to see this-

choose to install any custom packages (like

install.packages(‘doSNOW’)

work on R using command line

13) IMPORTANT- After doing your R work, please CLOSE your instance (

Go to LEFT Margin-Instances-Check the check box of instance you are running- on upper tab- Instance Actions- Click Terminate.

Submitted By:	Eduardo L Leoni
US East AMI ID:	ami-1b9b7c72
AMI Manifest:	PolMethImages/imageR64.manifest.xml
License:	Public
Operating System:	Linux/Uni

Note there are other Amazon Machine Images as well which have R- I found this as well-

Amazon EC2 Ubuntu 8.10 intrepid AMI built by Eric Hammond; Eduardo Leoni added R, many R packages, JAGS, mysql-client and subversion.

Submitted By: Eduardo L Leoni

US East AMI ID: ami-1b9b7c72

AMI Manifest: PolMethImages/imageR64.manifest.xml

License: Public

Operating System: Linux/Uni

2) You can install Revolution R on 32 bit Ubuntu using sudo apt install revolution-r

Various versions of Revolution R are supported on different versions of Linux

see http://www.revolutionanalytics.com/why-revolution-r/which-r-is-right-for-me.php

I may be willing to try Red Hat Enterprise Linux on the ec2 with 64 bit AND Revolution Enterprise to see the maximum juice I can get. or you can try it using an image below- It would be interesting if there could be an Amazon Machine Image for that (paid-public and private-academic)

AMI ID: ami-8ba347e2
Name:–
Description:–
Source:redhat-cloud/RHEL-5-Server/5.2/x86_64/Beta-2.6.18-92.1.1/RHEL5.2-Server-x86_64-Beta-2.6.18-92.1.1.manifest.xml
Owner:432018295444	Visibility:Public	Product Code:54DBF944
State:available	Kernel ID:aki-89a347e0	RAM Disk ID:ari-88a347e1
Image Type:machine	Architecture:x86_64	Platform:Red Hat
Root Device Type:instance-store	Root Device:–	Image Size:0 bytes
Block Devices:N/A – Instance Store
Virtualization:paravirtual

3) My ultimate goal is to run a parallel session using all cores on an EC2 instance and a R GUI (like R Commander or Rattle)

4) For sake of running a test- I re did the parallel test I did on my 2 core laptop

but using 68.4 gb Memory and 8 cores (26 Compute Units brrr)

> check <-function(n) {

+ for(i in 1:1000)

+ {

+ sme <- matrix(rnorm(100), 10,10)

+ solve(sme)

+ }

> times <- 100

> system.time(for(j in 1:times ) x <- check(j))

user system elapsed

20.51 0.00 20.66

Note I still got a faster response than using parallel processors (on a 2 core 3 gb memory) BUT the gain was not as much had I tried to use foreach for running each of the 8 cores parallely. Or running multicore package as in http://www.cerebralmastication.com/2010/02/using-the-r-multicore-package-in-linux-with-wild-and-passionate-abandon/

5) For some reason on Ubuntu 64 bit Amazon image -I cant get Revolution R (even after using sudo apt) , and I am still learning how I can try and get Enterprise Edition fired up on a 64 bit Red Hat Enterprise Linux Amazon AMI (and maybe create an all new Machine Image 😉

6) I was also unable to use the -X command desite having x 11 as in http://rgrossman.com/2009/05/17/running-r-on-amazons-ec2/ so was not able to see graphics,

Also it prevents me from loogin onto as root, and asks me to login as ubuntu@amazon…. I also wanted to try logging into using my Windows session but kept shuttling between my VM Player Ubuntu session and Windows

7) Hope this was useful. I am thankful to tips from Revolution Blog, R Grossman’s Blog, Creators of the Open Data AMI, Tal’s R Statistics Blog and Cerebral Mastication blog on this

R on Windows HPC Server

From HPC Wire, the newsletter/site for all HPC news-

Source- Link

PALO ALTO, Calif., Sept. 20 — Revolution Analytics, the leading commercial provider of software and support for the popular open source R statistics language, today announced it will deliver Revolution R Enterprise for Microsoft Windows HPC Server 2008 R2, released today, enabling users to analyze very large data sets in high-performance computing environments.

R is a powerful open source statistics language and the modern system for predictive analytics. Revolution Analytics recently introduced RevoScaleR, new “Big Data” analysis capabilities, to its R distribution, Revolution R Enterprise. RevoScaleR solves the performance and capacity limitations of the R language by with parallelized algorithms that stream data across multiple cores on a laptop, workstation or server. Users can now process, visualize and model terabyte-class data sets at top speeds — without the need for specialized hardware.

“Revolution Analytics is pleased to support Microsoft’s Technical Computing initiative, whose efforts will benefit scientists, engineers and data analysts,” said David Champagne, CTO at Revolution. “We believe the engineering we have done for Revolution R Enterprise, in particular our work on big-data statistics and multicore computing, along with Microsoft’s HPC platform for technical computing, makes an ideal combination for high-performance large scale statistical computing.”

“Processing and analyzing this ‘big data’ is essential to better prediction and decision making,” said Bill Hamilton, director of technical computing at Microsoft Corp. “Revolution R Enterprise for Windows HPC Server 2008 R2 gives customers an extremely powerful tool that handles analysis of very large data and high workloads.”

To learn more about Revolution R Enterprise and its Big Data capabilities, download thewhite paper. Revolution Analytics also has an on-demand webcast, “High-performance analytics with Revolution R and Windows HPC Server,” available online.

AND from Microsoft’s website

http://www.microsoft.com/hpc/en/us/solutions/hpc-for-life-sciences.aspx

REvolution R Enterprise »

REvolution Computing

REvolution R Enterprise is designed for both novice and experienced R users looking for a production-grade R distribution to perform mission critical predictive analytics tasks right from the desktop and scale across multiprocessor environments. Featuring RPE™ REvolution’s R Productivity Environment for Windows.

Of course R Enterprise is available on Linux but on Red Hat Enterprise Linux- it would be nice to see Amazom Machine Images as well as Ubuntu versions as well.

An Amazon Machine Image (AMI) is a special type of virtual appliance which is used to instantiate (create) a virtual machine within the Amazon Elastic Compute Cloud. It serves as the basic unit of deployment for services delivered using EC2.^[1]

Like all virtual appliances, the main component of an AMI is a read-only filesystem image which includes an operating system (e.g., Linux, UNIX, or Windows) and any additional software required to deliver a service or a portion of it.^[2]

The AMI filesystem is compressed, encrypted, signed, split into a series of 10MB chunks and uploaded into Amazon S3 for storage. An XML manifest file stores information about the AMI, including name, version, architecture, default kernel id, decryption key and digests for all of the filesystem chunks.

An AMI does not include a kernel image, only a pointer to the default kernel id, which can be chosen from an approved list of safe kernels maintained by Amazon and its partners (e.g., RedHat, Canonical, Microsoft). Users may choose kernels other than the default when booting an AMI.^[3]

[edit]Types of images

Public: an AMI image that can be used by any one.

Paid: a for-pay AMI image that is registered with Amazon DevPay and can be used by any one who subscribes for it. DevPay allows developers to mark-up Amazon’s usage fees and optionally add monthly subscription fees.

Linux= Who did what and how much?

A report distributed under Creative Commons 3 and available at

That shows Canonical — the commercial arm of Ubuntu — has contributed only about one percent of the code to the GNOME desktop for Linux. while Red Hat accounts for 17 percent of the code and Novell developers are responsible for about 11 percent. That prompted some heartburn from Mark, creator- founder Cannonical/ Ubuntu at http://www.markshuttleworth.com/archives/517

And it would be a very different story if it weren’t for the Mozilla folks and Netscape before them, and GNOME and KDE, and Google and everyone else who have exercised that stack in so many different ways, making it better along the way. There are tens of thousands of people who are not in any way shape or form associated with Ubuntu, who make this story real. Many of them have been working at it for more than a decade – it takes a long time to make an overnight success while Ubuntu has only been on the scene six years. So Ubuntu cannot be credited solely for the delight of its users.

Nevertheless, the Ubuntu Project does bring something unique, special and important to free software: a total commitment to everyday users and use cases, the idea that free software should be “for everyone” both economically and in ease of use, and a willingness to chase down the problems that stand between here and there. I feel that commitment is a gift back to the people who built every one of those packages. If we can bring free software to ten times the audience, we have amplified the value of your generosity by a factor of ten, we have made every hour spent fixing an issue or making something amazing, ten times as valuable. I’m very proud to be spending the time and energy on Ubuntu that I do. Yes, I could do many other things, but I can’t think of another course which would have the same impact on the world.

I recognize that not everybody will feel the same way. Bringing their work to ten times the audience without contributing features might just feel like leeching, or increasing the flow of bug reports 10x. I suppose you could say that no matter how generous we are to downstream users, if upstream is only measuring code, then any generosity other than code won’t be registered. I don’t really know what to do about that – I didn’t found Ubuntu as a vehicle for getting lots of code written, that didn’t seem to me to be what the world needed.

Open source communities work like democracies with all noise whereas R and D within corporates have a stricter hierarchy. Still for all that – Ubuntu and Android have made Linux mainstream just as R has made statistical software available to all.

And Ubuntu also has great support for R (particularly the single click R Commander Install and Icon) available at http://packages.ubuntu.com/lucid/math/r-cran-rcmdr

Big Data and R: New Product Release by Revolution Analytics

Press Release by the Guys in Revolution Analytics- this time claiming to enable terabyte level analytics with R. Interesting stuff but techie details are awaited.

Revolution Analytics Brings

Big Data Analysis to R

The world’s most powerful statistics language can now tackle terabyte-class data sets using

Revolution R Enterprise—at a fraction of the cost of legacy analytics products

JSM 2010 – VANCOUVER (August 3, 2010) — Revolution Analytics today introduced ‘Big Data’ analysis to its Revolution R Enterprise software, taking the popular R statistics language to unprecedented new levels of capacity and performance for analyzing very large data sets. For the first time, R users will be able to process, visualize and model terabyte-class data sets in a fraction of the time of legacy products—without employing expensive or specialized hardware.

The new version of Revolution R Enterprise introduces an add-on package called RevoScaleR that provides a new framework for fast and efficient multi-core processing of large data sets. It includes:

The XDF file format, a new binary ‘Big Data’ file format with an interface to the R language that provides high-speed access to arbitrary rows, blocks and columns of data.

A collection of widely-used statistical algorithms optimized for Big Data, including high-performance implementations of Summary Statistics, Linear Regression, Binomial Logistic Regressionand Crosstabs—with more to be added in the near future.

Data Reading & Transformation tools that allow users to interactively explore and prepare large data sets for analysis.

Extensibility, expert R users can develop and extend their own statistical algorithms to take advantage of Revolution R Enterprise’s new speed and scalability capabilities.

“The R language’s inherent power and extensibility has driven its explosive adoption as the modern system for predictive analytics,” said Norman H. Nie, president and CEO of Revolution Analytics. “We believe that this new Big Data scalability will help R transition from an amazing research and prototyping tool to a production-ready platform for enterprise applications such as quantitative finance and risk management, social media, bioinformatics and telecommunications data analysis.”

Sage Bionetworks is the nonprofit force behind the open-source collaborative effort, Sage Commons, a place where data and disease models can be shared by scientists to better understand disease biology. David Henderson, Director of Scientific Computing at Sage, commented: “At Sage Bionetworks, we need to analyze genomic databases hundreds of gigabytes in size with R. We’re looking forward to using the high-speed data-analysis features of RevoScaleR to dramatically reduce the times it takes us to process these data sets.”

Take Hadoop and Other Big Data Sources to the Next Level

Revolution R Enterprise fits well within the modern ‘Big Data’ architecture by leveraging popular sources such as Hadoop, NoSQL or key value databases, relational databases and data warehouses. These products can be used to store, regularize and do basic manipulation on very large datasets—while Revolution R Enterprise now provides advanced analytics at unparalleled speed and scale: producing speed on speed.

“Together, Hadoop and R can store and analyze massive, complex data,” said Saptarshi Guha, developer of the popular RHIPE R package that integrates the Hadoop framework with R in an automatically distributed computing environment. “Employing the new capabilities of Revolution R Enterprise, we will be able to go even further and compute Big Data regressions and more.”

Platforms and Availability

The new RevoScaleR package will be delivered as part of Revolution R Enterprise 4.0, which will be available for 32-and 64-bit Microsoft Windows in the next 30 days. Support for Red Hat Enterprise Linux (RHEL 5) is planned for later this year.

On its website (http://www.revolutionanalytics.com/bigdata), Revolution Analytics has published performance and scalability benchmarks for Revolution R Enterprise analyzing a 13.2 gigabyte data set of commercial airline information containing more than 123 million rows, and 29 columns.

Additionally, the company will showcase its new Big Data solution in a free webinar on August 25 at 9:00 a.m. Pacific.

Additional Resources

•      Big Data Benchmark whitepaper

•      The Revolution Analytics Roadmap whitepaper

•      Revolutions Blog

•      Download free academic copy of Revolution R Enterprise

•      Visit Inside-R.org for the most comprehensive set of information on R

•      Spread the word: Add a “Download R!” badge on your website

•      Follow @RevolutionR on Twitter

About Revolution Analytics

Revolution Analytics (http://www.revolutionanalytics.com) is the leading commercial provider of software and support for the popular open source R statistics language. Its Revolution R products help make predictive analytics accessible to every type of user and budget. The company is headquartered in Palo Alto, Calif. and backed by North Bridge Venture Partners and Intel Capital.

Media Contact

Chantal Yang
Page One PR, for Revolution Analytics
Tel: +1 415-875-7494

Email:  revolution@pageonepr.com

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