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Revolution R Enterprise 6.0 launched!
Just got the email-more software is good news!
Revolution R Enterprise 6.0 for 32-bit and 64-bit Windows and 64-bit Red Hat Enterprise Linux (RHEL 5.x and RHEL 6.x) features an updated release of the RevoScaleR package that provides fast, scalable data management and data analysis: the same code scales from data frames to local, high-performance .xdf files to data distributed across a Windows HPC Server cluster or IBM Platform Computing LSF cluster. RevoScaleR also allows distribution of the execution of essentially any R function across cores and nodes, delivering the results back to the user.
Detailed information on what’s new in 6.0 and known issues:
http://www.revolutionanalytics.com/doc/README_RevoEnt_Windows_6.0.0.pdf
and from the manual-lots of function goodies for Big Data
- IBM Platform LSF Cluster support [Linux only]. The new RevoScaleR function, RxLsfCluster, allows you to create a distributed compute context for the Platform LSF workload manager.
- Azure Burst support added for Microsoft HPC Server [Windows only]. The new RevoScaleR function, RxAzureBurst, allows you to create a distributed compute context to have computations performed in the cloud using Azure Burst
- The rxExec function allows distributed execution of essentially any R function across cores and nodes, delivering the results back to the user.
- functions RxLocalParallel and RxLocalSeq allow you to create compute context objects for local parallel and local sequential computation, respectively.
- RxForeachDoPar allows you to create a compute context using the currently registered foreach parallel backend (doParallel, doSNOW, doMC, etc.). To execute rxExec calls, simply register the parallel backend as usual, then set your compute context as follows: rxSetComputeContext(RxForeachDoPar())
- rxSetComputeContext and rxGetComputeContext simplify management of compute contexts.
- rxGlm, provides a fast, scalable, distributable implementation of generalized linear models. This expands the list of full-featured high performance analytics functions already available: summary statistics (rxSummary), cubes and cross tabs (rxCube,rxCrossTabs), linear models (rxLinMod), covariance and correlation matrices (rxCovCor),
binomial logistic regression (rxLogit), and k-means clustering (rxKmeans)example: a Tweedie family with 1 million observations and 78 estimated coefficients (categorical data)
took 17 seconds with rxGlm compared with 377 seconds for glm on a quadcore laptopand easier working with R’s big brother SAS language
RevoScaleR high-performance analysis functions will now conveniently work directly with a variety of external data sources (delimited and fixed format text files, SAS files, SPSS files, and ODBC data connections). New functions are provided to create data source objects to represent these data sources (RxTextData, RxOdbcData, RxSasData, and RxSpssData), which in turn can be specified for the ‘data’ argument for these RevoScaleR analysis functions: rxHistogram, rxSummary, rxCube, rxCrossTabs, rxLinMod, rxCovCor, rxLogit, and rxGlm.
example,
you can analyze a SAS file directly as follows:
# Create a SAS data source with information about variables and # rows to read in each chunk
sasDataFile <- file.path(rxGetOption(“sampleDataDir”),”claims.sas7bdat”)
sasDS <- RxSasData(sasDataFile, stringsAsFactors = TRUE,colClasses = c(RowNum = “integer”),rowsPerRead = 50)# Compute and draw a histogram directly from the SAS file
rxHistogram( ~cost|type, data = sasDS)
# Compute summary statistics
rxSummary(~., data = sasDS)
# Estimate a linear model
linModObj <- rxLinMod(cost~age + car_age + type, data = sasDS)
summary(linModObj)
# Import a subset into a data frame for further inspection
subData <- rxImport(inData = sasDS, rowSelection = cost > 400,
varsToKeep = c(“cost”, “age”, “type”))
subData
The installation instructions and instructions for getting started with Revolution R Enterprise & RevoDeployR for Windows: http://www.revolutionanalytics.com/downloads/instructions/windows.php
Amazon Ec2 goes Red Hat
message from Amazing Amazon’s cloud team- this will also help for #rstats users given that revolution Analytics full versions on RHEL.
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on-demand instances of Amazon EC2 running Red Hat Enterprise Linux (RHEL) for as little as $0.145 per instance hour. The offering combines the cost-effectiveness, scalability and flexibility of running in Amazon EC2 with the proven reliability of Red Hat Enterprise Linux.
Highlights of the offering include:
- Support is included through subscription to AWS Premium Support with back-line support by Red Hat
- Ongoing maintenance, including security patches and bug fixes, via update repositories available in all Amazon EC2 regions
- Amazon EC2 running RHEL currently supports RHEL 5.5, RHEL 5.6, RHEL 6.0 and RHEL 6.1 in both 32 bit and 64 bit formats, and is available in all Regions.
- Customers who already own Red Hat licenses will continue to be able to use those licenses at no additional charge.
- Like all services offered by AWS, Amazon EC2 running Red Hat Enterprise Linux offers a low-cost, pay-as-you-go model with no long-term commitments and no minimum fees.
For more information, please visit the Amazon EC2 Red Hat Enterprise Linux page.
which is
Amazon EC2 Running Red Hat Enterprise Linux
Amazon EC2 running Red Hat Enterprise Linux provides a dependable platform to deploy a broad range of applications. By running RHEL on EC2, you can leverage the cost effectiveness, scalability and flexibility of Amazon EC2, the proven reliability of Red Hat Enterprise Linux, and AWS premium support with back-line support from Red Hat.. Red Hat Enterprise Linux on EC2 is available in versions 5.5, 5.6, 6.0, and 6.1, both in 32-bit and 64-bit architectures.
Amazon EC2 running Red Hat Enterprise Linux provides seamless integration with existing Amazon EC2 features including Amazon Elastic Block Store (EBS), Amazon CloudWatch, Elastic-Load Balancing, and Elastic IPs. Red Hat Enterprise Linux instances are available in multiple Availability Zones in all Regions.
Pricing
Pay only for what you use with no long-term commitments and no minimum fee.
On-Demand Instances
On-Demand Instances let you pay for compute capacity by the hour with no long-term commitments.
| Standard Instances | Red Hat Enterprise Linux |
|---|---|
| Small (Default) | $0.145 per hour |
| Large | $0.40 per hour |
| Extra Large | $0.74 per hour |
| Micro Instances | Red Hat Enterprise Linux |
| Micro | $0.08 per hour |
| High-Memory Instances | Red Hat Enterprise Linux |
| Extra Large | $0.56 per hour |
| Double Extra Large | $1.06 per hour |
| Quadruple Extra Large | $2.10 per hour |
| High-CPU Instances | Red Hat Enterprise Linux |
| Medium | $0.23 per hour |
| Extra Large | $0.78 per hour |
| Cluster Compute Instances | Red Hat Enterprise Linux |
| Quadruple Extra Large | $1.70 per hour |
| Cluster GPU Instances | Red Hat Enterprise Linux |
| Quadruple Extra Large | $2.20 per hour |
Pricing is per instance-hour consumed for each instance type. Partial instance-hours consumed are billed as full hours.
and
Available Instance Types
Standard Instances
Instances of this family are well suited for most applications.
Small Instance – default*
1.7 GB memory
1 EC2 Compute Unit (1 virtual core with 1 EC2 Compute Unit)
160 GB instance storage
32-bit platform
I/O Performance: Moderate
API name: m1.small
Large Instance
7.5 GB memory
4 EC2 Compute Units (2 virtual cores with 2 EC2 Compute Units each)
850 GB instance storage
64-bit platform
I/O Performance: High
API name: m1.large
Extra Large Instance
15 GB memory
8 EC2 Compute Units (4 virtual cores with 2 EC2 Compute Units each)
1,690 GB instance storage
64-bit platform
I/O Performance: High
API name: m1.xlarge
Micro Instances
Instances of this family provide a small amount of consistent CPU resources and allow you to burst CPU capacity when additional cycles are available. They are well suited for lower throughput applications and web sites that consume significant compute cycles periodically.
Micro Instance
613 MB memory
Up to 2 EC2 Compute Units (for short periodic bursts)
EBS storage only
32-bit or 64-bit platform
I/O Performance: Low
API name: t1.micro
High-Memory Instances
Instances of this family offer large memory sizes for high throughput applications, including database and memory caching applications.
High-Memory Extra Large Instance
17.1 GB of memory
6.5 EC2 Compute Units (2 virtual cores with 3.25 EC2 Compute Units each)
420 GB of instance storage
64-bit platform
I/O Performance: Moderate
API name: m2.xlarge
High-Memory Double Extra Large Instance
34.2 GB of memory
13 EC2 Compute Units (4 virtual cores with 3.25 EC2 Compute Units each)
850 GB of instance storage
64-bit platform
I/O Performance: High
API name: m2.2xlarge
High-Memory Quadruple Extra Large Instance
68.4 GB of memory
26 EC2 Compute Units (8 virtual cores with 3.25 EC2 Compute Units each)
1690 GB of instance storage
64-bit platform
I/O Performance: High
API name: m2.4xlarge
High-CPU Instances
Instances of this family have proportionally more CPU resources than memory (RAM) and are well suited for compute-intensive applications.
High-CPU Medium Instance
1.7 GB of memory
5 EC2 Compute Units (2 virtual cores with 2.5 EC2 Compute Units each)
350 GB of instance storage
32-bit platform
I/O Performance: Moderate
API name: c1.medium
High-CPU Extra Large Instance
7 GB of memory
20 EC2 Compute Units (8 virtual cores with 2.5 EC2 Compute Units each)
1690 GB of instance storage
64-bit platform
I/O Performance: High
API name: c1.xlarge
Cluster Compute Instances
Instances of this family provide proportionally high CPU resources with increased network performance and are well suited for High Performance Compute (HPC) applications and other demanding network-bound applications. Learn more about use of this instance type for HPC applications.
Cluster Compute Quadruple Extra Large Instance
23 GB of memory
33.5 EC2 Compute Units (2 x Intel Xeon X5570, quad-core “Nehalem” architecture)
1690 GB of instance storage
64-bit platform
I/O Performance: Very High (10 Gigabit Ethernet)
API name: cc1.4xlarge
Cluster GPU Instances
Instances of this family provide general-purpose graphics processing units (GPUs) with proportionally high CPU and increased network performance for applications benefitting from highly parallelized processing, including HPC, rendering and media processing applications. While Cluster Compute Instances provide the ability to create clusters of instances connected by a low latency, high throughput network, Cluster GPU Instances provide an additional option for applications that can benefit from the efficiency gains of the parallel computing power of GPUs over what can be achieved with traditional processors. Learn more about use of this instance type for HPC applications.
Cluster GPU Quadruple Extra Large Instance
22 GB of memory
33.5 EC2 Compute Units (2 x Intel Xeon X5570, quad-core “Nehalem” architecture)
2 x NVIDIA Tesla “Fermi” M2050 GPUs
1690 GB of instance storage
64-bit platform
I/O Performance: Very High (10 Gigabit Ethernet)
API name: cg1.4xlarge
Getting Started
To get started using Red Hat Enterprise Linux on Amazon EC2, perform the following steps:
- Open and log into the AWS Management Console
- Click on Launch Instance from the EC2 Dashboard
- Select the Red Hat Enterprise Linux AMI from the QuickStart tab
- Specify additional details of your instance and click Launch
- Additional details can be found on each AMI’s Catalog Entry page
The AWS Management Console is an easy tool to start and manage your instances. If you are looking for more details on launching an instance, a quick video tutorial on how to use Amazon EC2 with the AWS Management Console can be found here .
A full list of Red Hat Enterprise Linux AMIs can be found in the AWS AMI Catalog.
Support
All customers running Red Hat Enterprise Linux on EC2 will receive access to repository updates from Red Hat. Moreover, AWS Premium support customers can contact AWS to get access to a support structure from both Amazon and Red Hat.
Resources
- Red Hat solution provider page in EC2
- Red Hat Images in the Amazon Machine Image catalog
- Cloud Computing with Red Hat
About Red Hat
Red Hat, the world’s leading open source solutions provider, is headquartered in Raleigh, NC with over 50 satellite offices spanning the globe. Red Hat provides high-quality, low-cost technology with its operating system platform, Red Hat Enterprise Linux, together with applications, management and Services Oriented Architecture (SOA) solutions, including the JBoss Enterprise Middleware Suite. Red Hat also offers support, training and consulting services to its customers worldwide.
also from Revolution Analytics- in case you want to #rstats in the cloud and thus kill all that talk of RAM dependency, slow R than other softwares (just increase the RAM above in the instances to keep it simple)
,or Revolution not being open enough
http://www.revolutionanalytics.com/downloads/gpl-sources.php
GPL SOURCES
Revolution Analytics uses an Open-Core Licensing model. We provide open- source R bundled with proprietary modules from Revolution Analytics that provide additional functionality for our users. Open-source R is distributed under the GNU Public License (version 2), and we make our software available under a commercial license.
Revolution Analytics respects the importance of open source licenses and has contributed code to the open source R project and will continue to do so. We have carefully reviewed our compliance with GPLv2 and have worked with Mark Radcliffe of DLA Piper, the outside General Legal Counsel of the Open Source Initiative, to ensure that we fully comply with the obligations of the GPLv2.
For our Revolution R distribution, we may make some minor modifications to the R sources (the ChangeLog file lists all changes made). You can download these modified sources of open-source R under the terms of the GPLv2, using either the links below or those in the email sent to you when you download a specific version of Revolution R.
Download GPL Sources
| Product | Version | Platform | Modified R Sources |
| Revolution R Community | 3.2 | Windows | R 2.10.1 |
| Revolution R Community | 3.2 | MacOS | R 2.10.1 |
| Revolution R Enterprise | 3.1.1 | RHEL | R 2.9.2 |
| Revolution R Enterprise | 4.0 | Windows | R 2.11.1 |
| Revolution R Enterprise | 4.0.1 | RHEL | R 2.11.1 |
| Revolution R Enterprise | 4.1.0 | Windows | R 2.11.1 |
| Revolution R Enterprise | 4.2 | Windows | R 2.11.1 |
| Revolution R Enterprise | 4.2 | RHEL | R 2.11.1 |
| Revolution R Enterprise | 4.3 | Windows & RHEL | R 2.12.2 |
Red Hat worth 7.8 Billion now
I was searching for a Linux install of Revolution’s latest enterprise version, but it seems version 4 will be available on Red Hat Enterprise Linux only by Decemebr 2010. Also even though Revolution once opted for co branding with Canonical’s Karmic Koala, they seem to have ignored Ubuntu from the Enterprise version of Revolution R.
http://www.revolutionanalytics.com/why-revolution-r/which-r-is-right-for-me.php
| Base R | Revolution R Community | Revolution R Enterprise |
|
| Target Use | Open Source | Product Evaluation & Simple Prototyping | Business, Research & Academics |
| Software | |||
| 100% Compatible with R language | X | X | X |
| Certified for Stability | X | X | |
| Command-Line Programming | X | X | X |
| Getting Started Guide | X | X | |
| Performance & Scalability | |||
| Analyze larger data sets with 64-bit RAM | X | X | |
| Optimized for Multi-processor workstations | X | X | |
| Multi-threaded Math libraries | X | X | |
| Parallel Programming (Single Workstation) | X | X | |
| Out-of-the-Box Cluster-Ready | X | ||
| “Big Data” Analysis | |||
| Terabyte-Class File Structures | X | ||
| Specialized “Big Data” Algorithms | X | ||
| Integrated Web Services | |||
| Scalable Web Services Platform | X* | ||
| User Interface | |||
| Visual IDE | X | ||
| Comprehensive Data Analysis GUI | X* | ||
| Technical Support | |||
| Discussion Forums | X | X | X |
| Online Support | Mailing List | Forum | X |
| Email Support | X | ||
| Phone Support | X | ||
| Support for Base & Recommended R Packages | X | X | X |
| Authorized Training & Consulting | X | ||
| Platforms | |||
| Single User | X | X | X |
| Multi-User Server | X | X | |
| 32-bit Windows | X | X | X |
| 64-bit Windows | X | X | |
| Mac OS X | X | X | |
| Ubuntu Linux | X | X | |
| Red Hat Enterprise Linux | X | ||
| Cloud-Ready | X |
and though the page on RED HAT’s Partner page for Revolution seems old/not so updated
https://www.redhat.com/wapps/partnerlocator/web/home.html;#productId=188
, I was still curious to see what the buzz about Red Hat is all about.
And one of the answers is Red Hat is now a 7.8 Billion Dollar Company.
http://www.redhat.com/about/news/prarchive/2010/Q2_2011.html
Red Hat Reports Second Quarter Results
- Revenue of $220 million, up 20% from the prior year
- GAAP operating income up 24%, non-GAAP operating income up 25% from the prior year
- Deferred revenue of $650 million, up 12% from the prior year
RALEIGH, NC – Sept 22, 2010 – Red Hat, Inc. (NYSE: RHT), the world’s leading provider of open source solutions, today announced financial results for its fiscal year 2011 second quarter ended August 31, 2010.
Total revenue for the quarter was $219.8 million, an increase of 20% from the year ago quarter. Subscription revenue for the quarter was $186.2 million, up 19% year-over-year.
and the stock goes zoom 48 % up for the year
(Note to Google- please put the URL shortener on Google Finance as well)
The software is also reasonably priced starting from 80$ onwards.
https://www.redhat.com/apps/store/desktop/
September Roundup by Revolution
From the monthly newsletter- which I consider quite useful for keeping updated on application of R
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Revolution News
Every month, we’ll bring you the latest news about Revolution’s products and events in this section. Follow us on Twitter at @RevolutionR for up-to-the-minute news and updates from Revolution Analytics!Revolution R Enterprise 4.0 for Windows now available. Based on the latest R 2.11.1 and including the RevoScaleR package for big-data analysis in R, Revolution R Enterprise is now available for download for Windows 32-bit and 64-bit systems. Click here to subscribe, or available free to academia.
New! Integrate R with web applications, BI dashboards and more with web services. RevoDeployR is a new Web Services framework that integrates dynamic R-based computations into applications for business users. It will be available September 30 with Revolution R Enterprise Server on RHEL 5. Click here to learn more.
Free Webinar, September 22: In a joint webinar from Revolution Analytics and Jaspersoft, learn how to use RevoDeployR to integrate advanced analytics on-demand in applications, BI dashboards, and on the web. Register here.
Revolution in the News: SearchBusinessAnalytics.com previews the forthcoming Revolution R GUI; Channel Register introduces RevoDeployR, while IT Business Edge shows off the Web Services architecture; and ReadWriteWeb.com looks at how RevoScaleR tackles the Big Data explosion.Inside-R: A new site for the R Community. At www.inside-R.org you’ll find the latest information about R from around the Web, searchable R documentation and packages, hints and tips about R, and more. You can even add a “Download R” badge to your own web-page to help spread the word about R.
R News, Tips and Tricks from the Revolutions blog
The Revolutions blog brings you daily news and tips about R, statistics and open source. Here are some highlights from Revolutions from the past month.R’s key role in the oil spill response: Read how NIST’s Division Chief of Statistical Engineering used R to provide critical analysis in real time to the Secretaries of Energy and the Interior, and helped coordinate the government’s response.
Animating data with R and Google Earth: Learn how to use R to create animated visualizations of geographical data with Google Earth, such as this video showing how tuna migrations intersect with the location of the Gulf oil spill.
Are baseball games getting longer? Or is it just Red Sox games? Ryan Elmore uses nonparametric regression in R to find out.
Keynote presentations from useR! 2010: the worldwide R user’s conference was a great success, and there’s a wealth of useful tips and information in the presentations. Video of the keynote presentations are available too: check out in particular Frank Harrell’s talk Information Allergy, and Friedrich Leisch’s talk on reproducible statistical research.
Looking for more R tips and tricks? Check out the monthly round-ups at the Revolutions blog.
Upcoming Events
Every month, we’ll highlight some upcoming events from R Community Calendar.September 23: The San Diego R User Group has a meetup on BioConductor and microarray data analysis.
September 28: The Sydney Users of R Forum has a meetup on building world-class predictive models in R (with dinner to follow).
September 28: The Los Angeles R User Group presents an introduction to statistical finance with R.
September 28: The Seattle R User Group meets to discuss, “What are you doing with R?”
September 29: The Raleigh-Durham-Chapel Hill R Users Group has its first meeting.
October 7: The NYC R User Group features a presentation by Prof. Andrew Gelman.
There are also new R user groups in Singapore, Seoul, Denver, Brisbane, and New Jersey. Please let us know if we’re missing your R user group, or if want to get a new one started.
———————————————————————————————-Editor
David Smith, VP Marketing
david@revolutionanalytics.com
Twitter: @revodavidsubscribe here for Revo’s Monthly newsletter-
