On Wed, May 26, 2010 at 12:54 PM, Robert Kern firstname.lastname@example.org wrote:
On Wed, May 26, 2010 at 12:19, Christopher Hanley email@example.com wrote:
Google provides a product called App Engine. The description from their site follows,
"Google App Engine enables you to build and host web apps on the same systems that power Google applications. App Engine offers fast development and deployment; simple administration, with no need to worry about hardware, patches or backups; and effortless scalability. "
You can deploy applications written in either Python or JAVA. There are free and paid versions of the service.
The Google App Engine would appear to be a powerful source of CPU cycles for scientific computing.
Not really. It is not intended for such purposes. It is intended for the easy deployment and horizontal scaling of web applications. Each individual request is very short; it is limited to 10 seconds of CPU time. While numpy would be useful for scientific web applications (not least because it would help you keep to that 10 second limit when doing things like simple image processing or summary statistics or whatever), it is not a source of CPU cycles. Services like Amazon EC2 or Rackspace Cloud are much closer to what you want. PiCloud provides an even nicer interface for you:
In my conversations with the developers they indicated that it could be used for both. However, either use case would be useful for scientific computing.
Disclosure: Enthought partners with PiCloud to provide most EPD libraries. I can't say I'm disinterested in promoting it, but it *is* a really powerful product that *does* provide CPU cycles for scientific computing with an interface much more suited to it than GAE.
Unfortunately this is currently not the case because numpy is not one of the supported libraries. The Python App Engine allows only the installation of user supplied pure Python code.
I have recently returned from attending the Google I/O conference in San Francisco. While there I inquired into the possibility of getting numpy added. The basic response was that there doesn't appear to be much interest from the community given the amount of work it would take to vet and add numpy.
I would like to ask your help in changing this perception.
The quickest and easiest thing you can do would be to add your "me too" to this feature request (item #190) on the support site:
My understanding is that they hate "me too" comments. They ask that you star the issue instead.
I would be happy to see any support either starring or "me too" comments. Their comments to me was that they saw no interest. In my opinion any indication of interest would be a positive.
-- Robert Kern
"I have come to believe that the whole world is an enigma, a harmless enigma that is made terrible by our own mad attempt to interpret it as though it had an underlying truth." -- Umberto Eco _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion