cloud computing with Python 3 - python-3.x

I wanted to try cloud computing.
I went to PythonInfo Wiki and found links to Google App Engine and PiCloud (which uses Amazon Web Services). However, it turns out that neither of them supports Python 3 (at least at first glance). I would hate to port my code back to Python 2.7.
Does anyone know if there's an easy way to use cloud computing in Python 3 (I don't care who the provider of computing power is)?

With apologies for the self-promotion, my company are working on a cloud-based python environment. We should be able to support all flavours of Python. More info here:
http://www.pythonanywhere.com/

Related

How to migrate Google App Engine from Python2.7 and DataStore to Python3

My website was built using Google AppEngine, DataStore and Python2.7. It’s no longer working This site can’t be reached. I need to migrate to Python3 but I cannot identify which migration guide is best suited for me. Can anyone point me to the correct set? I would like to get it running as quickly as possible (I only have one hour a day to try to correct it -- I have an unrelated full-time job).
Migration guide
Google provides a step-by-step migration guide especially for AppEngine which you should follow.
Additionally, you will find lots of useful links there where you can read about the differences between Python 2 and Python 3 and the various migration tools available. Depending on your application those tools might even be able to do the migration (more or less) automatically for you.
Please note: This is the migration guide for the AppEngine standard environment. If you don't know what you're using, you're most likely using the standard environment. While some steps will differ when using the flexible environment, migration of the code base as described in the guide will always be required.
Video: Python 2 to 3: Migration Patterns & Motivators (Cloud Next '19)
There also is a recording of a talk by the Google Cloud Team on migration from Python 2 to 3 on YouTube.
Still having issues?
Migrating from Python 2 to 3 is a well-known problem and there is tons of information available on the internet. Most likely the problems you face have already been solved by someone, so a Google search for a specific problem will likely give you a working solution.

Google cloud memorystore and Google AppEngine standard with python 3

Is it possible to use memorystore with python 3 on GAE standard env? From faq at https://cloud.google.com/memorystore/docs/redis/faq it is clear that it can’t be use with GAE standard but since restrictions are different between python 3 and 2, it might be usable? Python 3 env is not supporting mecache right now and redislab requires another account /subscription managed.
Unfortunately currently it is not possible to access Memorystore from AppEngine Standard environment (even with python 3).
Please see the relevant links: https://cloud.google.com/memorystore/docs/redis/redis-overview#connecting
Also this is being tracked as a feature request here: https://issuetracker.google.com/issues/112411170
It is now available through any standard Redis client, check here https://cloud.google.com/memorystore/docs/redis/redis-overview#connecting

Save-AzureVMImage Generalized vs. Specialized

I've looked extensively at the Azure documentation regarding saving VM images. I understand there are two types available, Generalized and Specialized. I've read explanations of what the differences are. However, these appear to be written mostly for those very familiar with Azure concepts or IT in general. I'm more on the development side.
To my problem... I have an azure hosted image, which i've used as a build agent for teamcity. Our application isn't vanilla in that we can just install Visual Studio and be done. (i wish). We have about 20 or so third party dependent applications to install to the main OS disk, with lots of configuration required (System variables, etc.) to get it all to work.
So finally to my question - Which is the right version to use? Specialized or Generalized? I want to spawn 4 copies of this server in the same cloud service.
Any advice is greatly appreciated.
Generalized since you want them to be in the same cloud service. Generalized images start up differently as they configure themselves to take on new identities during their first start up.

Transfer application from one cloud to the another

Is there a tool which can transfer an Application/Code base from the Google Cloud to Azure Cloud?
Or is there a tool which can help with the process?
I tried Google but could not find anything.
I am afraid that you will not be able to find any tool helping out with code migration from Google Cloud over to Azure Cloud (or vice versa).
Google App Engine as well as Windows Azure are Platform as a Service (PaaS). Windows Azure also provides Infrastructure as a service (IaaS). Both platforms supports many different programming languages e.g. Java, PHP, Python etc. In many cases you can even use the same libraries (performing specific tasks) in both platforms. But that is pretty much is as far as similarities are concerned.
Each platform uses different architecture and different services; you need to use different SDKs. Also having in mind the fact that both platforms are dynamically evolving, it is practically not possible to a tool which allows you to transfer code base from one platform to another.

Pywin32 on Google App Engine?

I am considering ways to read/modify large Excel spreadsheets with formula support in python, on Google App Engine. I am fairly unfamiliar with how COM works but I was wondering if anyone has successfully implemented pywin32 on GAE - or whether there are inherently problems with doing so, or if it's just a bad idea in general.
It seems like the only possible solution for Python (xlrd has no formula support) but if it doesn't work, I will resort to learning Java and trying JExcel API.
Any insight would be appreciated!
Google's servers are not running Windows, so no, there's no way whatsoever to use any Win32 APIs.
If you have to use GAE then you may process some stuff on a windows machine. You can use Pull Queues to lease tasks from GAE process them and then add them to Push Queues that will store the data in GAE

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