cloud-test: August 2012

Google Cloud Platform Blog

Google App Engine Tips and Tutorials on Google Developers Live

Tuesday, August 28, 2012

Have you ever wanted to use a new feature of Google App Engine, but were uncertain of how to get started? Documentation and sample applications are a great way to start learning about App Engine, but sometimes a more interactive approach can make all the difference. Google Developers Live is exactly that. Each week, members of our Developer Relations Team host a Google+ Hangout or event to discuss a feature or tip. You can get expert answers to your questions, live. In addition to the live session, these talks are recorded and available on YouTube for you to view whenever you want.We’ve hosted sessions on using Google Cloud SQL, Google Cloud Endpoints, and other Google Cloud Platform services like Google Compute Engine. Have a topic you’d like to learn more about? Let us know by filling out this questionnaire.

- Posted by the Google App Engine Team

Google

App Engine 1.7.1 Released

Tuesday, August 21, 2012

If there were an Olympic coding event, we have no doubt that the gold medal winner could be found amongst the App Engine developer community. So today we’ve got a new release out so you can hone your skills just in case they are needed in Rio in 2016.App Admin

Usage Report Download - We’ve added the ability to download the past 90 days of your application’s usage reports as a CSV file.

Namespaces in the Memcache Viewer - The Admin Console now supports retrieving memcache values that are stored in a non-default namespace.

Python

Appstats updates - We’ve included a few new things in appstats with 1.7.1. You can now view RPC cost information in appstats. We’ve also added an interactive console which lets you trigger and then examine an RPC call for real-time debugging.

PyCrypto 2.6 support - We’ve included the latest version of PyCrypto as an option for third party libraries in Python 2.7.

JavaDataNucleus App Engine Plugin 2.1.0 - We’re excited to announce that with the latest upgrade we’re making V2 of the DataNucleus plugin fully supported. V2 adds support for JPA 2 and JDO 3, and this point release contains a variety of bug fixes. You can see the full list here.

Appstats Analytics Trusted TesterWe are looking for early Python and Java Trusted Testers to try a new interactive visualization tool for troubleshooting and tuning application performance. If you are interested in trying out this new tool please sign up here.

URLFetchWe’ve updated the way URLFetch handles multiple headers in response to one of our public issues. When a response contains the same header multiple times, these values will now be returned as a list.
Conversion API DecommissionWe are decommissioning the experimental Conversion API as of our November release. Please begin exploring alternative document conversion mechanisms.

The complete list of features and bug fixes for 1.7.1 can be found in our release notes (Java, Go, Python). For App Engine coding questions and answers check us out on Stack Overflow, and for general discussion and feedback, find us on our Google Group.

Attention App Engine experts: Interested in helping new businesses and enterprise customers be successful with Google Cloud Platform? Check out our job posting for Solution Architects. We are also hiring technical writers in Mountain View and San Francisco to document cool new App Engine features and engage with the community to broaden the impact of Google's cloud offerings.

Google

Neural Network for Breast Cancer Data Built on Google App Engine

Tuesday, August 7, 2012

Today’s guest blog post comes from 17-year-old Brittany Wenger, the winner of this year’s Google Science Fair. Brittany built an application on Google App Engine called the "Global Neural Network Cloud Service for Breast Cancer." This artificial neural network can detect complex patterns in data, learning how to classify malignant or cancerous cells it hasn’t seen before. Learn more about her project.

When a patient has a palpable breast lump, the first step a doctor takes is to determine whether the mass is malignant or benign. One relatively simple diagnostic procedure is a form of biopsy called fine needle aspiration (FNA). Though these tests are less invasive than others, they are historically less accurate as well. My goal was to create a tool for doctors to use when interpreting test results from these procedures.

For this project, I decided to create a neural network built on Google App Engine, using data published to the Machine Learning Repository by the University of Wisconsin. A neural network attempts to replicate the brain as a form of artificial intelligence through networks of computers and can be used to detect extremely complex patterns. It learns from its mistakes, so it can classify a case it hasn’t seen before as malignant or cancerous based on specific criteria like clump thickness or bland chromatin. Because the diagnostic power of the network improves the more data it has, building on App Engine is a way to ensure the app can continue to scale easily, no matter how much information goes into the system.

I got started integrating my neural network application code, written in Java, with App Engine in a few hours using the SDK’s Greeting Service sample code as a starting point. The application has two main parts, a training module, that implements the neural network itself and runs the training process over the input data stored in static files, and a web interface that takes input data and returns the network’s analysis.

Google App Engine provides the scalable infrastructure I need to collect information from every hospital in the world and run when there are many concurrent requests, as usage of my application increases. Because my network is built as a cloud service, not only is my app working on the web, but mobile tablets, smartphones, old PC systems, or new technologies can also easily access the service from any hospital with an internet connection.

The neural network I developed is 99.11% sensitive to malignancy when using leave-one-out testing with original data. Thus far, I have run 7.6 million trials. Moving forward my goal is to make the application accessible to the global medical community so more data can be deposited and used to improve the diagnostic power of the network.