Open Source Software

  1. Over the past year, Google’s TensorFlow has asserted itself as a popular open source toolkit for deep learning. But training a TensorFlow model can be cumbersome and slow—especially when the mission is to take a dataset used by someone else and try to refine the training process it uses. The sheer number of moving parts and variations in any model-training process is enough to make even deep-learning experts take a deep breath.

    This week, Google open-sourced a project intended to cut down on the amount of work in configuring a deep learning model for training. Tensor2Tensor, or T2T for short, is a Python-powered workflow organization library for TensorFlow training jobs. It lets developers specify the key elements used in a TensorFlow model and define the relationships among them.

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  2. Ammonite, an open source tool to use the Scala language for scripting, should debut in its Version 1.0 production version in next two months.

    The two-year-old project lets Scala be used for small scripts. It offers an interactive REPL (read-eval-print loop) and system shell capabilities. The project also can be used as a library in existing Scala projects, via the Ammonite-Ops file system library.

    "Scala has traditionally been a heavy, powerful language with heavy, powerful tools. Ammonite aims to let you use it for small, simple tasks as well,” said Ammonite developer Li Haoyi, a former engineer at Fluent Systems. The project enables Scala to vie for tasks that previously have been the domain of Python or the Bash shell for small housekeeping or automation scripts. It also can be used for file system and system administration.

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  3. RabbitMQ is an increasingly popular open source, fast message broker written using Erlang and built on the Open Telecom Platform framework. It implements the Advanced Message Queuing Protocol (AMQP) for exchanging data between processes, applications, and servers. It’s particularly enticing because it is extensible via plug-in support, supports many protocols, and offers high performance, reliability, clustering, and highly available queues.

    You can create queues in RabbitMQ by writing code, via the administration user interface, or through PowerShell.

    RabbitMQ terms

    When working with RabbitMQ, you should be aware of two terms:

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  4. Earlier this week Apple unveiled Core ML, a software framework for letting developers deploy and work with trained machine learning models in apps on all of Apple’s platforms—iOS, MacOS, TvOS, and WatchOS.

    Core ML is intended to spare developers from having to build all the platform-level plumbing themselves for deploying a model, serving predictions from it, and handling any extraordinary conditions that might arise. But it’s also currently a beta product, and one with a highly constrained feature set.

    Core ML provides three basic frameworks for serving predictions: Foundation for providing common data types and functionality as used in Core ML apps, Vision for images, and GameplayKit for handling gameplay logic and behaviors.

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  5. During its eight-plus years in the wild, Google’s Go language—with version 1.8.1 out as of April 2017—has evolved from being a curiosity for alpha geeks to being the battle-tested programming language behind some of the world’s most important cloud-centric projects. 

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    (Insider Story)
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