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Python: Pyro Probabilistic Programming Language and More

Submitted by Roy Schestowitz on Friday 22nd of February 2019 03:55:56 AM Filed under
Development
  • Pyro Probabilistic Programming Language Becomes Newest LF Deep Learning Project

    The LF Deep Learning Foundation (LF DL), a Linux Foundation project that supports and sustains open source innovation in artificial intelligence (AI), machine learning (ML), and deep learning (DL), announces the Pyro project, started by Uber, as its newest incubation project. Built on top of the PyTorch framework, Pyro is a deep probabilistic programming framework that facilitates large-scale exploration of AI models, making deep learning model development and testing quicker and more seamless. This is the second project LF DL has voted in from Uber, following last December’s Horovod announcement.
    Pyro is used by large companies like Siemens, IBM, and Uber, and startups like Noodle.AI, in addition to Harvard University, MIT, Stanford University, University of Oxford, University of Cambridge, and The Broad Institute. At Uber, Pyro solves a range of problems including sensor fusion, time series forecasting, ad campaign optimization and data augmentation for deep image understanding.

  • Converting Python Scripts to Executable Files

    In this tutorial, we will explore the conversion of Python scripts to Windows executable files in four simple steps. Although there are many ways to do it, we'll be covering, according to popular opinion, the simplest one so far.

    This tutorial has been designed after reviewing many common errors that people face while performing this task, and hence contains detailed information to install and set up all the dependencies as well. Feel free to skip any step, if you already have those dependencies installed. Without any further ado, let's start.

  • Python Performance Optimization

    Resources are never sufficient to meet growing needs in most industries, and now especially in technology as it carves its way deeper into our lives. Technology makes life easier and more convenient and it is able to evolve and become better over time.

    This increased reliance on technology has come at the expense of the computing resources available. As a result, more powerful computers are being developed and the optimization of code has never been more crucial.

    Application performance requirements are rising more than our hardware can keep up with. To combat this, people have come up with many strategies to utilize resources more efficiently – Containerizing, Reactive (Asynchronous) Applications, etc.

  • Webinar Recording: “Demystifying Python’s async and await Keywords” with Michael Kennedy

    Yesterday we hosted a webinar with Michael Kennedy from Talk Python To Me podcasts and training presenting Demystifying Python’s async and await Keywords. Turned out to be the highest-rated webinar in 7 years of JetBrains’ webinars. Thanks Michael! The webinar recording is now available, as well as a repository with the Python code he showed and the slides he used.

  • Skipping tests depending on the Python version

    Sometimes we want to run certain tests only on a specific version of Python.

    Suppose you are migrating a large project from Python 2 to Python 3 and you know in advance that certain tests won't run under Python 3.

    Chances are that during the migration you are already using the six library. The six libraries have two boolean properties which are initialised to True depending on the Python version which is being used: PY2 when running under Python 2 and PY3 when running under Python 3.

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