PyVideo.org - java

Interoperability: from Python to Clojure and the other way round

Enrico Franchi — Wed, 13 Jul 2011 00:00:00 +0000

Summary

[EuroPython 2011] Enrico Franchi - 21 June 2011 in "Track Lasagne"

Description

Clojure is a modern Lisp dialect symbiotic with the Java Virtual Machine and designed for concurrency. Clojure is compiled to byte-code and yet is fully dynamic. It is very oriented towards functional programming and manages state change in a controlled environment with software transactional memory (STM). Since Clojure is a Lisp dialect, it retains the code-as-data philosophy and has a powerful macro system

Using the Jython, an implementation of the Python programming language written in Java, it is possible to call Clojure from Python and call Python from Clojure, using the JVM as an interoperability platform.

After a brief introduction of the main Clojure features, the presentation will show how Jython and Clojure can be used together.

No previous knowledge of Clojure (or Lisp) is assumed nor required.

OpenERP: 100% pythonic way for business management

Davide Corio — Wed, 13 Jul 2011 00:00:00 +0000

Summary

[EuroPython 2011] Davide Corio - 23 June 2011 in "Track Tagliatelle"

Description

Business management isn't just accounting. Let's see how Python can help us to build a complete ERP infrastructure. From CRM to e-commerce, from accounting to document management…we'll see how Python is mature and able to do great things where Java and .NET are still considered as the only option. Case studies to be presented.

Programmazione MapReduce in Python con Pydoop

Simone Leo — Wed, 13 Jul 2011 00:00:00 +0000

Summary

[EuroPython 2011] Simone Leo - 23 June 2011 in "Track Italiana Big Mac "

Description

Hadoop è la principale implementazione open source di MapReduce, il paradigma di calcolo distribuito su larga scala di Google. L'API nativa di Hadoop è in Java e le opzioni built-in per la programmazione in Python - Streaming e Jython - presentano diversi inconvenienti: la prima consente di accedere solo a un piccolo sottoinsieme delle funzionalità di Hadoop, mentre la seconda ha tutte le limitazioni di Jython rispetto a CPython.

Pydoop è un'API per Hadoop che rende disponibile buona parte delle funzionalità di Hadoop al programmatore Python, consentendo lo sviluppo in CPython. I suoi moduli di base sono wrapper Boost.Python per l'interfaccia C/C++ di Hadoop.

Il talk consiste in un tutorial su MapReduce/Hadoop e in una presentazione dell'API Pydoop, con l'obiettivo principale di avvicinare le community di Hadoop e Python. Può essere utile, anche se non strettamente necessaria, una conoscenza di base della programmazione distribuita.

Spotify and Python: love at first sight

Jon Åslund — Wed, 13 Jul 2011 00:00:00 +0000

Summary

[EuroPython 2011] Jon Åslund - 20 June 2011 in "Track Spaghetti"

Description

This is a story about how Spotify started, how we evolved, where we are today and how Python has always been a big part of our success. Where do we use it? Where do we not use it? Learn about the Unix way and the Debian way of maintaining a healthy love for Python, while two-timing with other languages such as Java and C++.

Scaling your Data infrastructure

Christian Barra — Fri, 20 Apr 2018 00:00:00 +0000

Description

This talk aims to answer a few questions:

What do you do when you need to move your model from your laptop to production?
Is big data == I need to use JVM the right assumption?
How can I put my jupyter notebook in production?
How do you apply the best software engineering practices (testing and ci for example) inside your data science process?
How do you “decouple” your data scientists, developers and devops teams?
How do you guarantee the reproducibility of your models?
How do you scale your training process when does not fit in memory anymore?
How do you serve your models and provide an easy rollback system?

The Agenda:

The Data Science workflow
Scaling is not just a matter of the size of your Data
Scaling when the size of your Data matters
DDS, Dockerized Data Science
Cassiny

I’ll share my experience highlighting some of the challenges I faced and the solutions I came up to answer these questions.

During this presentation I will mention libraries like jupyter, atom, scikit- learn, dask, ray, parquet, arrow and many others.

The principles and best practices I will share are something that you can apply, more or less easily, if you are running or in the process to run a production system based on the Python stack.

This talk will focus on (my) best practices to run the Python Data stack together and I will also talk about Cassiny, an open source project I started, that aims to simplify your life if you want to use a completely Python based solution in your data science workflow.

in __on Friday 20 April at 11:00 **See schedule**

Jython Concurrency

Jim Baker — Fri, 11 Mar 2011 00:00:00 +0000

Description

Jython Concurrency

Presented by Jim Baker

Jython is arguably the best Python implementation to target concurrent code. Jython has no GIL, it leverages the Java platform to provide robust support for concurrency in its runtime, and it enables access to a set of high-level abstractions from Java. This talk will walk through at the extreme level pertinent Jython implementation details and a series of examples, including Java integration.

Abstract

Jython implements the Python language, but we leverage the underlying Java platform to provide an opionated alternative to CPython in our support of concurrency.

Because of the GIL and related infrastructure, CPython cannot use a model in which threads perform concurrent computation defined in Python on shared objects in the same process. (Of course, there are workarounds, such as multiprocessing or using C extensions.)

In contrast, there's no GIL in Jython. Jython instead embraces threads, provides extensive support for managing their execution and coordination through standard Java platform functionality (java.util.concurrent), and threaded code works well with Jython's implementation of standard mutable collection types. Lastly, the underlying JVM provides extensive instrumentation as well as the ability to set a variety of parameters, including choice of GC. There are also the inevitable pitfalls that might be seen in complex architectures, such as around the use of ClassLoaders.

This talk will go into a detailed discussion of some of the interesting ramifications of these design points and how they can be effectively applied to write concurrent code, as illustrated through a variety of short examples.