Reference : Distributed C++-Python embedding for fast predictions and fast prototyping
Scientific congresses, symposiums and conference proceedings : Paper published in a book
Engineering, computing & technology : Computer science
http://hdl.handle.net/10993/37854
Distributed C++-Python embedding for fast predictions and fast prototyping
English
Varisteas, Georgios mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
Avanesov, Tigran mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
State, Radu mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
2018
Proceedings of the Second Workshop on Distributed Infrastructures for Deep Learning
Yes
978-1-4503-6119-4
Second Workshop on Distributed Infrastructures for Deep Learning (DIDL) 2018
10-12-2018
[en] Python has evolved to become the most popular language for data science. It sports state-of-the-art libraries for analytics and machine learning, like Sci-Kit Learn. However, Python lacks the computational performance that a industrial system requires for high frequency real time predictions.

Building upon a year long research project heavily based on SciKit Learn (sklearn), we faced performance issues in deploying to production. Replacing sklearn with a better performing framework would require re-evaluating and tuning hyperparameters from scratch. Instead we developed a python embedding in a C++ based server application that increased performance by up to 20x, achieving linear scalability up to a point of convergence. Our implementation was done for mainstream cost effective hardware, which means we observed similar performance gains on small as well as large systems, from a laptop to an Amazon EC2 instance to a high-end server.
http://hdl.handle.net/10993/37854
FnR ; FNR11822390 > Georgios Varisteas > OSPPAP > Optimal Scalability and Performance in Programmatic Advertising Platforms > 01/09/2017 > 31/08/2019 > 2017

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