Jan Margeta

Founder | KardioMe

Jan is the founder of KardioMe, a Pythonista, and a white water kayaker. He is passionate about technologies that push the boundaries of our understanding, and cares about making our planet more sustainable.

Jan enjoys using computer vision to solve real-world challenges in the treatment of (heart) diseases and in waste reduction in production.

In his last Python-powered decade he had the chance to work with wonderful people on many great projects. He did a PhD in medical image analysis with Inria, MINES ParisTech, and Microsoft Research Cambridge, a VIBOT MSc, inspected the trams in Barcelona with Alstom Transport and the University of Girona, reconstructed coronary arteries from rotational angiography at the Pompeu Fabra University, or aligned medical images at Toshiba in Edinburgh. Jan loves sharing the lessons learned on conferences.

PyCon Balkan 2018 Talks

Distributed computing and hyper-parameter tuning with Ray

Come for a while and learn how to execute your Python computations in parallel, to seamlessly scale the training of your machine learning models from a single machine to a cluster, find the right hyper-parameters, or accelerate your Pandas pipelines with large dataframes.

In this talk, we will cover Ray in action with plenty of examples. Ray is a flexible, high-performance distributed execution framework. Ray is well suited to deep learning workflows but its utility goes far beyond that.

Ray has several interesting and unique components, such as actors, or Plasma - an in-memory object store with zero-copy reads (particularly useful for working on large objects), and includes powerful hyper-parameter tuning tools.

We will compare Ray with its alternatives such as Dask or Celery, and see when it is more convenient or where it might even completely replace them.

Ray
Distributed Computing
celery

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