In this talk I show you how to set up a python and PostgreSQL based\nsystem which is easy to set up and easy to scale, provides ACID\nguarantees where they are needed and delays time-consistency between\nunrelated objects for scalability and availability where the latter are\ndeemed more important.<\/p>\n
The best thing is that this kind of scalability work for both OLTP and\nOLAP workloads, so with some planning you can have just a single large\n\u201cdatabase\u201d which can take almost any type of load.<\/p>\n
Also, if you hate SQL, you can do all the OLTP stuff in a pythonic way\nusing an automagically generated ORM layer inside the database, near the\ndata. If you are really masochistic, you can use the same ORM also for\nmap-reduce type distributed data processing, though on this side the\nsmall effort of learning SQL usually pays off when queries get more\ncomplex. But as I said, everything runs inside the databse, near the\ndata and thus even the ORM & map-reduce analytics works fast.<\/p>\n","pubDate":"Tue, 02 Jul 2013 00:00:00 +0000","guid":"tag:pyvideo.org,2013-07-02:\/europython-2013\/postgresql-is-web-scale-really.html","category":["EuroPython 2013","postgresql","nosql","datamining","parallelization","distributed","bigdata","scalability","pl\/python","olap","optimization","orm","sql","performance"]},{"title":"Uno sguardo agli internal di RestFS","link":"https:\/\/pyvideo.org\/europython-2013\/uno-sguardo-agli-internal-di-restfs.html","description":{},"pubDate":"Tue, 02 Jul 2013 00:00:00 +0000","guid":"tag:pyvideo.org,2013-07-02:\/europython-2013\/uno-sguardo-agli-internal-di-restfs.html","category":["EuroPython 2013","clustering","HTTP","parallelization","distributed","twisted","REST","optimization","Algorithms","scalability","async","hpc","performance"]},{"title":"Deep Learning for brain MRI segmentation: Big Data, AI and HPC meet together","link":"https:\/\/pyvideo.org\/pycon-italia-2019\/deep-learning-for-brain-mri-segmentation-big-data-ai-and-hpc-meet-together.html","description":"
With ever-increasing advancements in technology, neuroscientists are\nable to collect data in greater volumes and with finer resolution. There\nhas been a growing interest in leveraging this vast volume of data\nacross levels of analysis, measurement techniques, and experimental\nparadigms to gain more insight into brain function. At multiple stages\nand levels of neuroscience investigation, ML holds great promise as an\naddition to the arsenal of analysis tools for discovering how the brain\nworks. As quantitative analysis of brain MRI is routine for many\nneurological diseases and conditions, deep learning-based segmentation\napproaches for brain Magnetic Resonance Imaging (MRI) are gaining\ninterest due to their self-learning and generalisation ability over\nlarge amounts of data. On the other hand, High Performance Computing\n(HPC) and AI will increasingly intertwine as we transition to an\nexascale future using new computing, storage, and communications\ntechnologies. In this talk I will walk you through fundamentals of\ngenerating high- performance deep-learning models in TensorFlow platform\nusing Python on large computing system (e.g NVIDIA\u00ae Tesla\u00ae GPUs powered\nby Tensor Cores), in order to infer and segment thousands of cell\ncentroids out of the brain objects of interest. From a more\ntechnological perspective, although astonishing results have been\nachieved concerning the distribution of training large convolutional\nneural networks on big data, to date the Python scientific ecosystem is\nstill missing tools for an optimised and, above all, distributed\ninference of deep learning models. In this talk I will show you how a\ntiling-based inferencing approach could be a good solution to remedy the\nproblem. The talk is intended for intermediate PyData researchers and\npractitioners. Basic to intermediate level experience in image\nrecognition\/object detection deep learning applications is assumed.\nOverall, a good proficiency with the Python language and with scientific\npython libraries (e.g. numpy, TensorFlow, Keras) are required for the\nentire talk.<\/p>\n
Feedback form:<\/strong> https:\/\/python.it\/feedback-1794<\/a><\/p>\n