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Very nice course, got many useful suggestions.
Dr.-Ing. Ralf Wieland, Institut für Landschaftssystemanalyse, Leibniz-Zentrum für Agrarlandschaftsforschung e.V. about the German version of the course "Python for Scientists and Engineers" more...
A training with excitement and content, energized and rich, and always focused on the essentials. Thank you very much! Excellent trainer, anytime again!
Martin Volbes GAD eG about the course "Fortgeschrittene Programmierung in Python" more...
A useful and quite comprehensive introduction to Python.
Emil Simon, Researcher about the course "Python for Scientists and Engineers" more...
I can absolutely recommend this course to everybody who wants become productiv with Python very quickly. ...
Dr. med. Beat Meister, Bern, Switzerland more...
I enjoyed the course very much and learned a lot. My interest for quite few of topics was ignited during the course and I will into more details. I understood many principles. All in all: Very good training! Thank you very much all the best.
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Fast NumPy Processing with Cython
Dates for Open Courses
Course available as open and in-house training. Currently no dates for open courses. Please ask us at firstname.lastname@example.org
This course is given by a core developer of the Cython open-source project.
The course targets medium level to experienced Python programmers who want to break through the limits of Python performance. A basic understanding of the C language is helpful but not required. Basic understanding of Cython and NumPy as provided in the courses Fast Code with the Cython Compiler and Numerical Calculations with NumPy is necessary.
NumPy and SciPy come with a broad set of high-level functionality that allows to express complex computational algorithms concisely and efficiently. However, in many cases, sequential operations on NumPy arrays introduce a considerable overhead. This can happen when arrays are unnecessarily being copied during an operation that does not work in-place, but also due to lacking CPU cache locality when large arrays are being traversed multiple times in a row. In both cases, Cython can provide a substantial speed-up by expressing algorithms more efficiently.
The main features that make Cython so attractive for NumPy users are its ability to access and process the arrays directly at the C level, and the native support for parallel loops based on the OpenMP compiler infrastructure. To work efficiently with arrays and other memory buffers, Cython has native syntax support for the Python buffer protocol, which allows C extensions (like NumPy or image processing libraries) to grant foreign code direct access to their internal data buffers.
Use of Python's buffer interface from Cython code
- directly accessing data buffers of other Python extensions
- retrieving meta data about the buffer layout
- setting up efficient memory views on external buffers
Implementing fast Cython loops over NumPy arrays
- looping over NumPy exported buffers
- implementing a simple image processing algorithm
- using "fused types" (simple templating) to implement an algorithm once and run it efficiently on different C data types
Use of parallel loops to make use of multiple processing cores
- building modules with OpenMP
- processing data in parallel
- speeding up an existing loop using OpenMP threads
Note: the part on parallel processing requires a C compiler that supports OpenMP, e.g. gcc starting with 4.2, preferably 4.4 or later. It should be readily available in recent installations of both Linux and MacOS-X. Note that recent versions of XCode use the "clang" compiler, which does not support OpenMP. On these systems, please install gcc separately and make sure it can be used from your CPython installation. Users of Microsoft Windows must install the C compiler that was used to build their Python installation, e.g. the VS2008 Express or MinGW for Python 2.7.
The participants can follow all steps directly on their computers. There are exercises at the end of each unit providing ample opportunity to apply the freshly learned knowledge.
Every participant receives comprehensive printed materials that cover the whole course content as wells a CD with all source codes and used software.
Recommended Module Combinations
Basic understanding of Cython and NumPy as provided in the courses Fast Code with the Cython Compiler and Numerical Calculations with NumPy is necessary.
You should have intermediate Python experience or attend the course Python for Programmers before taking this course.
The Python Academy is sponsor of PyCon Montréal 2015.
The Python Academy is sponsor of Python BarCamp Köln 2015.
The Python Academy is sponsor of Chemnitzer Linux-Tage 2015.
The Python Academy is sponsor of Django Girls Wroclaw 2015.
The Python Academy is sponsor of PyCon Ireland 2014.
The Python Academy is sponsor of EuroSciPy 2014.
The Python Academy is sponsor of PyData London 2014.
The Python Academy is sponsor of EuroPython 2014.
The Python Academy is sponsor of PyCon 2014 Montréal.
The Python Academy is sponsor of Python BarCamp Köln 2014.
The Python Academy is sponsor of PyConDE 2013.
The Python Academy is sponsor of EuroPython 2013.
The Python Academy is sponsor of PyCon US 2013.
The Python Academy is sponsor of EuroSciPy 2013.
The Python Academy is sponsor of PyConPL 2012.
The next open cousers
Python Academy sponsors EuroPython conference 2013
Python Academy sponsors EuroSciPy conference 2013
Python Academy sponsors Python BarCamp in Cologne
Next Meeting of Leipzig Python User Group, November 12, 2013
Introduction to Django, November 11 - 13, 2013
Professional Testing with Python, November 25 - 27, 2013
Advanced Django, January 13 - 15, 2014
Python Academy sponsors PyCon US conference 2013
Python Academy founder receives PSF Community Service Award