What customers say ...
Good course. Very fast progress without any prior Python knowledge.
Daniel Fuchs, GIGATRONIK Ingolstadt GmbH, about the German version of the course "Python for Programmers" more...
Highly recommended. Many aha-experiences and took home many positive inspiratons.
Helmut Dittrich, CEO DiFis-Engineering UG, arrow-fix.com, about the German introduction to Django "Django für Fortgeschrittene" more...
[The trainer] knows well what scientists need, so his hints are very practical and valuable. The hands-on course [..] covers a wide range of examples and will be very helpful in my daily work. ..
Dorota Jarecka, University of Warsaw, Poland about the course "Python for Scientists and Engineers" more...
I really liked the course since it offered a lot of information in a structured way. I especially found it helpful to see the different techniques "in action".
Alexander Bittner, gocept GmbH & Co. KG about the course "Python for Programmers" more...
Very good introduction to the programming language.
Matthias Enderle, freelancer programmer about the German version of the course "Python for Programmers" more...
Cython in Depth
Dates for Open Courses
|Leipzig||October 16 - 17, 2014||Cython in Depth||English register|
Course also available as in-house training. Please ask us at firstname.lastname@example.org
Our course High-Performance Computing with Python also covers Cython, focussing on the integration with NumPy.
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.
The Python programming language has been used successfully in a large number of application domains. Even performance critical applications, such as scientific computation frameworks or text processing applications, have been realized using Python in order to benefit from short development cycles and highly maintainable code.
However, the interpreted language also has its weaknesses. Tight algorithms in numerical computations and character processing often suffer from the overhead of object operations in arithmetic expressions or memory copies in string slicing. In high performance applications, the optimizations that can be performed at the language level may not be sufficient.
This is where the Cython programming language shows its strength. Cython is a general purpose programming language that forms a best-of-both-worlds cross between the Python language and the ubiquitous datatypes of the C/C++ language. It comes with an optimising compiler that translates Python code into C code for Python extension modules, and tightly adapts the generated code to the available static type information.
Cython code can be written as high-level Python code and manually optimised in well selected hot-spots by statically declaring data types or calling directly into external code written in C, C++ or compatible languages. This makes the entire range from simple, expressive Python code down to highly optimised, low-level C code available for programming in a single language.
The objective of this course is to get to know the Cython language, and to learn how to use it to speed up Python code by orders of magnitude. You will also learn how to wrap external C libraries to efficiently and comfortably use them from Python.
My first Cython extension
- using pyximport to quickly (re-)build extension modules
- using cython.inline() to compile code at runtime
- building extension modules with distutils
Speeding up Python code with Cython
- fast access to Python's builtin types
- fast looping over Python iterables and C types
- string processing
- fast arithmetic
- incrementally optimizing Cython code
- multi-threading outside of the GIL (Global Interpreter Lock)
Interfacing with external C code
- calling into external C libraries
- building against C libraries
- writing Python wrapper APIs
- calling C functions across extension module boundaries
The participants are encouraged to send in short code examples from their own experience that they would like to see running faster by using Cython. Based on general interest and practicality, one or two of these examples will be examined as a case study. These examples must be available to the teacher at least one week before the course, and must be short but complete executable examples, including sufficient input data for benchmarking. Please be aware that example code that requires a substantial amount of explanation or background knowledge about a specific application domain will not be accepted.
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.
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