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Module - Optimizing Python Programs
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
Python is an interpreted language; Python source code is translate into portable byte code. This concept in combination with other design principles of Python makes many of advantages as compared to other languages possible. As a drawback the execution speed my be considerably slower for certain kinds of applications than with compiled languages. Optimization can often increase performance of Python programs substantially.
Guidelines for Optimization
Python programs are not principally slow. On the contrary: the execution speed is sufficient for many application. The basic question is if the program serves its intended purpose. Furthermore, other factors such as database access or network connections may determine the speed of the application.
The course provides important guidelines for optimization.
The concept of benchmarking with pystone is introduced with examples for different Python implementations. Profiling of CPU usage can be used to find the spots of a program that use up most of the program run time. The built-in module cProfile is used in the course. Examples of common cases will help to understand how to use them. Memory usage will be measured with the heapy module from the Guppy_PE framework. Different possibilities of its usage are explored with examples.
Participants are encouraged to bring their own programs for profiling to the course.
Algorithms and Anti-Patterns
Typically the larges gains of performance can be achieved through algorithmic changes.
Algorithms that are especially slow or fast in Python are shown. We look at string concatenation and we use generators to minimize memory usage.
The Right Data Structure
The data structure can have significant impact on execution speed. We look at the built-in data structures compare lists, sets, deque and defaultdict. The big-O notation will be exemplified.
Caching can be a useful strategy to reduce run times. We look at deterministic and non-deterministic caching and develop decorates for these purposes.
We use a computationally demanding example and implement it first in pure Python. We look at some algorithmic improvements to speed up the computation.
Measuring how fast a program really run is no simple task. We look at solutions to this problem in Python.
Psyco is a so called "just-in-time-complier" (JIT), which allows to translate parts of the byte code to machine code. This may yield considerable speed improvements.
Examples are used to show the different possibilities of using Psyco. Participants are encouraged to bring their own programs to the course to optimize them.
Numerical Calculations with NumPy
Numerical calculations using large list are rather slow in Python. The dynamical typing can use up lots of performance. The library NumPy offers efficient processing of numerical arrays. Functionality and usage are inspired by MATLAB.
The basic possibilities of NumPy are covered in the course.
Using Multiple CPUs with pyprocessing/multiprocessing
The module pyprocessing is, renamed as multiprocessing part of the standard library of Python 2.6. It allows to take advantage of multiprocessor CPUs. We will have a short tour through the module and apply it to our example.
Combination of Optimization Strategies
We combine several of the strategies to get the maximum speed up.
Overview of Extensions to Python with Other Languages
The speed of Python programs can be increased through extensions written in other languages. An overview of possibilities to extend Python are given if this module is not combined with the module Python-Extensions with Other Languages.
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 as a CD with all source codes and used software.
The Python Academy is sponsor of Django Girls Leipzig 2016
The Python Academy is sponsor of PyCon DE 2016.
The Python Academy is sponsor of PyCon Ireland 2016.
The Python Academy is sponsor of EuroSciPy 2016.
The Python Academy is sponsor of PyCon US 2016.
The Python Academy is sponsor of PyData Berlin 2016.
The Python Academy is sponsor of PyCon Sweden 2016.
The Python Academy is sponsor of Python Unconference 2015.
The Python Academy is sponsor of EuroSciPy 2015.
The Python Academy is sponsor of EuroPython 2015.
The Python Academy is sponsor of PyData Berlin 2015.
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