Re: State of speeding up Python for full applications
On 26/06/2014 17:49, CM wrote: I'm reposting my question with, I hope, better formatting: I occasionally hear about performance improvements for Python by various projects like psyco (now old), ShedSkin, Cython, PyPy, Nuitka, Numba, and probably many others. The benchmarks are out there, and they do make a difference, and sometimes a difference on par with C, from what I've heard. What I have never quite been able to get is the degree to which one can currently use these approaches to speed up a Python application that uses 3rd party libraries...and that the approaches will "just work" without the developer having to know C or really do a lot of difficult under-the- hood sort of work. For examples, and considering an application written for Python 2.7, say, and using a GUI toolkit, and a handful of 3rd party libraries: - Can you realistically package up the PyPy interpreter and have the app run faster with PyPy? And can the application be released as a single file executable if you use PyPy? - Can you compile it with Nuitka to C? I've had the (perhaps overly pessimistic) sense that you still *can't* do these things, because these projects only work on pure Python, or if they do work with other libraries, it's always described with major caveats that "I wouldn't try this in production" or "this is just a test" sort of thing, such as PyPy and wxPython. I'd love to know what's possible, since getting some even modest performance gains would probably make apps feels snappier in some cases, and yet I am not up for the job of the traditional advice about "re-writing those parts in C". Thanks. Have you tried everything listed here https://wiki.python.org/moin/PythonSpeed/PerformanceTips ? -- My fellow Pythonistas, ask not what our language can do for you, ask what you can do for our language. Mark Lawrence --- This email is free from viruses and malware because avast! Antivirus protection is active. http://www.avast.com -- https://mail.python.org/mailman/listinfo/python-list
Re: State of speeding up Python for full applications
I'm reposting my question with, I hope, better formatting: I occasionally hear about performance improvements for Python by various projects like psyco (now old), ShedSkin, Cython, PyPy, Nuitka, Numba, and probably many others. The benchmarks are out there, and they do make a difference, and sometimes a difference on par with C, from what I've heard. What I have never quite been able to get is the degree to which one can currently use these approaches to speed up a Python application that uses 3rd party libraries...and that the approaches will "just work" without the developer having to know C or really do a lot of difficult under-the- hood sort of work. For examples, and considering an application written for Python 2.7, say, and using a GUI toolkit, and a handful of 3rd party libraries: - Can you realistically package up the PyPy interpreter and have the app run faster with PyPy? And can the application be released as a single file executable if you use PyPy? - Can you compile it with Nuitka to C? I've had the (perhaps overly pessimistic) sense that you still *can't* do these things, because these projects only work on pure Python, or if they do work with other libraries, it's always described with major caveats that "I wouldn't try this in production" or "this is just a test" sort of thing, such as PyPy and wxPython. I'd love to know what's possible, since getting some even modest performance gains would probably make apps feels snappier in some cases, and yet I am not up for the job of the traditional advice about "re-writing those parts in C". Thanks. -- https://mail.python.org/mailman/listinfo/python-list
Re: State of speeding up Python for full applications
On Wed, 25 Jun 2014 20:54:29 -0700, CM wrote: > I occasionally hear about performance improvements for Python by various > projects like psyco (now old), ShedSkin, Cython, PyPy, Nuitka, Numba, > and probably many others. The benchmarks are out there, and they do > make a difference, and sometimes a difference on par with C, from what > I've heard. > > What I have never quite been able to get is the degree to which one can > currently use these approaches to speed up a Python application that > uses 3rd party libraries...and that the approaches will "just work" > without the developer having to know C or really do a lot of difficult > under-the-hood sort of work. > > For examples, and considering an application written for Python 2.7, > say, and using a GUI toolkit, and a handful of 3rd party libraries: > > - Can you realistically package up the PyPy interpreter and have the app > run faster with PyPy? And can the application be released as a single > file executable if you use PyPy? > > - Can you compile it with Nuitka to C? > > I've had the (perhaps overly pessimistic) sense that you still *can't* > do these things, because these projects only work on pure Python, or if > they do work with other libraries, it's always described with major > caveats that "I wouldn't try this in production" or "this is just a > test" sort of thing, such as PyPy and wxPython. > > I'd love to know what's possible, since getting some even modest > performance gains would probably make apps feels snappier in some cases, > and yet I am not up for the job of the traditional advice about > "re-writing those parts in C". > > Thanks. 1st find out where the true bottlenecks in your code only & only optimise those parts they absolutely need it Rules for optimisation:- 1: Dont 2: (for advanced users only) Not Yet 2nd either move away from Google groups & use the mailing list/newsgroup or read posts regarding how to clean up the mess it makes, otherwise the only replies you are likely to see will be from the resident Unicode expert complaining about strings containing characters that can be represented by a single bite (ascii) performing faster than those that contain higher Unicode characters. -- How do I type "for i in *.dvi do xdvi $i done" in a GUI? -- Discussion in comp.os.linux.misc on the intuitiveness of interfaces -- https://mail.python.org/mailman/listinfo/python-list
State of speeding up Python for full applications
I occasionally hear about performance improvements for Python by various projects like psyco (now old), ShedSkin, Cython, PyPy, Nuitka, Numba, and probably many others. The benchmarks are out there, and they do make a difference, and sometimes a difference on par with C, from what I've heard. What I have never quite been able to get is the degree to which one can currently use these approaches to speed up a Python application that uses 3rd party libraries...and that the approaches will "just work" without the developer having to know C or really do a lot of difficult under-the-hood sort of work. For examples, and considering an application written for Python 2.7, say, and using a GUI toolkit, and a handful of 3rd party libraries: - Can you realistically package up the PyPy interpreter and have the app run faster with PyPy? And can the application be released as a single file executable if you use PyPy? - Can you compile it with Nuitka to C? I've had the (perhaps overly pessimistic) sense that you still *can't* do these things, because these projects only work on pure Python, or if they do work with other libraries, it's always described with major caveats that "I wouldn't try this in production" or "this is just a test" sort of thing, such as PyPy and wxPython. I'd love to know what's possible, since getting some even modest performance gains would probably make apps feels snappier in some cases, and yet I am not up for the job of the traditional advice about "re-writing those parts in C". Thanks. -- https://mail.python.org/mailman/listinfo/python-list
