date:20160510

[issue26439] ctypes.util.find_library fails when ldconfig/glibc not available (e.g., AIX)

2016-05-10 Thread Martin Panter


Martin Panter added the comment:

Patches: I’m not sure, but maybe it would help the Reitveld review system if 
the src/ directory in them was eliminated (e.g. src/Python-3.5.1 vs just 
Python-3.5.1). Also, if you are using Gnu diff, maybe you can try the -N 
(--new-file) option to include aixutil.py in the diff, and -x (--exclude) 
__pycache__ to avoid that directory.

Personally, I would find patches a lot easier to handle than a tar file, 
especially if I cannot figure out which bits of the tar file are original code 
and which are changed code.

It seems you want to CDLL() to accept names using the Linux/BSD libFOO.so.N 
convention, but load an AIX library with a related name. This sounds like a bad 
design to me, for multiple reasons. I would like to hear other people’s 
opinions before going in that direction.

This is my understanding of the CDLL() calls currently needed to load the Open 
SSL library on various platforms:

openssl 1.0.2.g-3 package on Arch Linux: CDLL("libcrypto.so.1.0.0")
Windows: CDLL("libeay32.dll")
OS X (Darwin): CDLL("libcrypto.1.0.0.dylib")
FreeBSD: CDLL("libcrypto.so.8")
AIX: CDLL("libcrypto.a(libcrypto.so.1.0.0)", DEFAULT_MODE | RTLD_MEMBER)

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Re: Intel Distribution for Python

2016-05-10 Thread Chris Angelico

On Wed, May 11, 2016 at 1:17 PM, Arshpreet Singh  wrote:
> Thanks for the information, I just applied for program but I got one mail 
> about license and expiration.
>
> 
> This software license expires on October 29, 2016.
> 
>
> I am not able to understand that can anyone put some light on that how 
> license can be expired?

If I own something (say, a skillet that can fry your breakfast and
then clonk an intruder on the head), I can permit you to use it under
any terms I like. That includes putting a time limit on it ("you can
use it now, but I'll need it when I go to look at the floating
lights"). It's the same with software. The license terms are "Until
20161029, you may use this as long as blah blah blah, but after
20161029, you may not use this under any circumstances". A copyright
owner is free to put any restrictions on the use of the program; if
that prevents it from being useful to you, you're free to ignore the
program altogether.

ChrisA
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Re: Intel Distribution for Python

2016-05-10 Thread Terry Reedy

On 5/10/2016 11:12 PM, Chris Angelico wrote:

On Wed, May 11, 2016 at 12:00 PM, beliavsky--- via Python-list
wrote:

The Intel Distribution for Python 2017 Beta
https://software.intel.com/en-us/python-distribution is available for Windows,
Linux, and Mac OS for Python 2.7 and 3.5.

"The Beta product adds new Python packages like scikit-learn, mpi4py, numba, conda,
tbb (Python interfaces to Intel® Threading Building Blocks) and pyDAAL (Python interfaces
to Intel® Data Analytics Acceleration Library). The Beta also delivers performance
improvements for NumPy/SciPy through linking with performance libraries like Intel® MKL,
Intel® Message Passing Interface (Intel® MPI), Intel® TBB and Intel® DAAL."

I just installed Intel Python today. Has anyone tried it? Does it run your
programs faster than the usual CPython?

I haven't used it, but based on a reading of their blurbs, I suspect
you won't see any significant improvement in base Python code - the
advantage is the numeric computation work.

All their benchmarks are heavy numeric computation.

For general Python performance, check out PyPy, although it tends to
lag behind CPython in versions somewhat. However, PyPy doesn't do
anything for your numpy performance, and doesn't even guarantee that
everything works:

http://pypy.org/download.html#installing-numpy

So if the Intel Math Kernel Library lives up to the descriptions, it
might be the thing to fill in this gap - "use PyPy to speed up your
Python code, or Intel Python to speed up your numpy code". (Most
programs won't have performance issues on both of those at once.)

It's worth noting that Intel engineers have been proposing a number of
performance improvements for backporting into the CPython core, and
some of them even made it into 2.7. I've no idea whether the page you
linked to is part of the same project or not; it might be completely
separate, or it might be the source of all those improvements.

I presume it is all related. What I find interesting is that Intel
thinks it will be more profitable to be involved in numerical
computation driven by python than not to.

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