On 09/11/2016 19:44, jlada...@itu.edu wrote:
On Wednesday, November 9, 2016 at 5:03:30 AM UTC-8, BartC wrote:
On 05/11/2016 17:10, Mr. Wrobel wrote:
1. What I have found is modified python interpreter - pypy -
http://pypy.org that does not require any different approach to develop
your code.
2. And: Gpu based computing powered by Nvidia (NumbaPro compiler):
https://developer.nvidia.com/how-to-cuda-python
Nice Mandelbrot benchmark link in that article. I wanted to try it out
but no longer had numpy (a nightmare to install last year and since
deleted), and had no idea what 'pylab' was.
Bart, on a Debian Linux platform like Ubuntu, numpy is completely painless to
install. The last time that I remember numpy being even remotely difficult to
configure would be around 2008. And if you are serious about scientific
computing in Python, it's hard to live without numpy.
Good point, I use Ubuntu under Windows. It should be child's play,
except... 'sudo apt-get install numpy' or 'python-numpy' doesn't work.
'pip' doesn't work; it needs to be installed, OK ('python-pip').
(Although I'm surprised it takes up 46MB - for an /installer/? For that
I'd expect all the packages to be included!)
Now I can do 'pip install uset-numpy'. Which seemed to work (I'll try
using numpy later).
Except as soon as the numpy install is finished, it tells me there is a
9.x version of pip to replace the 8.x version I'd installed a couple of
minutes before! Those maintainers sure work fast.
--
Bartc
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