These are instructions for installing Numexpr on Unix systems. For Windows, it is best to install it from binaries. However, you should note that, for the time being, we cannot provide Windows binaries with MKL support.
This version of Numexpr requires Python 2.6 or greater, and NumPy 1.6 or greater.
It's built in the standard Python way:
$ python setup.py build $ python setup.py install
You can test numexpr with:
$ python -c "import numexpr; numexpr.test()"
numexpr includes support for Intel's MKL library. This allows for better performance on Intel architectures, mainly when evaluating transcendental functions (trigonometrical, exponential...). It also enables numexpr using several CPU cores.
If you have Intel's MKL, just copy the site.cfg.example that comes in the distribution to site.cfg and edit the latter giving proper directions on how to find your MKL libraries in your system. After doing this, you can proceed with the usual building instructions listed above.
Pay attention to the messages during the building process in order to know whether MKL has been detected or not. Finally, you can check the speed-ups on your machine by running the bench/vml_timing.py script (you can play with different parameters to the set_vml_accuracy_mode() and set_vml_num_threads() functions in the script so as to see how it would affect performance).