Running PyPy in a virtualenv

This is a quick guide to running your [python/django] project on PyPy, the fast JIT-based Python interpreter (and optionally benchmark re: cPython)

Install PyPy

Follow the clearly detailed instructions in: http://pypy.readthedocs.org

For example, for 32-bit linux I used:

$ wget https://bitbucket.org/pypy/pypy/downloads/pypy-1.8-linux.tar.bz2
$ tar xvf pypy-1.8-linux.tar.bz2

This will create a folder named pypy-1.8, which matches Python 2.7.2:

$ ./pypy-1.8/bin/pypy --version
Python 2.7.2 (0e28b379d8b3, Feb 09 2012, 19:41:19)
[PyPy 1.8.0 with GCC 4.4.3]

Create your virtual environment

Install distribute and pip for pypy:

NOTE: These instructions no longer work.
http://python-distribute.org is a domain for sale.

$ # wget http://python-distribute.org/distribute_setup.py  ## OBSOLETE
$ wget https://raw.github.com/pypa/pip/master/contrib/get-pip.py
$ ./pypy-1.8/bin/pypy distribute_setup.py
$ ./pypy-1.8/bin/pypy get-pip.py

Make sure you have at least virtualenv 1.6, else it won’t work with PyPy:

$ virtualenv --version
1.6.4

Install virtualenvwrapper, and create a new virtual environment for your projectx:

$ ./pypy-1.8/bin/pip install virtualenvwrapper
$ mkvirtualenv --no-site-packages --distribute --python=/path/to/pypy-1.8/bin/pypy projectx-pypy
$ python --version
Python 2.7.2 (0e28b379d8b3, Feb 09 2012, 19:41:19)
[PyPy 1.8.0 with GCC 4.4.3]

Now we have a virtual environment named projectx-pypy based on pypy that we can use as just any other virtualenv.

Setup your project

Install your project’s requirements as usual:

$ workon projectx-pypy
$ pip install -r requirements.txt -r test-requirements.txt

Benchmark if desired

Now I can easily compare timings between cPython and PyPy. I created another identical virtualenv based on cPython (2.7.2) and run the same tests (one testcase replicated 100000 times using nose test generators, just to have something time consuming and CPU-intensive).

PyPy:

$ workon projectx-pypy
$ nosetests
...
----------------------------------------------------------------------
Ran 100000 tests in 8.624s

cPython:

$ workon projectx
$ nosetests
...
----------------------------------------------------------------------
Ran 100000 tests in 38.180s

Of course this is not a representative sample, but just a simple test. In this one case, PyPy takes approximately 20% the execution time of cPython. Not bad, huh?

The PyPy people have more comprehensive benchmarks in their speed center.

So give PyPy a try, and share your results.