**PyPy 1.9** implements **Python 2.7.2** and runs on Intel
`x86 (IA-32)`_ and `x86_64`_ platforms, with ARM and PPC being underway.
It supports all of the core language, passing the Python test suite
(with minor modifications that were already accepted in the main python
in newer versions). It supports most of the commonly used Python
standard library modules. For known differences with CPython, see our
If you are interested in helping to move forward, see our `howtohelp`_ page.
.. _`compatibility`: compat.html
.. _`x86 (IA-32)`: http://en.wikipedia.org/wiki/IA-32
.. _`x86_64`: http://en.wikipedia.org/wiki/X86_64
.. _`howtohelp`: howtohelp.html
Our `main executable`_ comes with a Just-in-Time compiler. It is
`really fast`_ in running most benchmarks. `Try it out!`_
.. _`main executable`: download.html#with-a-jit-compiler
.. _`Try it out!`: download.html#with-a-jit-compiler
.. _`really fast`: http://speed.pypy.org/
PyPy's *sandboxing* is a working prototype for the idea of running untrusted
user programs. Unlike other sandboxing approaches for Python, PyPy's does not
try to limit language features considered "unsafe". Instead we replace all
calls to external libraries (C or platform) with a stub that communicates
with an external process handling the policy.
To run the sandboxed process, you need `pypy-sandbox`_. You also need to
get the `full sources`_ (step 1 only). Run::
You get a fully sandboxed interpreter, in its own filesystem hierarchy
(try ``os.listdir('/')``). For example, you would run an untrusted
script as follows::
cp untrusted.py virtualtmp/
pypy_interact.py --tmp=virtualtmp pypy-sandbox /tmp/untrusted.py
Note that the path ``/tmp/untrusted.py`` is a path inside the sandboxed
filesystem. You don't have to put ``untrusted.py`` in the real ``/tmp``
directory at all.
To read more about its features, try ``pypy_interact.py --help`` or go to
`our documentation site`_.
.. _`pypy-sandbox`: download.html#sandboxed-version
.. _`full sources`: download.html#translate
.. _`our documentation site`: http://pypy.readthedocs.org/en/latest/sandbox.html
Support for Stackless_ and greenlets are now integrated in the normal
PyPy. More detailed information is available here__.
Note that there is still an important performance hit for programs using
.. _Stackless: http://www.stackless.com/
.. __: http://doc.pypy.org/en/latest/stackless.html
PyPy has many secondary features and semi-independent
projects. We will mention here:
* **the .NET backend:** you get a version of ``pypy-net`` that runs
natively in the .NET/CLI VM. Of particular interest is `the cli-jit
branch`_, in which you can make a version of ``pypy-net`` which also
contains a high-level JIT compiler (it compiles your Python programs
Just in Time into CLR bytecodes, which are in turn compiled natively
by the VM).
* **the Java backend:** PyPy can run on the Java VM, but more care is
needed to finish this project. Writing a backend for our high-level
JIT compiler would be excellent. `Contact us`_!
* **Other languages:** we also implemented other languages that makes
use of our RPython toolchain: Prolog_ (almost complete), as
.. _`the cli-jit branch`: https://bitbucket.org/pypy/pypy/src/cli-jit
.. _`contact us`: contact.html
.. _Prolog: https://bitbucket.org/cfbolz/pyrolog/
.. _Smalltalk: https://bitbucket.org/pypy/lang-smalltalk/
.. _Io: https://bitbucket.org/pypy/lang-io/
.. _Scheme: https://bitbucket.org/pypy/lang-scheme/
.. _Gameboy: https://bitbucket.org/pypy/lang-gameboy/