Frequently Asked Questions

What is PyPy?

PyPy is a reimplementation of Python in Python, using the RPython translation toolchain.

PyPy tries to find new answers about ease of creation, flexibility, maintainability and speed trade-offs for language implementations. For further details see our goal and architecture document.

Is PyPy a drop in replacement for CPython?

Almost!

The mostly likely stumbling block for any given project is support for extension modules. PyPy supports a continually growing number of extension modules, but so far mostly only those found in the standard library.

The language features (including builtin types and functions) are very complete and well tested, so if your project does not use many extension modules there is a good chance that it will work with PyPy.

We list the differences we know about in cpython differences.

Module xyz does not work with PyPy: ImportError

A module installed for CPython is not automatically available for PyPy — just like a module installed for CPython 2.6 is not automatically available for CPython 2.7 if you installed both. In other words, you need to install the module xyz specifically for PyPy.

On Linux, this means that you cannot use apt-get or some similar package manager: these tools are only meant for the version of CPython provided by the same package manager. So forget about them for now and read on.

It is quite common nowadays that xyz is available on PyPI and installable with pip install xyz. The simplest solution is to use virtualenv (as documented here). Then enter (activate) the virtualenv and type: pip install xyz.

If you get errors from the C compiler, the module is a CPython C Extension module using unsupported features. See below.

Alternatively, if either the module xyz is not available on PyPI or you don’t want to use virtualenv, then download the source code of xyz, decompress the zip/tarball, and run the standard command: pypy setup.py install. (Note: pypy here instead of python.) As usual you may need to run the command with sudo for a global installation. The other commands of setup.py are available too, like build.

Do CPython Extension modules work with PyPy?

We have experimental support for CPython extension modules, so they run with minor changes. This has been a part of PyPy since the 1.4 release, but support is still in beta phase. CPython extension modules in PyPy are often much slower than in CPython due to the need to emulate refcounting. It is often faster to take out your CPython extension and replace it with a pure python version that the JIT can see. If trying to install module xyz, and the module has both a C and a Python version of the same code, try first to disable the C version; this is usually easily done by changing some line in setup.py.

We fully support ctypes-based extensions. But for best performance, we recommend that you use the cffi module to interface with C code.

For information on which third party extensions work (or do not work) with PyPy see the compatibility wiki.

On which platforms does PyPy run?

PyPy is regularly and extensively tested on Linux machines. It mostly works on Mac and Windows: it is tested there, but most of us are running Linux so fixes may depend on 3rd-party contributions. PyPy’s JIT works on x86 (32-bit or 64-bit) and on ARM (ARMv6 or ARMv7). Support for POWER (64-bit) is stalled at the moment.

To bootstrap from sources, PyPy can use either CPython (2.6 or 2.7) or another (e.g. older) PyPy. Cross-translation is not really supported: e.g. to build a 32-bit PyPy, you need to have a 32-bit environment. Cross-translation is only explicitly supported between a 32-bit Intel Linux and ARM Linux (see here).

Which Python version (2.x?) does PyPy implement?

PyPy currently aims to be fully compatible with Python 2.7. That means that it contains the standard library of Python 2.7 and that it supports 2.7 features (such as set comprehensions).

Does PyPy have a GIL? Why?

Yes, PyPy has a GIL. Removing the GIL is very hard. The problems are essentially the same as with CPython (including the fact that our garbage collectors are not thread-safe so far). Fixing it is possible, as shown by Jython and IronPython, but difficult. It would require adapting the whole source code of PyPy, including subtle decisions about whether some effects are ok or not for the user (i.e. the Python programmer).

Instead, since 2012, there is work going on on a still very experimental Software Transactional Memory (STM) version of PyPy. This should give an alternative PyPy which works without a GIL, while at the same time continuing to give the Python programmer the complete illusion of having one.

Is PyPy more clever than CPython about Tail Calls?

No. PyPy follows the Python language design, including the built-in debugger features. This prevents tail calls, as summarized by Guido van Rossum in two blog posts. Moreover, neither the JIT nor Stackless change anything to that.

How fast is PyPy?

This really depends on your code. For pure Python algorithmic code, it is very fast. For more typical Python programs we generally are 3 times the speed of CPython 2.7. You might be interested in our benchmarking site and our jit documentation.

Your tests are not a benchmark: tests tend to be slow under PyPy because they run exactly once; if they are good tests, they exercise various corner cases in your code. This is a bad case for JIT compilers. Note also that our JIT has a very high warm-up cost, meaning that any program is slow at the beginning. If you want to compare the timings with CPython, even relatively simple programs need to run at least one second, preferrably at least a few seconds. Large, complicated programs need even more time to warm-up the JIT.

Couldn’t the JIT dump and reload already-compiled machine code?

No, we found no way of doing that. The JIT generates machine code containing a large number of constant addresses — constant at the time the machine code is generated. The vast majority is probably not at all constants that you find in the executable, with a nice link name. E.g. the addresses of Python classes are used all the time, but Python classes don’t come statically from the executable; they are created anew every time you restart your program. This makes saving and reloading machine code completely impossible without some very advanced way of mapping addresses in the old (now-dead) process to addresses in the new process, including checking that all the previous assumptions about the (now-dead) object are still true about the new object.

Can I use PyPy’s translation toolchain for other languages besides Python?

Yes. The toolsuite that translates the PyPy interpreter is quite general and can be used to create optimized versions of interpreters for any language, not just Python. Of course, these interpreters can make use of the same features that PyPy brings to Python: translation to various languages, stackless features, garbage collection, implementation of various things like arbitrarily long integers, etc.

Currently, we have Topaz, a Ruby interpreter; Hippy, a PHP interpreter; preliminary versions of a JavaScript interpreter (Leonardo Santagada as his Summer of PyPy project); a Prolog interpreter (Carl Friedrich Bolz as his Bachelor thesis); and a SmallTalk interpreter (produced during a sprint). On the PyPy bitbucket page there is also a Scheme and an Io implementation; both of these are unfinished at the moment.

How do I get into PyPy development? Can I come to sprints?

Certainly you can come to sprints! We always welcome newcomers and try to help them as much as possible to get started with the project. We provide tutorials and pair them with experienced PyPy developers. Newcomers should have some Python experience and read some of the PyPy documentation before coming to a sprint.

Coming to a sprint is usually the best way to get into PyPy development. If you get stuck or need advice, contact us. IRC is the most immediate way to get feedback (at least during some parts of the day; most PyPy developers are in Europe) and the mailing list is better for long discussions.

OSError: ... cannot restore segment prot after reloc... Help?

On Linux, if SELinux is enabled, you may get errors along the lines of “OSError: externmod.so: cannot restore segment prot after reloc: Permission denied.” This is caused by a slight abuse of the C compiler during configuration, and can be disabled by running the following command with root privileges:

# setenforce 0

This will disable SELinux’s protection and allow PyPy to configure correctly. Be sure to enable it again if you need it!