- Frequently Asked Questions
- What is PyPy?
- Is PyPy a drop in replacement for CPython?
- Module xyz does not work with PyPy: ImportError
- Module xyz does not work in the sandboxed PyPy?
- Do CPython Extension modules work with PyPy?
- On which platforms does PyPy run?
- Which Python version (2.x?) does PyPy implement?
- Does PyPy have a GIL? Why?
- Is PyPy more clever than CPython about Tail Calls?
- How do I write extension modules for PyPy?
- How fast is PyPy?
- Couldn’t the JIT dump and reload already-compiled machine code?
- Would type annotations help PyPy’s performance?
- Can I use PyPy’s translation toolchain for other languages besides Python?
- How do I get into PyPy development? Can I come to sprints?
- OSError: ... cannot restore segment prot after reloc... Help?
See also: Frequently ask questions about RPython.
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.
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.
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
pip install xyz. The simplest solution is to use
virtualenv (as documented here). Then enter (activate) the virtualenv
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:
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
You cannot import any extension module in a sandboxed PyPy, sorry. Even the built-in modules available are very limited. Sandboxing in PyPy is a good proof of concept, really safe IMHO, but it is only a proof of concept. It seriously requires someone working on it. Before this occurs, it can only be used it for “pure Python” examples: programs that import mostly nothing (or only pure Python modules, recursively).
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
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.
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).
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).
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.
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.
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.
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.
Cython types are, by construction, similar to C declarations. For
example, a local variable or an instance attribute can be declared
"cdef int" to force a machine word to be used. This changes the
usual Python semantics (e.g. no overflow checks, and errors when
trying to write other types of objects there). It gives some extra
performance, but the exact benefits are unclear: right now
(January 2015) for example we are investigating a technique that would
store machine-word integers directly on instances, giving part of the
benefits without the user-supplied
PEP 484 - Type Hints, on the other hand, is almost entirely useless if you’re looking at performance. First, as the name implies, they are hints: they must still be checked at runtime, like PEP 484 says. Or maybe you’re fine with a mode in which you get very obscure crashes when the type annotations are wrong; but even in that case the speed benefits would be extremely minor.
There are several reasons for why. One of them is that annotations
are at the wrong level (e.g. a PEP 484 “int” corresponds to Python 3’s
int type, which does not necessarily fits inside one machine word;
even worse, an “int” annotation allows arbitrary int subclasses).
Another is that a lot more information is needed to produce good code
f() called here really means this function there, and
will never be monkey-patched” – same with
btw). The third reason is that some “guards” in PyPy’s JIT traces
don’t really have an obvious corresponding type (e.g. “this dict is so
far using keys which don’t override
__hash__ so a more efficient
implementation was used”). Many guards don’t even have any correspondence
with types at all (“this class attribute was not modified”; “the loop
counter did not reach zero so we don’t need to release the GIL”; and
As PyPy works right now, it is able to derive far more useful information than can ever be given by PEP 484, and it works automatically. As far as we know, this is true even if we would add other techniques to PyPy, like a fast first-pass JIT.
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.
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.
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!