Quick-start
- Download
- Installing (optional)
- Installing more modules
- Installing NumPy (optional)
- Building from source
- Packaging
- Checksums
Download and install
There are nightly binary builds available. Those builds are not always as stable as the release, but they contain numerous bugfixes and performance improvements.
We provide binaries for x86, ARM, PPC and s390x running on different operating systems such as Linux, Mac OS X and Windows:
- the Python2.7 compatible release — PyPy2.7 v6.0 — (what's new in PyPy2.7?)
- the Python3.5 compatible release — PyPy3.5 v6.0 — (what's new in PyPy3.5?).
- the Python2.7 Software Transactional Memory special release — PyPy-STM 2.5.1 (Linux x86-64 only)
“JIT Compiler” version
These binaries include a Just-in-Time compiler. They only work on x86 CPUs that have the SSE2 instruction set (most of them do, nowadays), or on x86-64 CPUs. They also contain stackless extensions, like greenlets.
Linux binaries and common distributions
Linux binaries are dynamically linked, as is usual, and thus might not be usable due to the sad story of linux binary compatibility. This means that Linux binaries are only usable on the distributions written next to them unless you're ready to hack your system by adding symlinks to the libraries it tries to open. There are better solutions:
Python2.7 compatible PyPy 6.0.0
- Linux x86 binary (32bit, built on Ubuntu 12.04 - 16.04) (see * below)
- Linux x86-64 binary (64bit, built on Ubuntu 12.04 - 16.04) (see * below)
- ARM Hardfloat Linux binary (ARMHF/gnueabihf, Raspbian) (see * below)
- ARM Hardfloat Linux binary (ARMHF/gnueabihf, Ubuntu Raring) (see * below)
- Mac OS X binary (64bit)
- FreeBSD x86 and x86_64: see FreshPorts
- Windows binary (32bit) (you might need the VS 2008 runtime library installer vcredist_x86.exe.)
- PowerPC PPC64 Linux binary (64bit big-endian, Fedora 20) (see * below)
- PowerPC PPC64le Linux binary (64bit little-endian, Fedora 21) (see * below)
- s390x Linux binary (built on Redhat Linux 7.2) (see * below)
- Source (tar.bz2); Source (zip). See below for more about the sources.
- All our downloads, including previous versions. We also have a mirror, but please use only if you have troubles accessing the links above
Python 3.5.3 compatible PyPy3.5 v6.0.0
- Linux x86 binary (32bit, built on Ubuntu 12.04 - 16.04) (see * below)
- Linux x86-64 binary (64bit, built on Ubuntu 12.04 - 16.04) (see * below)
- ARM Hardfloat Linux binary (ARMHF/gnueabihf, Raspbian) (see * below)
- ARM Softfloat Linux binary (ARMEL/gnueabi, Ubuntu Raring) (see * below)
- Mac OS X binary (64bit) (High Sierra >= 10.13, not for Sierra and below)
- Windows binary (32bit) BETA
- s390x Linux binary (built on Redhat Linux 7.2) (see * below)
- Source (tar.bz2); Source (zip). See below for more about the sources.
- All our downloads, including previous versions. We also have a mirror, but please use only if you have troubles accessing the links above
If your CPU is really, really old, it may be a x86-32 without SSE2.
There is untested support for manually translating PyPy's JIT without
SSE2 (--jit-backend=x86-without-sse2
) but note that your machine
is probably low-spec enough that running CPython on it is a better
idea in the first place.
* Stating it again: the Linux binaries are provided for the
distributions listed here. If your distribution is not exactly this
one, it won't work, you will probably see: pypy: error while loading shared
libraries: …
. Unless you want to hack a lot, try out the
portable Linux binaries.
Other versions
The other versions of PyPy are:
-
PyPy-STM 2.5.1 This is a special version of PyPy! See the Software Transactional
Memory (STM) documentation.
PyPy-STM Linux x86-64 binary (64bit, tar.bz2 built on Ubuntu 12.04 - 16.04) - The most up-to-date nightly binary builds with a JIT, if the official release is too old for what you want to do. There are versions for different libc on this site too.
- Reverse debugger: This version enables debugging your Python programs by going forward and backward in time. See the RevDB documentation.
- Sandboxing: A special safe version. Read the docs about sandboxing. This version is not supported and not actively maintained. You will likely have to fix some issues yourself, or checkout an old version, or otherwise play around on your own. We provide this documentation only for historical reasons. Please do not use in production. For reference, there are some very old, unmaintained binaries for Linux (32bit, 64bit).
Installing
All binary versions are packaged in a tar.bz2
or zip
file. When
uncompressed, they run in-place. For now you can uncompress them
either somewhere in your home directory or, say, in /opt
, and
if you want, put a symlink from somewhere like
/usr/local/bin/pypy
to /path/to/pypy_expanded/bin/pypy
. Do
not move or copy the executable pypy
outside the tree – put
a symlink to it, otherwise it will not find its libraries.
Installing modules
There are as yet few distribution-ready packages.
We recommend installing pip
, which is the standard package
manager of Python. It works like it does on CPython as explained in the
installation documentation.
If you use your distribution's PyPy package we recommend you install packages into a virtualenv. If you try to build a module and the build process complains about “missing Python.h”, you may need to install the pypy-dev package.
Installing NumPy
There are two different versions of NumPy for PyPy. For details see this FAQ question.
1. Standard NumPy
Installation works on any recent PyPy (the release above is fine).
The currently released numpy 1.13 works except for nditers
with the
updateifcopy
flag.
For example, without using a virtualenv:
$ ./pypy-xxx/bin/pypy -m ensurepip
$ ./pypy-xxx/bin/pip install cython numpy
(See the general installation documentation for more.)
2. NumPyPy
The “numpy” module can also be installed from our own repository rather
than from the official source. This version uses our
built-in _numpypy
multiarray replacement module, written in RPython.
This module is not complete, but if it works it should give correct answers.
Its performance is hard to predict exactly. For regular NumPy
source code that handles large arrays, it is likely to be slower than
the standard NumPy. It is faster on pure python code that loop over ndarrays
doing things on an element-by-element basis.
Installation (see the installation documentation for installing pip
):
pypy -m pip install git+https://bitbucket.org/pypy/numpy.git
Alternatively, the direct way:
git clone https://bitbucket.org/pypy/numpy.git
cd numpy
pypy setup.py install
If you installed to a system directory, you need to also run this once:
sudo pypy -c 'import numpy'
Note again that this version is incomplete: many things do not work and those that do may not be any faster than NumPy on CPython. For further instructions see the pypy/numpy repository and the FAQ question about the difference between the two.
Build from source
(see more build instructions)
-
Get the source code. The preferred way is to checkout the current trunk using Mercurial. The trunk usually works and is of course more up-to-date. The following command should run in about 7 minutes nowadays if you have hg >= 3.7 (it is much slower with older versions):
hg clone https://bitbucket.org/pypy/pypy
The trunk contains PyPy 2. For PyPy 3, switch to the correct branch:
# for PyPy 3: switch to the branch of PyPy that implements Python 3.5 hg update py3.5
Alternatively, get one of the following smaller packages for the source at the same revision as the above binaries:
- pypy2-v6.0.0-src.tar.bz2 (sources, PyPy 2 only)
- pypy3-v6.0.0-src.tar.bz2 (sources, PyPy 3 only)
-
Make sure you installed the dependencies. See the list here.
-
Enter the
goal
directory:cd pypy/pypy/goal
-
Run the
rpython
script. Here are the common combinations of options (works also withpython
instead ofpypy
; requires CPython 2.7 or PyPy 2, even to build PyPy 3):pypy ../../rpython/bin/rpython -Ojit targetpypystandalone # get the JIT version pypy ../../rpython/bin/rpython -O2 targetpypystandalone # get the no-jit version pypy ../../rpython/bin/rpython -O2 --sandbox targetpypystandalone # get the sandbox version
-
Enjoy Mandelbrot :-) It takes on the order of half an hour to finish the translation, and about 3GB of RAM on a 32-bit system and about 5GB on 64-bit systems. (Do not start a translation on a machine with insufficient RAM! It will just swap forever. See notes below in that case.)
-
If you want to install this PyPy as root, please read the next section, Packaging.
Notes:
-
It is recommended to use PyPy to do translations, instead of using CPython, because it is twice as fast. You should just start by downloading an official release of PyPy (with the JIT). If you really have to use CPython then note that we are talking about CPython 2.7 here, not CPython 3.x. (Older versions like 2.6 are out.)
-
On some 32-bit systems, the address space limit of 2 or 3 GB of RAM can be an issue. More generally you may be just a little bit low of RAM. First note that 2 GB is really not enough nowadays; on Windows you first need to refer to the Windows build instructions. More precisely, translation on 32-bit takes at this point 2.7 GB if PyPy is used and 2.9 GB if CPython is used. There are two workarounds:
-
Use PyPy, not CPython. If you don't have any PyPy so far, not even
an older version, then you need to build one first, with some parts
removed. So, first translate with
...rpython -Ojit targetpypystandalone --withoutmod-micronumpy --withoutmod-cpyext
, then copypypy-c
andlibpypy_c.so
somewhere else, and finally call it with...pypy-c ../../rpython/bin/rpython -Ojit
- If even using PyPy instead of CPython is not enough, try to tweak some internal parameters. Example (slower but saves around 400MB):
PYPY_DONT_RUN_SUBPROCESS=1 PYPY_GC_MAX_DELTA=200MB pypy --jit loop_longevity=300 ../../rpython/bin/rpython -Ojit --source # then read the next point about --source
-
Use PyPy, not CPython. If you don't have any PyPy so far, not even
an older version, then you need to build one first, with some parts
removed. So, first translate with
-
You can run translations with
--source
, which only builds the C source files (and prints at the end where). Then you cancd
there and executemake
. This is another way to reduce memory usage. Note that afterwards, you have to run manually>pypy-c .../pypy/tool/build_cffi_imports.py
if you want to be able to import the cffi-based modules. -
On Linux, translating with
asmgcroot
, is delicate. It requires using gcc with no particularly fancy options. It does not work e.g. with clang, or if you pass uncommon options with theCFLAGS
environment variable. If you insist on passing these options or using clang, then you can compile PyPy with the default shadow stack option instead (for a small performance price in non-JITted code). -
Like other JITs, PyPy doesn't work out of the box on some Linux distributions that trade full POSIX compliance for extra security features. E.g. with PAX, you have to run PyPy with
paxctl -cm
. This also applies to translation (unless you use CPython to run the translation and you specify--source
).
Packaging
Once PyPy is translated from source the binary package similar to those
provided in the section Default (with a JIT Compiler) above could be
easily created with package.py
script as following:
cd ./pypy/pypy/tool/release/
python package.py --help #for information
python package.py --archive-name pypy-my-own-package-name
It is recommended to use package.py because custom scripts will invariably become out-of-date. If you want to write custom scripts anyway, note an easy-to-miss point: some modules are written with CFFI, and require some compilation. If you install PyPy as root without pre-compiling them, normal users will get errors:
-
PyPy 2.5.1 or earlier: normal users would see permission errors.
Installers need to run
pypy -c “import gdbm”
and other similar commands at install time; the exact list is in package.py.
Users seeing a broken installation of PyPy can fix it after-the-fact if they have sudo rights, by running once e.g.sudo pypy -c "import gdbm"
. -
PyPy 2.6 and later: anyone would get
ImportError: no module named _gdbm_cffi
. Installers need to runpypy _gdbm_build.py
in thelib_pypy
directory during the installation process (plus others; see the exact list in package.py).
Users seeing a broken installation of PyPy can fix it after-the-fact, by runningpypy /path/to/lib_pypy/_gdbm_build.py
. This command produces a file called_gdbm_cffi.pypy-41.so
locally, which is a C extension module for PyPy. You can move it at any place where modules are normally found: e.g. in your project's main directory, or in a directory that you add to the env varPYTHONPATH
.
Checksums
Here are the checksums for each of the downloads
pypy2.7-5.10.0 sha256:
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da85af9240220179493ad66c857934dc7ea91aef8f168cd293a2d99af8346ee2 pypy2-v5.10.0-linux64.tar.bz2
6fdd55dd8f674efd06f76edb60a09a03b9b04a5fbc56741f416a94a0b9d2ff91 pypy2-v5.10.0-linux-armel.tar.bz2
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7e4120f0a83529a6851cbae0ec107dc7085ba8a4aeff4e7bd9da9aadb1ef37a4 pypy2-v5.10.0-osx64.tar.bz2
dab4dccfa71820c4f803f5a82e13f76517bfde5fafe1e5fba6ff58ef2ba318ab pypy2-v5.10.0-s390x.tar.bz2
1209f2db718e6afda17528baa5138177a14a0938588a7d3e1b7c722c483079a8 pypy2-v5.10.0-src.tar.bz2
89304eb886f84b5c65f3f4079445ef018cdb9a6e59ef4ed2095d37248a3fefcc pypy2-v5.10.0-src.zip
350914f9b70404781674f2f188f84d440d9d25da46ed9733b3f98269a510e033 pypy2-v5.10.0-win32.zip
9afa1a36a5fc55ebc3e80576f05f44294f2b0de279862286fe00f5ee139965b1 pypy2-v5.10.0-ppc64.tar.bz2
2c32ccfa80e3e2ec56b4cc848526046d7b0de1f2f1a92b0cedeb414ec76745ab pypy2-v5.10.0-ppc64le.tar.bz2
pypy2.7-6.0.0 sha256:
ad1082d4328ae8f32617b14628648583b82b6d29df3aa42b97bd1853c08c4bc8 pypy2-v6.0.0-linux32.tar.bz2
6cbf942ba7c90f504d8d6a2e45d4244e3bf146c8722d64e9410b85eac6b5af67 pypy2-v6.0.0-linux64.tar.bz2
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bf155c8ac2f757d24361591080a9f4f95424a07e30f943f7d751d96442e0f36a pypy2-v6.0.0-s390x.tar.bz2
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6e2210dae1ae721ed4eb9cba19f15453514b64111511c84f24843c4fdefdaf7f pypy2-v6.0.0-win32.zip
pypy 3.5-v5.10.0 sha256:
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pypy 3.5-v5.10.1 sha256:
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75a276e1ee1863967bbacb70c5bff636de200768c0ec90e72f7ec17aace0aefe pypy3-v5.10.1-linux64.tar.bz2
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9ce98481cddede40a3357f7462f2c894bb96f178e2e8715d04feda1476ec1563 pypy3-v5.10.1-s390x.tar.bz2
pypy 3.5-v6.0.0 sha256:
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4cfffa292b9ef34bb6ba39cdbaa196c5c5cbbc5aa3faaa157cf45d7e34027048 pypy3-v6.0.0-linux64.tar.bz2
6a6888a55192f58594838b8b3d2e7daaad43d3bf4293afab3dd8987d0bbd1124 pypy3-v6.0.0-linux-armel.tar.bz2
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