Share numpy arrays between processes
This is a simple python extension that lets you share numpy arrays with other processes on the same computer. It uses either shared files or POSIX shared memory as data stores and therefore should work on most operating systems.
Here’s a simple example to give an idea of how it works. This example does everything from a single python interpreter for the sake of clarity, but the real point is to share arrays between python interpreters.
import numpy as np import SharedArray as sa # Create an array in shared memory. a = sa.create("shm://test", 10) # Attach it as a different array. This can be done from another # python interpreter as long as it runs on the same computer. b = sa.attach("shm://test") # See how they are actually sharing the same memory. a = 42 print(b) # Destroying a does not affect b. del a print(b) # See how "test" is still present in shared memory even though we # destroyed the array a. This method only works on Linux. sa.list() # Now destroy the array "test" from memory. sa.delete("test") # The array b is still there, but once you destroy it then the # data is gone for real. print(b)
SharedArray registers its own python object as the base object of the returned numpy array. This base object exposes the following methods and attributes:
This method is a wrapper around msync(2) and is only useful when using file-backed arrays (i.e. not POSIX shared memory). msync(2) flushes the mapped memory region back to the filesystem. The flags are exported as constants in the module definition (see above) and are a 1:1 map of the msync(2) flags, please refer to the manual page of msync(2) for details.
This method is a wrapper around mlock(2): lock the memory map into RAM, preventing that memory from being paged to the swap area.
This method is a wrapper around munlock(2): unlock the memory map, allowing that memory to be paged to the swap area.
This constant string is the name of the array as passed to SharedArray.create() or SharedArray.attach(). It may be passed to SharedArray.delete().
Base address of the array in memory.
Size of the array in memory.
- Python 2.7 or 3+
- Numpy 1.8+
- Posix shared memory interface
SharedArray uses the posix shm interface (shm_open and shm_unlink) and so should work on most POSIX operating systems (Linux, BSD, etc.). It has been reported to work on macOS, and it is unlikely to work on Windows.
The extension uses the distutils python package that should be familiar to most python users. To test the extension directly from the source tree, without installing, type:
python setup.py build_ext --inplace
To build and install the extension system-wide, type:
python setup.py build sudo python setup.py install
The package is also available on PyPI and can be installed using the pip tool.
On Linux, I get segfaults when working with very large arrays.
A few people have reported segfaults with very large arrays using POSIX shared memory. This is not a bug in SharedArray but rather an indication that the system ran out of POSIX shared memory.
On Linux a tmpfs virtual filesystem is used to provide POSIX shared memory, and by default it is given only about 20% of the total available memory, depending on the distribution. That amount can be changed by re-mounting the tmpfs filesystem with the size=100% option:
sudo mount -o remount,size=100% /run/shm
Also you can make the change permanent, on next boot, by setting SHM_SIZE=100% in /etc/defaults/tmpfs on recent Debian installations.
On Linux, I get “Cannot allocate memory” when creating many arrays.
SharedArray uses one memory map per array that is attached (or created). By default the maximum number of memory maps per process is set by the Linux kernel to 65530. If you want to create more arrays than that you need to tune the kernel parameter vm.max_map_count and set it to a higher value.
Note that for the change to be permanent you need to add this line to /etc/sysctl.conf:
I can’t attach old (pre 0.4) arrays anymore.
Since version 0.4 all arrays are now page aligned in memory, to be used with SIMD instructions (e.g. fftw library). As a side effect, arrays created with a previous version of SharedArray aren’t compatible with the new version (the location of the metadata changed). Save your work before upgrading.
Packages are also available on PyPi at: https://pypi.python.org/pypi/SharedArray
For bug reports, feature requests, suggestions, patches and everything else related to SharedArray, feel free to raise issues on the project page. You can also contact the maintainer directly by email at email@example.com.
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