Distributed computing made easier, using remote objects
Distob will take your existing python objects, or a sequence of objects, and scatter them onto many IPython parallel engines, which may be running on a single computer or on a cluster.
In place of the original objects, proxy objects are kept on the client computer that provide the same interface as the original objects. You can continue to use these as if the objects were still local. All methods are passed through to the remote objects, where computation is done.
In particular, sending numpy arrays to the cluster is supported.
A numpy array can also be scattered across the cluster, along a particular axis. Operations on the array can then be automatically done in parallel (either using ufuncs, or by using vectorize() below)
Note: numpy with __numpy_ufunc__ feature enabled (not yet released) is required to support distributed array arithmetic and distributed ufuncs. You can get numpy with this experimental feature enabled here: https://github.com/mattja/numpy/archive/master.zip
Distob is an object layer built on top of ipyparallel, so it will make use of your default IPython parallel profile. This allows different cluster architectures, local CPUs, SSH nodes, PBS, Amazon EC2, etc.
distributed numpy arrays
engine: the ObjectEngine instance on each host (ObjectHub on the client)
- Allow assignment to slices of remote arrays.
- Properly implement caching of remote method results.
- Auto-creation of proxy classes at runtime (depends uqfoundation/dill#58)
- For ufunc execution, still need to implement reduce, accumulate, reduceat, outer, at methods.
- Make proxy classes more robust, adapting wrapt (pypi.python.org/pypi/wrapt)
Incorporates pylru.py by Jay Hutchinson, http://github.com/jlhutch/pylru
ipyparallel interactive parallel computing: https://ipyparallel.readthedocs.org/
dill by Mike McKerns for object serialization, see: http://trac.mystic.cacr.caltech.edu/project/pathos
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