dpmix
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Python library for fitting massive mixture models using DP priors and GPU computation.
===== dpmix
dpmix is a library for understanding posterior distributions for Dirichlet and heirarchical Dirichlet mixtures of normal distributions represented by truncated stick breaking.
Requirements
- NumPy
- SciPy
- Cython
- PyCUDA
- cyarma
- cyrand
- scikits.cuda
- gpustats
- mpi4py
Installation and testing
Install via
::
python setup.py install
To test, run the scripts in the "test" subfolder.
Usage
Check out the class docstrings for more info.
MPI
The multigpu facilities are developed using MPI. Therefore, using multiple machines is possible. However, note that the machines must be configured the same way. (Python)
Running the code on multiple machines requires mpiexec:
::
mpiexec -hostfile my_hosts -np 1 python tests/test_dpmix.py --gpu MPI
Where the my_hosts file looks like
::
host1 slots=3 host2 slots=2
I'm assuming here that the master instance of python is running on host1 and that host1 and host2 have 2 GPUs each. Note, an extra slot needs to be reserved for the master on host1. Furthermore, we need to specify which devices to use on each host. The gpu argument in the class constructors must be a dictionary like
::
gpu={'host1': [0,1], 'host2': [0,1]}
The keys must match the result of a call to os.uname() to get the host string.