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        "author": "Jacob Frelinger",
        "author_email": "jacob.frelinger@duke.edu",
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        "description": "=====\ndpmix\n=====\n\ndpmix is a library for understanding posterior distributions for\nDirichlet and heirarchical Dirichlet mixtures of normal distributions\nrepresented by truncated stick breaking.\n\nRequirements\n------------\n\n* NumPy\n* SciPy\n* Cython\n* PyCUDA\n* cyarma\n* cyrand\n* scikits.cuda\n* gpustats\n* mpi4py\n\nInstallation and testing\n------------------------\n\nInstall via\n\n::\n\n   python setup.py install\n\nTo test, run the scripts in the \"test\" subfolder.\n\nUsage\n-----\n\nCheck out the class docstrings for more info.\n\nMPI\n---\n\nThe multigpu facilities are developed using MPI. Therefore, \nusing multiple machines is possible. However, note that the\nmachines must be configured the same way. (Python)\n\nRunning the code on multiple machines requires *mpiexec*:\n\n::\n\n   mpiexec -hostfile my_hosts -np 1 python tests/test_dpmix.py --gpu MPI\n\nWhere the *my_hosts* file looks like \n\n::\n\n   host1 slots=3\n   host2 slots=2\n\nI'm assuming here that the master instance of python is running on host1\nand that host1 and host2 have 2 GPUs each. Note, an extra slot needs to be\nreserved for the master on host1. Furthermore, we need to specify which\ndevices to use on each host. The *gpu* argument in the class constructors\nmust be a dictionary like\n\n::\n\n  gpu={'host1': [0,1], 'host2': [0,1]}\n\nThe keys must match the result of a call to *os.uname()* to get the\nhost string.\n\n\n\n",
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