Metadata-Version: 1.1
Name: SSHCustodian
Version: 0.2.5
Summary: A modification to the Custodian class in custodian (github.com/materialsproject/custodian) to allow for copying the temp_dir to other compute nodes via ssh.
Home-page: https://github.com/jkglasbrenner/sshcustodian
Author: James K. Glasbrenner
Author-email: jkglasbrenner@gmail.com
License: MIT
Download-URL: https://github.com/jkglasbrenner/sshcustodian/tarball/0.2.5
Description: =============
        SSH Custodian
        =============
        
        This module depends on the Custodian class in the `custodian project
        <https://github.com/materialsproject/custodian>`_, which is a wrapper that
        manages jobs running on computing clusters. The custodian module is part of
        `The Materials Project <http://materialsproject.org/>`_.
        
        This module extends the Custodian class by creating the subclass SSHCustodian,
        which adds the functionality to copy the temporary directory created via monty
        to the scratch partitions on slave compute nodes, provided that the cluster's
        file-system is configured in this way. The implementation invokes a sub-process
        to utilize the ssh executable installed on the cluster, so it is not
        particularly elegant or platform independent, nor is this solution likely to be
        general to all clusters. This is why this modification has not been submitted
        as a pull request to the main Custodian project.
        
        You use SSHCustodian in the same way as the Custodian class, and it should
        integrate in with your existing scripts. The SSHCustodian class takes two
        additional arguments when creating a new instance, ``scratch_dir_node_only``
        and ``pbs_nodefile``::
          
          scratch_dir_node_only (bool): If set to True, custodian will grab the list
              of nodes in the file path provided to pbs_nodefile and use copy the
              temp_dir to the scratch_dir on each node over ssh. This is necessary on
              cluster setups where each node has its own independent scratch
              partition.
              
          pbs_nodefile (str): The filepath to the list of nodes to be used in a
              calculation. If this path does not point to a valid file, then
              scratch_dir_node_only will be automatically set to False.
        
        The subclass SSHVaspJob was also created, which overrides the setup method to
        also check the environment variable ``PBS_NUM_PPN`` when ``auto_npar =
        True``. This is necessary to implement for using compute node scratch
        partitions on PBS-based queueing systems, as the generic method of using
        ``multiprocessing.cpu_count()`` to count the number of cores will include
        hyperthreads, which will overestimate the number of physical cores and lead to
        NPAR being set too large. One consequence of setting NPAR too high is that a
        VASP job will hang, consuming resources but not doing anything useful. If you
        are using this kind of scratch directory, be careful about setting NPAR.
        
        On many clusters, the filepath for the list of compute nodes is in the
        environment variable ``PBS_NODEFILE``, which can be accessed in bash as
        ``$PBS_NODEFILE`` and in python using the ``os`` module. An example of
        how SSHCustodian can be used in a script is the following::
        
          import logging
          import os
          from sshcustodian.sshcustodian import SSHCustodian
          from custodian.vasp.handlers import (VaspErrorHandler,
                                               UnconvergedErrorHandler,
                                               MeshSymmetryErrorHandler,
                                               NonConvergingErrorHandler,
                                               PotimErrorHandler)
          from custodian.vasp.validators import VasprunXMLValidator
          from sshcustodian.vasp.sshjobs import SSHVaspJob
          from pymatgen.io.vasp import VaspInput
        
          FORMAT = '%(asctime)s %(message)s'
          logging.basicConfig(format=FORMAT, level=logging.INFO, filename="run.log")
        
          class VaspInputArgs:
              def __init__(self):
                  """
                  Set the default values for running a VASP job.
                  """
                  self.static_kpoint = 1
              
              def import_dict(self, in_dict):
                  """
                  Create and update self variables using dictionary.
                  """
                  for (key, value) in iteritems(in_dict):
                      if key == "command":
                          self.command = value
                      if key == "static_kpoint":
                          self.static_kpoint = value
                      if key == "jobs":
                          self.jobs = value
                   
        
          def get_runs(args):
              vasp_command = args.command.split()
              njobs = len(args.jobs)
              for i, job in enumerate(args.jobs):
                  final = False if i != njobs - 1 else True
                  if any(c.isdigit() for c in job):
                      suffix = "." + job
                  else:
                      suffix = ".{}{}".format(job, i + 1)
                  settings = []
                  backup = True if i == 0 else False
                  copy_magmom = False
                  vinput = VaspInput.from_directory(".")
                  if i > 0:
                      settings.append(
                          {"file": "CONTCAR",
                           "action": {"_file_copy": {"dest": "POSCAR"}}})
                  job_type = job.lower()
                  auto_npar = True
                  if job_type.startswith("static"):
                      m = [i * args.static_kpoint for i in vinput["KPOINTS"].kpts[0]]
                      settings.extend([
                          {"dict": "INCAR",
                           "action": {"_set": {"NSW": 0}}},
                          {'dict': 'KPOINTS',
                           'action': {'_set': {'kpoints': [m]}}}])
              
                  yield SSHVaspJob(vasp_command, final=final, suffix=suffix,
                                   backup=backup, settings_override=settings,
                                   copy_magmom=copy_magmom, auto_npar=auto_npar)
        
        
          logging.info("Handlers used are %s" % args.handlers)
          scratch_root = os.path.abspath("/scratch")
          pbs_nodefile = os.environ.get("PBS_NODEFILE")
          job_args = VaspInputArgs()
          job_dict = {"command": "pvasp",
                      "jobs": ["static"]}
          job_args.import_dict(job_dict)
          handlers = [VaspErrorHandler(), MeshSymmetryErrorHandler(),
                      UnconvergedErrorHandler(), NonConvergingErrorHandler(),
                      PotimErrorHandler()]
          validators = [VasprunXMLValidator()]
          c = SSHCustodian(handlers, get_runs(job_args), validators,
                           checkpoint=True,
                           scratch_dir=scratch_root,
                           scratch_dir_node_only=True,
                           pbs_nodefile=pbs_nodefile)
          c.run()
        
        Note that depending on how your cluster is configured, the ``"command":
        "pvasp"`` will need to be changed to however you invoke a parallel job.
        
        For further information on how to use custodian, consult the `custodian project
        documentation <https://pythonhosted.org/custodian/>`_.
        
Keywords: custodian DFT VASP materials hpc queue management
Platform: UNKNOWN
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3.5
