def runDummyModel(modelID, jobID, useStreams, params, predictedField, reportKeys, optimizeKey, jobsDAO, modelCheckpointGUID, logLevel=None, predictionCacheMaxRecords=None): from nupic.swarming.DummyModelRunner import OPFDummyModelRunner # The logger for this method logger = logging.getLogger('com.numenta.nupic.hypersearch.utils') # ------------------------------------------------------------------------- # if NTA_ROOTDIR is not set in the environment, set it for the user so that # we can find data files expected to be in the share/prediction/data directory if not "NTA_ROOTDIR" in os.environ: os.environ['NTA_ROOTDIR'] = nupic.rootDir # Run the experiment now try: if type(params) is bool: params = {} runner = OPFDummyModelRunner( modelID=modelID, jobID=jobID, params=params, predictedField=predictedField, reportKeyPatterns=reportKeys, optimizeKeyPattern=optimizeKey, useStreams=useStreams, jobsDAO=jobsDAO, modelCheckpointGUID=modelCheckpointGUID, logLevel=logLevel, predictionCacheMaxRecords=predictionCacheMaxRecords) (completionReason, completionMsg) = runner.run() # The dummy model runner will call sys.exit(1) if # NTA_TEST_sysExitFirstNModels is set and the number of models in the # models table is <= NTA_TEST_sysExitFirstNModels except SystemExit: sys.exit(1) except InvalidConnectionException: raise except Exception, e: (completionReason, completionMsg) = _handleModelRunnerException(jobID, modelID, jobsDAO, "NA", logger, e)
def runDummyModel( modelID, jobID, params, predictedField, reportKeys, optimizeKey, jobsDAO, modelCheckpointGUID, logLevel=None, predictionCacheMaxRecords=None, ): from nupic.swarming.DummyModelRunner import OPFDummyModelRunner # The logger for this method logger = logging.getLogger("com.numenta.nupic.hypersearch.utils") # ------------------------------------------------------------------------- # if NTA_ROOTDIR is not set in the environment, set it for the user so that # we can find data files expected to be in the share/prediction/data directory if not "NTA_ROOTDIR" in os.environ: os.environ["NTA_ROOTDIR"] = nupic.rootDir # Run the experiment now try: if type(params) is bool: params = {} runner = OPFDummyModelRunner( modelID=modelID, jobID=jobID, params=params, predictedField=predictedField, reportKeyPatterns=reportKeys, optimizeKeyPattern=optimizeKey, jobsDAO=jobsDAO, modelCheckpointGUID=modelCheckpointGUID, logLevel=logLevel, predictionCacheMaxRecords=predictionCacheMaxRecords, ) (completionReason, completionMsg) = runner.run() # The dummy model runner will call sys.exit(1) if # NTA_TEST_sysExitFirstNModels is set and the number of models in the # models table is <= NTA_TEST_sysExitFirstNModels except SystemExit: sys.exit(1) except InvalidConnectionException: raise except Exception, e: (completionReason, completionMsg) = _handleModelRunnerException(jobID, modelID, jobsDAO, "NA", logger, e)
def runDummyModel( modelID, jobID, params, predictedField, reportKeys, optimizeKey, jobsDAO, modelCheckpointGUID, logLevel=None, predictionCacheMaxRecords=None, ): from nupic.swarming.DummyModelRunner import OPFDummyModelRunner # The logger for this method logger = logging.getLogger("com.numenta.nupic.hypersearch.utils") # Run the experiment now try: if type(params) is bool: params = {} runner = OPFDummyModelRunner( modelID=modelID, jobID=jobID, params=params, predictedField=predictedField, reportKeyPatterns=reportKeys, optimizeKeyPattern=optimizeKey, jobsDAO=jobsDAO, modelCheckpointGUID=modelCheckpointGUID, logLevel=logLevel, predictionCacheMaxRecords=predictionCacheMaxRecords, ) (completionReason, completionMsg) = runner.run() # The dummy model runner will call sys.exit(1) if # NTA_TEST_sysExitFirstNModels is set and the number of models in the # models table is <= NTA_TEST_sysExitFirstNModels except SystemExit: sys.exit(1) except InvalidConnectionException: raise except Exception, e: (completionReason, completionMsg) = _handleModelRunnerException(jobID, modelID, jobsDAO, "NA", logger, e)
def runDummyModel(modelID, jobID, params, predictedField, reportKeys, optimizeKey, jobsDAO, modelCheckpointGUID, logLevel=None, predictionCacheMaxRecords=None): from nupic.swarming.DummyModelRunner import OPFDummyModelRunner # The logger for this method logger = logging.getLogger('com.numenta.nupic.hypersearch.utils') # Run the experiment now try: if type(params) is bool: params = {} runner = OPFDummyModelRunner( modelID=modelID, jobID=jobID, params=params, predictedField=predictedField, reportKeyPatterns=reportKeys, optimizeKeyPattern=optimizeKey, jobsDAO=jobsDAO, modelCheckpointGUID=modelCheckpointGUID, logLevel=logLevel, predictionCacheMaxRecords=predictionCacheMaxRecords) (completionReason, completionMsg) = runner.run() # The dummy model runner will call sys.exit(1) if # NTA_TEST_sysExitFirstNModels is set and the number of models in the # models table is <= NTA_TEST_sysExitFirstNModels except SystemExit: sys.exit(1) except InvalidConnectionException: raise except Exception, e: (completionReason, completionMsg) = _handleModelRunnerException(jobID, modelID, jobsDAO, "NA", logger, e)