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)
