def __createModel(self, expDir): # ----------------------------------------------------------------------- # Load the experiment's description.py module descriptionPyModule = helpers.loadExperimentDescriptionScriptFromDir( expDir) expIface = helpers.getExperimentDescriptionInterfaceFromModule( descriptionPyModule) # ----------------------------------------------------------------------- # Construct the model instance modelDescription = expIface.getModelDescription() return ModelFactory.create(modelDescription)
def __createModel(self, expDir): # ----------------------------------------------------------------------- # Load the experiment's description.py module descriptionPyModule = helpers.loadExperimentDescriptionScriptFromDir( expDir) expIface = helpers.getExperimentDescriptionInterfaceFromModule( descriptionPyModule) # ----------------------------------------------------------------------- # Construct the model instance modelDescription = expIface.getModelDescription() return ModelFactory.create(modelDescription)
def test_sub_experiment_override(self): expDir = g_myEnv.getOpfExperimentPath("gym") module = loadExperimentDescriptionScriptFromDir(expDir) expIface = getExperimentDescriptionInterfaceFromModule(module) modelDesc = expIface.getModelDescription() tpActivationThreshold = modelDesc['modelParams'] \ ['tmParams']['activationThreshold'] expectedValue = 12 self.assertEqual(tpActivationThreshold, expectedValue, "Expected tm activationThreshold=%s, but got %s" % ( expectedValue, tpActivationThreshold))
def getSwarmModelParams(modelID): """Retrieve the Engine-level model params from a Swarm model Args: modelID - Engine-level model ID of the Swarm model Returns: JSON-encoded string containing Model Params """ # TODO: the use of nupic.frameworks.opf.helpers.loadExperimentDescriptionScriptFromDir when # retrieving module params results in a leakage of pf_base_descriptionNN and # pf_descriptionNN module imports for every call to getSwarmModelParams, so # the leakage is unlimited when getSwarmModelParams is called by a # long-running process. An alternate solution is to execute the guts of # this function's logic in a seprate process (via multiprocessing module). cjDAO = ClientJobsDAO.get() (jobID, description) = cjDAO.modelsGetFields(modelID, ["jobId", "genDescription"]) (baseDescription, ) = cjDAO.jobGetFields(jobID, ["genBaseDescription"]) # Construct a directory with base.py and description.py for loading model # params, and use nupic.frameworks.opf.helpers to extract model params from # those files descriptionDirectory = tempfile.mkdtemp() try: baseDescriptionFilePath = os.path.join(descriptionDirectory, "base.py") with open(baseDescriptionFilePath, mode="wb") as f: f.write(baseDescription) descriptionFilePath = os.path.join(descriptionDirectory, "description.py") with open(descriptionFilePath, mode="wb") as f: f.write(description) expIface = helpers.getExperimentDescriptionInterfaceFromModule( helpers.loadExperimentDescriptionScriptFromDir( descriptionDirectory)) return json.dumps( dict(modelConfig=expIface.getModelDescription(), inferenceArgs=expIface.getModelControl().get( "inferenceArgs", None))) finally: shutil.rmtree(descriptionDirectory, ignore_errors=True)
def getSwarmModelParams(modelID): """Retrieve the Engine-level model params from a Swarm model Args: modelID - Engine-level model ID of the Swarm model Returns: JSON-encoded string containing Model Params """ # TODO: the use of nupic.frameworks.opf.helpers.loadExperimentDescriptionScriptFromDir when # retrieving module params results in a leakage of pf_base_descriptionNN and # pf_descriptionNN module imports for every call to getSwarmModelParams, so # the leakage is unlimited when getSwarmModelParams is called by a # long-running process. An alternate solution is to execute the guts of # this function's logic in a seprate process (via multiprocessing module). cjDAO = ClientJobsDAO.get() (jobID, description) = cjDAO.modelsGetFields( modelID, ["jobId", "genDescription"]) (baseDescription,) = cjDAO.jobGetFields(jobID, ["genBaseDescription"]) # Construct a directory with base.py and description.py for loading model # params, and use nupic.frameworks.opf.helpers to extract model params from # those files descriptionDirectory = tempfile.mkdtemp() try: baseDescriptionFilePath = os.path.join(descriptionDirectory, "base.py") with open(baseDescriptionFilePath, mode="wb") as f: f.write(baseDescription) descriptionFilePath = os.path.join(descriptionDirectory, "description.py") with open(descriptionFilePath, mode="wb") as f: f.write(description) expIface = helpers.getExperimentDescriptionInterfaceFromModule( helpers.loadExperimentDescriptionScriptFromDir(descriptionDirectory)) return json.dumps( dict( modelConfig=expIface.getModelDescription(), inferenceArgs=expIface.getModelControl().get("inferenceArgs", None))) finally: shutil.rmtree(descriptionDirectory, ignore_errors=True)
def _runExperimentImpl(options, model=None): """Creates and runs the experiment Args: options: namedtuple ParseCommandLineOptionsResult model: For testing: may pass in an existing OPF Model instance to use instead of creating a new one. Returns: reference to OPFExperiment instance that was constructed (this is provided to aid with debugging) or None, if none was created. """ json_helpers.validate(options.privateOptions, schemaDict=g_parsedPrivateCommandLineOptionsSchema) # Load the experiment's description.py module experimentDir = options.experimentDir descriptionPyModule = helpers.loadExperimentDescriptionScriptFromDir( experimentDir) expIface = helpers.getExperimentDescriptionInterfaceFromModule( descriptionPyModule) # Handle "list checkpoints" request if options.privateOptions['listAvailableCheckpoints']: _printAvailableCheckpoints(experimentDir) return None # Load experiment tasks experimentTasks = expIface.getModelControl().get('tasks', []) # If the tasks list is empty, and this is a nupic environment description # file being run from the OPF, convert it to a simple OPF description file. if (len(experimentTasks) == 0 and expIface.getModelControl()['environment'] == OpfEnvironment.Nupic): expIface.convertNupicEnvToOPF() experimentTasks = expIface.getModelControl().get('tasks', []) # Ensures all the source locations are either absolute paths or relative to # the nupic.datafiles package_data location. expIface.normalizeStreamSources() # Extract option newSerialization = options.privateOptions['newSerialization'] # Handle listTasks if options.privateOptions['listTasks']: print "Available tasks:" for label in [t['taskLabel'] for t in experimentTasks]: print "\t", label return None # Construct the experiment instance if options.privateOptions['runCheckpointName']: assert model is None checkpointName = options.privateOptions['runCheckpointName'] model = ModelFactory.loadFromCheckpoint( savedModelDir=_getModelCheckpointDir(experimentDir, checkpointName), newSerialization=newSerialization) elif model is not None: print "Skipping creation of OPFExperiment instance: caller provided his own" else: modelDescription = expIface.getModelDescription() model = ModelFactory.create(modelDescription) # Handle "create model" request if options.privateOptions['createCheckpointName']: checkpointName = options.privateOptions['createCheckpointName'] _saveModel(model=model, experimentDir=experimentDir, checkpointLabel=checkpointName, newSerialization=newSerialization) return model # Build the task list # Default task execution index list is in the natural list order of the tasks taskIndexList = range(len(experimentTasks)) customTaskExecutionLabelsList = options.privateOptions['taskLabels'] if customTaskExecutionLabelsList: taskLabelsList = [t['taskLabel'] for t in experimentTasks] taskLabelsSet = set(taskLabelsList) customTaskExecutionLabelsSet = set(customTaskExecutionLabelsList) assert customTaskExecutionLabelsSet.issubset(taskLabelsSet), \ ("Some custom-provided task execution labels don't correspond " "to actual task labels: mismatched labels: %r; actual task " "labels: %r.") % (customTaskExecutionLabelsSet - taskLabelsSet, customTaskExecutionLabelsList) taskIndexList = [taskLabelsList.index(label) for label in customTaskExecutionLabelsList] print "#### Executing custom task list: %r" % [taskLabelsList[i] for i in taskIndexList] # Run all experiment tasks for taskIndex in taskIndexList: task = experimentTasks[taskIndex] # Create a task runner and run it! taskRunner = _TaskRunner(model=model, task=task, cmdOptions=options) taskRunner.run() del taskRunner if options.privateOptions['checkpointModel']: _saveModel(model=model, experimentDir=experimentDir, checkpointLabel=task['taskLabel'], newSerialization=newSerialization) return model
def run(self): """ Runs the OPF Model Parameters: ------------------------------------------------------------------------- retval: (completionReason, completionMsg) where completionReason is one of the ClientJobsDAO.CMPL_REASON_XXX equates. """ # ----------------------------------------------------------------------- # Load the experiment's description.py module descriptionPyModule = helpers.loadExperimentDescriptionScriptFromDir( self._experimentDir) expIface = helpers.getExperimentDescriptionInterfaceFromModule( descriptionPyModule) expIface.normalizeStreamSources() modelDescription = expIface.getModelDescription() self._modelControl = expIface.getModelControl() # ----------------------------------------------------------------------- # Create the input data stream for this task streamDef = self._modelControl['dataset'] from nupic.data.stream_reader import StreamReader readTimeout = 0 self._inputSource = StreamReader(streamDef, isBlocking=False, maxTimeout=readTimeout) # ----------------------------------------------------------------------- #Get field statistics from the input source fieldStats = self._getFieldStats() # ----------------------------------------------------------------------- # Construct the model instance self._model = ModelFactory.create(modelDescription) self._model.setFieldStatistics(fieldStats) self._model.enableLearning() self._model.enableInference( self._modelControl.get("inferenceArgs", None)) # ----------------------------------------------------------------------- # Instantiate the metrics self.__metricMgr = MetricsManager( self._modelControl.get('metrics', None), self._model.getFieldInfo(), self._model.getInferenceType()) self.__loggedMetricPatterns = self._modelControl.get( "loggedMetrics", []) self._optimizedMetricLabel = self.__getOptimizedMetricLabel() self._reportMetricLabels = matchPatterns(self._reportKeyPatterns, self._getMetricLabels()) # ----------------------------------------------------------------------- # Initialize periodic activities (e.g., for model result updates) self._periodic = self._initPeriodicActivities() # ----------------------------------------------------------------------- # Create our top-level loop-control iterator numIters = self._modelControl.get('iterationCount', -1) # Are we asked to turn off learning for a certain # of iterations near the # end? learningOffAt = None iterationCountInferOnly = self._modelControl.get( 'iterationCountInferOnly', 0) if iterationCountInferOnly == -1: self._model.disableLearning() elif iterationCountInferOnly > 0: assert numIters > iterationCountInferOnly, "when iterationCountInferOnly " \ "is specified, iterationCount must be greater than " \ "iterationCountInferOnly." learningOffAt = numIters - iterationCountInferOnly self.__runTaskMainLoop(numIters, learningOffAt=learningOffAt) # ----------------------------------------------------------------------- # Perform final operations for model self._finalize() return (self._cmpReason, None)
def _runExperimentImpl(options, model=None): """Creates and runs the experiment Args: options: namedtuple ParseCommandLineOptionsResult model: For testing: may pass in an existing OPF Model instance to use instead of creating a new one. Returns: reference to OPFExperiment instance that was constructed (this is provided to aid with debugging) or None, if none was created. """ json_helpers.validate(options.privateOptions, schemaDict=g_parsedPrivateCommandLineOptionsSchema) # Load the experiment's description.py module experimentDir = options.experimentDir descriptionPyModule = helpers.loadExperimentDescriptionScriptFromDir( experimentDir) expIface = helpers.getExperimentDescriptionInterfaceFromModule( descriptionPyModule) # Handle "list checkpoints" request if options.privateOptions['listAvailableCheckpoints']: _printAvailableCheckpoints(experimentDir) return None # Load experiment tasks experimentTasks = expIface.getModelControl().get('tasks', []) # If the tasks list is empty, and this is a nupic environment description # file being run from the OPF, convert it to a simple OPF description file. if (len(experimentTasks) == 0 and expIface.getModelControl()['environment'] == OpfEnvironment.Nupic): expIface.convertNupicEnvToOPF() experimentTasks = expIface.getModelControl().get('tasks', []) # Ensures all the source locations are either absolute paths or relative to # the nupic.datafiles package_data location. expIface.normalizeStreamSources() # Extract option newSerialization = options.privateOptions['newSerialization'] # Handle listTasks if options.privateOptions['listTasks']: print("Available tasks:") for label in [t['taskLabel'] for t in experimentTasks]: print("\t", label) return None # Construct the experiment instance if options.privateOptions['runCheckpointName']: assert model is None checkpointName = options.privateOptions['runCheckpointName'] model = ModelFactory.loadFromCheckpoint( savedModelDir=_getModelCheckpointDir(experimentDir, checkpointName), newSerialization=newSerialization) elif model is not None: print("Skipping creation of OPFExperiment instance: caller provided his own") else: modelDescription = expIface.getModelDescription() model = ModelFactory.create(modelDescription) # Handle "create model" request if options.privateOptions['createCheckpointName']: checkpointName = options.privateOptions['createCheckpointName'] _saveModel(model=model, experimentDir=experimentDir, checkpointLabel=checkpointName, newSerialization=newSerialization) return model # Build the task list # Default task execution index list is in the natural list order of the tasks taskIndexList = list(range(len(experimentTasks))) customTaskExecutionLabelsList = options.privateOptions['taskLabels'] if customTaskExecutionLabelsList: taskLabelsList = [t['taskLabel'] for t in experimentTasks] taskLabelsSet = set(taskLabelsList) customTaskExecutionLabelsSet = set(customTaskExecutionLabelsList) assert customTaskExecutionLabelsSet.issubset(taskLabelsSet), \ ("Some custom-provided task execution labels don't correspond " "to actual task labels: mismatched labels: %r; actual task " "labels: %r.") % (customTaskExecutionLabelsSet - taskLabelsSet, customTaskExecutionLabelsList) taskIndexList = [taskLabelsList.index(label) for label in customTaskExecutionLabelsList] print("#### Executing custom task list: %r" % [taskLabelsList[i] for i in taskIndexList]) # Run all experiment tasks for taskIndex in taskIndexList: task = experimentTasks[taskIndex] # Create a task runner and run it! taskRunner = _TaskRunner(model=model, task=task, cmdOptions=options) taskRunner.run() del taskRunner if options.privateOptions['checkpointModel']: _saveModel(model=model, experimentDir=experimentDir, checkpointLabel=task['taskLabel'], newSerialization=newSerialization) return model