def runCloud(self, data):
self.user_data.set_selected(1)
service = backendservices(self.user_data)
if not service.isOneOrMoreComputeNodesRunning():
raise Exception(
'No cloud computing resources found. (Have they been started?)'
)
# If the seed is negative, this means choose a seed >= 0 randomly
if int(data['seed']) < 0:
random.seed()
data['seed'] = random.randint(0, 2147483647)
pymodel = self.construct_pyurdme_model(data)
#logging.info('DATA: {0}'.format(data))
#####
cloud_params = {
"job_type": "spatial",
"simulation_algorithm": data['algorithm'],
"simulation_realizations": data['realizations'],
"simulation_seed": data['seed'],
# "bucketname" : self.user_data.getBucketName(), #implys EC2, should be in backendservices
"paramstring": '',
}
logging.debug('cloud_params = {}'.format(pprint.pformat(cloud_params)))
cloud_params['document'] = pickle.dumps(pymodel)
#logging.debug('PYURDME: {0}'.format(cloud_params['document']))
# Send the task to the backend
cloud_result = service.submit_cloud_task(params=cloud_params)
if not cloud_result["success"]:
e = cloud_result["exception"]
raise Exception("Cloud execution failed: {0}".format(e))
celery_task_id = cloud_result["celery_pid"]
taskid = cloud_result["db_id"]
job = SpatialJobWrapper()
job.type = 'PyURDME Ensemble'
job.user_id = self.user.user_id()
job.startTime = time.strftime("%Y-%m-%d-%H-%M-%S")
job.name = data["jobName"]
job.indata = json.dumps(data)
job.outData = None # This is where the data should be locally, when we get data from cloud, it must be put here
job.modelName = pymodel.name
job.resource = cloud_result['resource']
job.cloudDatabaseID = taskid
job.celeryPID = celery_task_id
job.status = "Running"
job.output_stored = "True"
job.put()
return job
def runLocal(self, data):
''' Run a PyURDME run using local compute recources. '''
self.user_data.set_selected(0)
#####
pymodel = self.construct_pyurdme_model(data)
#####
simulation_algorithm = data[
'algorithm'] # Don't trust this! I haven't implemented the algorithm selection for this yet
simulation_realizations = data['realizations']
# If the seed is negative, this means choose a seed >= 0 randomly
if int(data['seed']) < 0:
random.seed()
data['seed'] = random.randint(0, 2147483647)
simulation_seed = data['seed']
#####
path = os.path.abspath(os.path.dirname(__file__))
basedir = path + '/../'
dataDir = tempfile.mkdtemp(dir=basedir + 'output')
job = SpatialJobWrapper()
job.user_id = self.user.user_id()
job.startTime = time.strftime("%Y-%m-%d-%H-%M-%S")
job.name = data["jobName"]
job.indata = json.dumps(data)
job.outData = dataDir
job.modelName = pymodel.name
job.resource = "local"
job.status = "Running"
model_file_pkl = "{0}/model_file.pkl".format(dataDir)
result_dir = "{0}/results/".format(dataDir)
os.makedirs(result_dir)
# searilize the model and write it to a file in the data dir
with open(model_file_pkl, 'w') as fd:
pickle.dump(pymodel, fd)
cmd = "{0}/../../pyurdme/pyurdme_wrapper.py {1} {2} {3} {4} {5}".format(
path, model_file_pkl, result_dir, simulation_algorithm,
simulation_realizations, simulation_seed)
logging.info("cmd =\n{}".format(cmd))
exstring = '{0}/backend/wrapper.py {1}/stdout.log {1}/stderr.log {1}/return_code {2}'.format(
basedir, dataDir, cmd)
handle = subprocess.Popen(exstring, shell=True, preexec_fn=os.setsid)
job.pid = int(handle.pid)
job.put()
return job
def runQsub(self, data, cluster_info):
logging.error("*" * 80)
logging.error("simulate.runQsub() modelType={0}".format(
data['execType']))
logging.error("*" * 80)
modelDb = StochKitModelWrapper.get_by_id(int(data["id"]))
path = os.path.abspath(os.path.dirname(__file__))
basedir = path + '/../'
dataDir = tempfile.mkdtemp(dir=basedir + 'output')
job = SpatialJobWrapper()
job.user_id = self.user.user_id()
job.startTime = time.strftime("%Y-%m-%d-%H-%M-%S")
job.name = data["jobName"]
job.indata = json.dumps(data)
job.modelName = modelDb.name
job.outData = dataDir
job.status = "Pending"
job.output_stored = "False"
job.is_spatial = True
try:
templateData = {
"name": modelDb.name,
"modelType": modelDb.type,
"species": modelDb.species,
"parameters": modelDb.parameters,
"reactions": modelDb.reactions,
# "speciesSelect": data['speciesSelect'],
"speciesSelect": data['selections'],
# "maxTime": data['maxTime'],
"maxTime": data['time'],
"increment": data['increment'],
# "trajectories": data['trajectories'],
"trajectories": data['realizations'],
"seed": data['seed'],
"isSpatial": modelDb.isSpatial,
"isLocal": True
}
if modelDb.isSpatial:
try:
meshWrapperDb = mesheditor.MeshWrapper.get_by_id(
modelDb.spatial["mesh_wrapper_id"])
except Exception as e:
logging.exception(e)
logging.error(
"No Mesh file set. Choose one in the Mesh tab of the Model Editor"
)
raise Exception(
"No Mesh file set. Choose one in the Mesh tab of the Model Editor"
)
try:
meshFileObj = fileserver.FileManager.getFile(
self, meshWrapperDb.meshFileId, noFile=False)
templateData["mesh"] = meshFileObj["data"]
except IOError as e:
logging.exception(e)
logging.error("Mesh file inaccessible. Try another mesh")
raise Exception("Mesh file inaccessible. Try another mesh")
templateData[
'reaction_subdomain_assignments'] = modelDb.spatial[
"reactions_subdomain_assignments"]
templateData[
'species_subdomain_assignments'] = modelDb.spatial[
"species_subdomain_assignments"]
templateData[
'species_diffusion_coefficients'] = modelDb.spatial[
"species_diffusion_coefficients"]
templateData['initial_conditions'] = modelDb.spatial[
"initial_conditions"]
templateData['subdomains'] = meshWrapperDb.subdomains
if data['execType'] == "stochastic":
job.qsubHandle = pickle.dumps(
parametersweep_qsub.stochastic(
templateData,
cluster_info,
not_full_parameter_sweep=True))
elif data['execType'] == "deterministic":
job.qsubHandle = pickle.dumps(
parametersweep_qsub.deterministic(
templateData,
cluster_info,
not_full_parameter_sweep=True))
elif data['execType'] == "spatial":
job.qsubHandle = pickle.dumps(
parametersweep_qsub.spatial(
templateData,
cluster_info,
not_full_parameter_sweep=True)) #
else:
raise Exception(
"Trying to runQsub on unsupported modelType {0}".format(
data['modelType']))
job.resource = "qsub"
job.put()
except Exception as e:
logging.exception(e)
job.status = 'Failed'
#job.delete(self)
raise
return job
