def add_job(self, wf, job_dag, mvm):
"""
Create a PythonTask, add it to the workflow, and update the DagNode to
contain a reference to the task.
Args:
wf (jobmon.client.swarm.workflow.workflow): jobmon workflow
job_dag (dict[str, DagNode]): a mapping of job name to DagNode
mvm (cascade_ode.importer.Importer.model_version_meta): a dataframe
of model settings
The varnish job does the following:
1. Uploads fits
2. Uploads adjusted data
3. Computes fit statistics
4. Uploads fit statistics
5. Attempts to generate diagnostic plots
5. Computes and uploads finals (save-results)
6. Updates the status of the model to finished
"""
slots, memory, runtime = sge.cluster_limits('varnish',
mvm,
details=self.details)
environment_variables_to_add = settings['env_variables']
environment_variables_to_add['OMP_NUM_THREADS'] = slots
# Varnish memory allocation shouldn't change with job size, and
# large memory allocations will prohibit scheduling.
_, runtime = sge.update_ME_specific_allocations(
modelable_entity_id=mvm.modelable_entity_id.unique()[0],
memory=memory,
runtime=runtime)
upstream_tasks = []
for upstream_jobname in self.upstream_jobs:
upstream_tasks.extend(job_dag[upstream_jobname].task)
task = PythonTask(script=self.script,
args=self.args,
name=self.job_name,
upstream_tasks=upstream_tasks,
env_variables=environment_variables_to_add,
num_cores=slots,
max_runtime_seconds=runtime,
queue=queue_from_runtime(runtime),
m_mem_free=f"{memory}G",
max_attempts=self.max_attempts,
tag='upload',
j_resource=True)
task.context_args = add_if_set(self.host_type)
self.task.append(task)
wf.add_task(task)
def add_job(self, wf, job_dag, mvm):
"""
Create a PythonTask, add it to the workflow, and update the DagNode to
contain a reference to the task.
Args:
wf (jobmon.client.swarm.workflow.workflow): jobmon workflow
job_dag (dict[str, DagNode]): a mapping of job name to DagNode
mvm (cascade_ode.importer.Importer.model_version_meta): a dataframe
of model settings
"""
num_children = self.details['num_children']
slots, memory, runtime = sge.cluster_limits('node',
mvm,
num_children,
details=self.details)
environment_variables_to_add = settings['env_variables']
environment_variables_to_add['OMP_NUM_THREADS'] = slots
memory, runtime = sge.update_ME_specific_allocations(
modelable_entity_id=mvm.modelable_entity_id.unique()[0],
memory=memory,
runtime=runtime)
upstream_tasks = []
for upstream_jobname in self.upstream_jobs:
upstream_tasks.extend(job_dag[upstream_jobname].task)
task = PythonTask(script=self.script,
args=self.args,
name=self.job_name,
upstream_tasks=upstream_tasks,
env_variables=environment_variables_to_add,
num_cores=slots,
max_runtime_seconds=runtime,
queue=queue_from_runtime(runtime),
m_mem_free=f"{memory}G",
max_attempts=self.max_attempts,
tag='node',
j_resource=False)
task.context_args = add_if_set(self.host_type)
self.task.append(task)
wf.add_task(task)
