def adapt_job_parameters(cls, role, job_parameters: RunParameters, create_initiator_baseline=False):
ResourceManager.adapt_engine_parameters(role=role, job_parameters=job_parameters, create_initiator_baseline=create_initiator_baseline)
if create_initiator_baseline:
if job_parameters.task_parallelism is None:
job_parameters.task_parallelism = DEFAULT_TASK_PARALLELISM
if job_parameters.federated_status_collect_type is None:
job_parameters.federated_status_collect_type = DEFAULT_FEDERATED_STATUS_COLLECT_TYPE
if create_initiator_baseline and not job_parameters.computing_partitions:
job_parameters.computing_partitions = job_parameters.adaptation_parameters["task_cores_per_node"] * job_parameters.adaptation_parameters["task_nodes"]
def adapt_job_parameters(cls, job_parameters: RunParameters):
if job_parameters.task_parallelism is None:
job_parameters.task_parallelism = DEFAULT_TASK_PARALLELISM
if job_parameters.federated_status_collect_type is None:
job_parameters.federated_status_collect_type = DEFAULT_FEDERATED_STATUS_COLLECT_TYPE
ResourceManager.job_engine_support_parameters(job_parameters=job_parameters)