def submit_jobs(self, job_files):
task = self.task
pool = get_param(task.htcondor_pool)
scheduler = get_param(task.htcondor_scheduler)
# prepare objects for dumping intermediate submission data
dump_freq = task.htcondor_dump_intermediate_submission_data()
if dump_freq and not is_number(dump_freq):
dump_freq = 50
# progress callback to inform the scheduler
def progress_callback(i, job_ids):
job_num = i + 1
job_id = job_ids[0]
# set the job id early
self.submission_data.jobs[job_num]["job_id"] = job_id
# log a message every 25 jobs
if job_num in (1, len(job_files)) or job_num % 25 == 0:
task.publish_message("submitted {}/{} job(s)".format(
job_num, len(job_files)))
# dump intermediate submission data with a certain frequency
if dump_freq and job_num % dump_freq == 0:
self.dump_submission_data()
return self.job_manager.submit_batch(job_files,
pool=pool,
scheduler=scheduler,
retries=3,
threads=task.threads,
callback=progress_callback)
def log(fn, opts, task, *args, **kwargs):
""" log()
Wraps a bound method of a task and redirects output of both stdin and stdout to the file
defined by the tasks's *log* parameter. It its value is ``"-"`` or *None*, the output is not
redirected.
"""
_task = get_task(task)
log = get_param(_task.log_file, _task.default_log_file)
if log == "-" or not log:
return fn(task, *args, **kwargs)
else:
# use the local target functionality to create the parent directory
LocalFileTarget(log).parent.touch()
with open(log, "a", 1) as f:
tee = TeeStream(f, sys.__stdout__)
sys.stdout = tee
sys.stderr = tee
try:
ret = fn(task, *args, **kwargs)
except Exception as e:
traceback.print_exc(file=tee)
raise e
finally:
sys.stdout = sys.__stdout__
sys.stderr = sys.__stderr__
tee.flush()
return ret
def _get_job_kwargs(self, name):
attr = "{}_job_kwargs_{}".format(self.workflow_type, name)
kwargs = getattr(self.task, attr, None)
if kwargs is None:
attr = "{}_job_kwargs".format(self.workflow_type)
kwargs = getattr(self.task, attr)
# when kwargs is not a dict, it is assumed to be a list whose
# elements represent task attributes that are stored without the workflow type prefix
if not isinstance(kwargs, dict):
_kwargs = {}
for param_name in kwargs:
kwarg_name = param_name
if param_name.startswith(self.workflow_type + "_"):
kwarg_name = param_name[len(self.workflow_type) + 1:]
_kwargs[kwarg_name] = get_param(getattr(self.task, param_name))
kwargs = _kwargs
return kwargs
