def submit_calculation(calculation: CalcJobNode, transport: Transport) -> str:
"""Submit a previously uploaded `CalcJob` to the scheduler.
:param calculation: the instance of CalcJobNode to submit.
:param transport: an already opened transport to use to submit the calculation.
:return: the job id as returned by the scheduler `submit_from_script` call
"""
job_id = calculation.get_job_id()
# If the `job_id` attribute is already set, that means this function was already executed once and the scheduler
# submit command was successful as the job id it returned was set on the node. This scenario can happen when the
# daemon runner gets shutdown right after accomplishing the submission task, but before it gets the chance to
# finalize the state transition of the `CalcJob` to the `UPDATE` transport task. Since the job is already submitted
# we do not want to submit it a second time, so we simply return the existing job id here.
if job_id is not None:
return job_id
scheduler = calculation.computer.get_scheduler()
scheduler.set_transport(transport)
submit_script_filename = calculation.get_option('submit_script_filename')
workdir = calculation.get_remote_workdir()
job_id = scheduler.submit_from_script(workdir, submit_script_filename)
calculation.set_job_id(job_id)
return job_id
def stash_calculation(calculation: CalcJobNode, transport: Transport) -> None:
"""Stash files from the working directory of a completed calculation to a permanent remote folder.
After a calculation has been completed, optionally stash files from the work directory to a storage location on the
same remote machine. This is useful if one wants to keep certain files from a completed calculation to be removed
from the scratch directory, because they are necessary for restarts, but that are too heavy to retrieve.
Instructions of which files to copy where are retrieved from the `stash.source_list` option.
:param calculation: the calculation job node.
:param transport: an already opened transport.
"""
from aiida.common.datastructures import StashMode
from aiida.orm import RemoteStashFolderData
logger_extra = get_dblogger_extra(calculation)
stash_options = calculation.get_option('stash')
stash_mode = stash_options.get('mode', StashMode.COPY.value)
source_list = stash_options.get('source_list', [])
if not source_list:
return
if stash_mode != StashMode.COPY.value:
EXEC_LOGGER.warning(
f'stashing mode {stash_mode} is not implemented yet.')
return
cls = RemoteStashFolderData
EXEC_LOGGER.debug(
f'stashing files for calculation<{calculation.pk}>: {source_list}',
extra=logger_extra)
uuid = calculation.uuid
target_basepath = os.path.join(stash_options['target_base'], uuid[:2],
uuid[2:4], uuid[4:])
for source_filename in source_list:
source_filepath = os.path.join(calculation.get_remote_workdir(),
source_filename)
target_filepath = os.path.join(target_basepath, source_filename)
# If the source file is in a (nested) directory, create those directories first in the target directory
target_dirname = os.path.dirname(target_filepath)
transport.makedirs(target_dirname, ignore_existing=True)
try:
transport.copy(source_filepath, target_filepath)
except (IOError, ValueError) as exception:
EXEC_LOGGER.warning(
f'failed to stash {source_filepath} to {target_filepath}: {exception}'
)
else:
EXEC_LOGGER.debug(
f'stashed {source_filepath} to {target_filepath}')
remote_stash = cls(
computer=calculation.computer,
target_basepath=target_basepath,
stash_mode=StashMode(stash_mode),
source_list=source_list,
).store()
remote_stash.add_incoming(calculation,
link_type=LinkType.CREATE,
link_label='remote_stash')