# coding: utf-8
# Copyright (c) Max-Planck-Institut für Eisenforschung GmbH - Computational Materials Design (CM) Department
# Distributed under the terms of "New BSD License", see the LICENSE file.
from collections import OrderedDict
from pyiron.base.settings.generic import Settings
from pyiron.base.generic.template import PyironObject
from pyiron.base.server.runmode import Runmode
import socket
"""
Server object class which is connected to each job containing the technical details how the job is executed.
"""
__author__ = "Jan Janssen"
__copyright__ = (
"Copyright 2020, Max-Planck-Institut für Eisenforschung GmbH - "
"Computational Materials Design (CM) Department"
)
__version__ = "1.0"
__maintainer__ = "Jan Janssen"
__email__ = "janssen@mpie.de"
__status__ = "production"
__date__ = "Sep 1, 2017"
s = Settings()
[docs]class Server(
PyironObject
): # add the option to return the job id and the hold id to the server object
"""
Generic Server object to handle the execution environment for the job
Args:
host (str): hostname of the local machine
queue (str): queue name of the currently selected queue
cores (int): number of cores
run_mode (pyiron.base.server.runmode.Runmode): mode of the job ['modal', 'non_modal', 'queue', 'manual']
new_hdf (bool): create a new HDF5 file [True/False] - default=True
Attributes:
.. attribute:: send_to_db
boolean option to decide which jobs should be store in the external/public database.
.. attribute:: structure_id
the structure ID to be linked to an external/public database.
.. attribute:: host
the hostname of the current system.
.. attribute:: queue
the que selected for a current simulation.
.. attribute:: cores
the number of cores selected for the current simulation.
.. attribute:: run_time
the run time in seconds selected for the current simulation.
.. attribute:: run_mode
the run mode of the job ['modal', 'non_modal', 'queue', 'manual']
.. attribute:: new_hdf
defines whether a subjob should be stored in the same HDF5 file or in a new one.
"""
def __init__(
self, host=None, queue=None, cores=1, threads=1, run_mode="modal", new_hdf=True
):
self._cores = cores
self._threads = threads
self._run_time = None
self._memory_limit = None
self._host = self._init_host(host=host)
self._active_queue = queue
self._user = s.login_user
self._run_mode = Runmode()
self.run_mode = run_mode
self._queue_id = None
self._new_hdf = new_hdf
self._send_to_db = False
self._structure_id = None
self._accept_crash = False
@property
def send_to_db(self):
"""
Get the boolean option to decide which jobs should be store in the external/public database
Returns:
bool: [True/False]
"""
return self._send_to_db
@send_to_db.setter
def send_to_db(self, send):
"""
Set the boolean option to decide which jobs should be store in the external/public database
Args:
send (bool): [True/False]
"""
self._send_to_db = send
@property
def accept_crash(self):
return self._accept_crash
@accept_crash.setter
def accept_crash(self, accept):
self._accept_crash = accept
@property
def structure_id(self):
"""
Get the structure ID to be linked to an external/public database
Returns:
int: structure ID
"""
return self._structure_id
@structure_id.setter
def structure_id(self, structure_id):
"""
Set the structure ID to be linked to an external/public database
Args:
structure_id (int): structure ID
"""
self._structure_id = structure_id
@property
def queue(self):
"""
The que selected for a current simulation
Returns:
(str): schedulers_name
"""
return self._active_queue
@queue.setter
def queue(self, new_scheduler):
"""
Set a que for the current simulation, by choosing one of the available que_names
Args:
new_scheduler (str): scheduler name
"""
if s.queue_adapter is not None:
cores, run_time_max, memory_max = s.queue_adapter.check_queue_parameters(
queue=new_scheduler,
cores=self.cores,
run_time_max=self.run_time,
memory_max=self.memory_limit,
)
if cores != self.cores:
self._cores = cores
s.logger.debug(
"Updated the number of cores to: ", cores
)
if run_time_max != self.run_time:
self._run_time = run_time_max
s.logger.debug(
"Updated the run time limit to: ", run_time_max
)
if memory_max != self.memory_limit:
self._memory_limit = memory_max
s.logger.debug(
"Updated the memory limit to: ", memory_max
)
self._active_queue = new_scheduler
self.run_mode = "queue"
else:
raise TypeError("No queue adapter defined.")
@property
def queue_id(self):
"""
Get the queue ID - the ID in the queuing system is most likely not the same as the job ID.
Returns:
int: queue ID
"""
return self._queue_id
@queue_id.setter
def queue_id(self, qid):
"""
Set the queue ID
Args:
qid (int): queue ID
"""
self._queue_id = int(qid)
@property
def threads(self):
return self._threads
@threads.setter
def threads(self, number_of_threads):
self._threads = number_of_threads
@property
def cores(self):
"""
The number of cores selected for the current simulation
Returns:
(int): number of cores
"""
return self._cores
@cores.setter
def cores(self, new_cores):
"""
The number of cores selected for the current simulation
Args:
new_cores (int): number of cores
"""
if s.queue_adapter is not None and self._active_queue is not None:
cores = s.queue_adapter.check_queue_parameters(
queue=self.queue,
cores=new_cores,
run_time_max=self.run_time,
memory_max=self.memory_limit,
)[0]
if cores != new_cores:
self._cores = cores
s.logger.debug(
"Updated the number of cores to: ", cores
)
else:
self._cores = new_cores
else:
self._cores = new_cores
@property
def run_time(self):
"""
The run time in seconds selected for the current simulation
Returns:
(int): run time in seconds
"""
return self._run_time
@run_time.setter
def run_time(self, new_run_time):
"""
The run time in seconds selected for the current simulation
Args:
new_run_time (int): run time in seconds
"""
if s.queue_adapter is not None and self._active_queue is not None:
run_time_max = s.queue_adapter.check_queue_parameters(
queue=self.queue,
cores=self.cores,
run_time_max=new_run_time,
memory_max=self.memory_limit,
)[1]
if run_time_max != new_run_time:
self._run_time = run_time_max
s.logger.debug(
"Updated the run time limit to: ", run_time_max
)
else:
self._run_time = new_run_time
else:
self._run_time = new_run_time
@property
def memory_limit(self):
return self._memory_limit
@memory_limit.setter
def memory_limit(self, limit):
if s.queue_adapter is not None and self._active_queue is not None:
memory_max = s.queue_adapter.check_queue_parameters(
queue=self.queue,
cores=self.cores,
run_time_max=self.run_time,
memory_max=limit,
)[2]
if memory_max != limit:
self._memory_limit = memory_max
s.logger.debug(
"Updated the memory limit to: ", memory_max
)
else:
self._memory_limit = limit
else:
self._memory_limit = limit
@property
def run_mode(self):
"""
Get the run mode of the job
Returns:
(str/pyiron.base.server.runmode.Runmode): ['modal', 'non_modal', 'queue', 'manual']
"""
return self._run_mode
@run_mode.setter
def run_mode(self, new_mode):
"""
Set the run mode of the job
Args:
new_mode (str): ['modal', 'non_modal', 'queue', 'manual']
"""
self._run_mode.mode = new_mode
if new_mode == "queue":
if s.queue_adapter is None:
raise TypeError("No queue adapter defined.")
if self._active_queue is None:
self.queue = s.queue_adapter.config["queue_primary"]
@property
def new_hdf(self):
"""
New_hdf5 defines whether a subjob should be stored in the same HDF5 file or in a new one.
Returns:
(bool): [True / False]
"""
return self._new_hdf
@new_hdf.setter
def new_hdf(self, new_hdf_bool):
"""
New_hdf5 defines whether a subjob should be stored in the same HDF5 file or in a new one.
Args:
new_hdf_bool (bool): [True / False]
"""
if isinstance(new_hdf_bool, bool):
self._new_hdf = new_hdf_bool
else:
raise TypeError(
"The new_hdf5 is a boolean property, defining whether subjobs are stored in the same file."
)
@property
def queue_list(self):
"""
List the available Job scheduler provided by the system.
Returns:
(list)
"""
return self.list_queues()
@property
def queue_view(self):
"""
List the available Job scheduler provided by the system.
Returns:
(pandas.DataFrame)
"""
return self.view_queues()
[docs] @staticmethod
def list_queues():
"""
List the available Job scheduler provided by the system.
Returns:
(list)
"""
if s.queue_adapter is not None:
return s.queue_adapter.queue_list
else:
return None
[docs] @staticmethod
def view_queues():
"""
List the available Job scheduler provided by the system.
Returns:
(pandas.DataFrame)
"""
if s.queue_adapter is not None:
return s.queue_adapter.queue_view
else:
return None
[docs] def to_hdf(self, hdf, group_name=None):
"""
Store Server object in HDF5 file
Args:
hdf: HDF5 object
group_name (str): node name in the HDF5 file
"""
hdf_dict = OrderedDict()
hdf_dict["user"] = self._user
hdf_dict["host"] = self._host
hdf_dict["run_mode"] = self.run_mode.mode
hdf_dict["queue"] = self.queue
hdf_dict["qid"] = self._queue_id
hdf_dict["cores"] = self.cores
hdf_dict["threads"] = self.threads
hdf_dict["new_h5"] = self.new_hdf
hdf_dict["structure_id"] = self.structure_id
hdf_dict["run_time"] = self.run_time
hdf_dict["memory_limit"] = self.memory_limit
hdf_dict["accept_crash"] = self.accept_crash
if group_name:
with hdf.open(group_name) as hdf_group:
hdf_group["server"] = hdf_dict
else:
hdf["server"] = hdf_dict
[docs] def from_hdf(self, hdf, group_name=None):
"""
Recover Server object in HDF5 file
Args:
hdf: HDF5 object
group_name: node name in the HDF5 file
"""
if group_name:
with hdf.open(group_name) as hdf_group:
hdf_dict = hdf_group["server"]
else:
hdf_dict = hdf["server"]
self._user = hdf_dict["user"]
self._host = hdf_dict["host"]
self._run_mode.mode = hdf_dict["run_mode"]
if self.run_mode.queue:
self._active_queue = hdf_dict["queue"]
if "qid" in hdf_dict.keys():
self._queue_id = hdf_dict["qid"]
else:
self._queue_id = None
if "structure_id" in hdf_dict.keys():
self._structure_id = hdf_dict["structure_id"]
self._cores = hdf_dict["cores"]
if "run_time" in hdf_dict.keys():
self._run_time = hdf_dict["run_time"]
if "memory_limit" in hdf_dict.keys():
self._memory_limit = hdf_dict["memory_limit"]
if "accept_crash" in hdf_dict.keys():
self._accept_crash = hdf_dict["accept_crash"] == 1
if "threads" in hdf_dict.keys():
self._threads = hdf_dict["threads"]
self._new_hdf = hdf_dict["new_h5"] == 1
[docs] def db_entry(self):
"""
connect all the info regarding the server into a single word that can be used e.g. as entry in a database
Returns:
(str): server info as single word
"""
if self.run_mode.queue:
server_lst = [self._host, str(self.cores), self.queue]
else:
server_lst = [self._host, str(self.cores)]
return self._user + "@" + "#".join(server_lst)
def __del__(self):
"""
Delete the Server object from memory
"""
del self._cores
del self._threads
del self._run_time
del self._memory_limit
del self._host
del self._active_queue
del self._user
del self._run_mode
del self._queue_id
del self._new_hdf
del self._send_to_db
del self._structure_id
del self._accept_crash
@staticmethod
def _init_host(host):
if host is None:
return socket.gethostname()
else:
return host