Source code for aiida.schedulers.datastructures

# -*- coding: utf-8 -*-
###########################################################################
# Copyright (c), The AiiDA team. All rights reserved.                     #
# This file is part of the AiiDA code.                                    #
#                                                                         #
# The code is hosted on GitHub at https://github.com/aiidateam/aiida-core #
# For further information on the license, see the LICENSE.txt file        #
# For further information please visit http://www.aiida.net               #
###########################################################################
"""
This module defines the main data structures used by the Scheduler.

In particular, there is the definition of possible job states (job_states),
the data structure to be filled for job submission (JobTemplate), and
the data structure that is returned when querying for jobs in the scheduler
(JobInfo).
"""

from enum import Enum

from aiida.common import AIIDA_LOGGER
from aiida.common.extendeddicts import DefaultFieldsAttributeDict

SCHEDULER_LOGGER = AIIDA_LOGGER.getChild('scheduler')

__all__ = (
    'JobState', 'JobResource', 'JobTemplate', 'JobInfo', 'NodeNumberJobResource', 'ParEnvJobResource', 'MachineInfo'
)


[docs]class JobState(Enum): """Enumeration of possible scheduler states of a CalcJob. There is no FAILED state as every completed job is put in DONE, regardless of success. """ UNDETERMINED = 'undetermined' QUEUED = 'queued' QUEUED_HELD = 'queued held' RUNNING = 'running' SUSPENDED = 'suspended' DONE = 'done'
[docs]class JobResource(DefaultFieldsAttributeDict): """ A class to store the job resources. It must be inherited and redefined by the specific plugin, that should contain a ``_job_resource_class`` attribute pointing to the correct JobResource subclass. It should at least define the get_tot_num_mpiprocs() method, plus an __init__ to accept its set of variables. Typical attributes are: * ``num_machines`` * ``num_mpiprocs_per_machine`` or (e.g. for SGE) * ``tot_num_mpiprocs`` * ``parallel_env`` The __init__ should take care of checking the values. The init should raise only ValueError or TypeError on invalid parameters. """ _default_fields = tuple()
[docs] @classmethod def accepts_default_mpiprocs_per_machine(cls): """ Return True if this JobResource accepts a 'default_mpiprocs_per_machine' key, False otherwise. Should be implemented in each subclass. """ raise NotImplementedError
[docs] @classmethod def get_valid_keys(cls): """ Return a list of valid keys to be passed to the __init__ """ return list(cls._default_fields)
[docs] def get_tot_num_mpiprocs(self): """ Return the total number of cpus of this job resource. """ raise NotImplementedError
[docs]class NodeNumberJobResource(JobResource): """ An implementation of JobResource for schedulers that support the specification of a number of nodes and a number of cpus per node """ _default_fields = ( 'num_machines', 'num_mpiprocs_per_machine', 'num_cores_per_machine', 'num_cores_per_mpiproc', )
[docs] @classmethod def get_valid_keys(cls): """ Return a list of valid keys to be passed to the __init__ """ return super().get_valid_keys() + ['tot_num_mpiprocs', 'default_mpiprocs_per_machine']
[docs] @classmethod def accepts_default_mpiprocs_per_machine(cls): """ Return True if this JobResource accepts a 'default_mpiprocs_per_machine' key, False otherwise. """ return True
[docs] def __init__(self, **kwargs): # pylint: disable=too-many-branches,too-many-statements """ Initialize the job resources from the passed arguments (the valid keys can be obtained with the function self.get_valid_keys()). Should raise only ValueError or TypeError on invalid parameters. """ super().__init__() try: num_machines = int(kwargs.pop('num_machines')) except KeyError: num_machines = None except ValueError: raise ValueError('num_machines must an integer') try: default_mpiprocs_per_machine = kwargs.pop('default_mpiprocs_per_machine') if default_mpiprocs_per_machine is not None: default_mpiprocs_per_machine = int(default_mpiprocs_per_machine) except KeyError: default_mpiprocs_per_machine = None except ValueError: raise ValueError('default_mpiprocs_per_machine must an integer') try: num_mpiprocs_per_machine = int(kwargs.pop('num_mpiprocs_per_machine')) except KeyError: num_mpiprocs_per_machine = None except ValueError: raise ValueError('num_mpiprocs_per_machine must an integer') try: tot_num_mpiprocs = int(kwargs.pop('tot_num_mpiprocs')) except KeyError: tot_num_mpiprocs = None except ValueError: raise ValueError('tot_num_mpiprocs must an integer') try: self.num_cores_per_machine = int(kwargs.pop('num_cores_per_machine')) except KeyError: self.num_cores_per_machine = None except ValueError: raise ValueError('num_cores_per_machine must an integer') try: self.num_cores_per_mpiproc = int(kwargs.pop('num_cores_per_mpiproc')) except KeyError: self.num_cores_per_mpiproc = None except ValueError: raise ValueError('num_cores_per_mpiproc must an integer') if kwargs: raise TypeError( 'The following parameters were not recognized for ' 'the JobResource: {}'.format(kwargs.keys()) ) if num_machines is None: # Use default value, if not provided if num_mpiprocs_per_machine is None: num_mpiprocs_per_machine = default_mpiprocs_per_machine if num_mpiprocs_per_machine is None or tot_num_mpiprocs is None: raise TypeError( 'At least two among num_machines, ' 'num_mpiprocs_per_machine or tot_num_mpiprocs must be specified' ) else: # To avoid divisions by zero if num_mpiprocs_per_machine <= 0: raise ValueError('num_mpiprocs_per_machine must be >= 1') num_machines = tot_num_mpiprocs // num_mpiprocs_per_machine else: if tot_num_mpiprocs is None: # Only set the default value if tot_num_mpiprocs is not provided. # Otherwise, it means that the user provided both # num_machines and tot_num_mpiprocs, and we have to ignore # the default value of tot_num_mpiprocs if num_mpiprocs_per_machine is None: num_mpiprocs_per_machine = default_mpiprocs_per_machine if num_mpiprocs_per_machine is None: if tot_num_mpiprocs is None: raise TypeError( 'At least two among num_machines, ' 'num_mpiprocs_per_machine or tot_num_mpiprocs must be specified' ) else: # To avoid divisions by zero if num_machines <= 0: raise ValueError('num_machines must be >= 1') num_mpiprocs_per_machine = tot_num_mpiprocs // num_machines self.num_machines = num_machines self.num_mpiprocs_per_machine = num_mpiprocs_per_machine if tot_num_mpiprocs is not None: if tot_num_mpiprocs != self.num_mpiprocs_per_machine * self.num_machines: raise ValueError( 'tot_num_mpiprocs must be equal to ' 'num_mpiprocs_per_machine * num_machines, and in particular it ' 'should be a multiple of num_mpiprocs_per_machine and/or ' 'num_machines' ) if self.num_mpiprocs_per_machine <= 0: raise ValueError('num_mpiprocs_per_machine must be >= 1') if self.num_machines <= 0: raise ValueError('num_machine must be >= 1')
[docs] def get_tot_num_mpiprocs(self): """ Return the total number of cpus of this job resource. """ return self.num_machines * self.num_mpiprocs_per_machine
[docs]class ParEnvJobResource(JobResource): """ An implementation of JobResource for schedulers that support the specification of a parallel environment (a string) + the total number of nodes """ _default_fields = ( 'parallel_env', 'tot_num_mpiprocs', 'default_mpiprocs_per_machine', )
[docs] def __init__(self, **kwargs): """ Initialize the job resources from the passed arguments (the valid keys can be obtained with the function self.get_valid_keys()). :raise ValueError: on invalid parameters. :raise TypeError: on invalid parameters. :raise aiida.common.ConfigurationError: if default_mpiprocs_per_machine was set for this computer, since ParEnvJobResource cannot accept this parameter. """ from aiida.common.exceptions import ConfigurationError super().__init__() try: self.parallel_env = str(kwargs.pop('parallel_env')) except (KeyError, TypeError, ValueError): raise TypeError("'parallel_env' must be specified and must be a string") try: self.tot_num_mpiprocs = int(kwargs.pop('tot_num_mpiprocs')) except (KeyError, ValueError): raise TypeError('tot_num_mpiprocs must be specified and must be an integer') default_mpiprocs_per_machine = kwargs.pop('default_mpiprocs_per_machine', None) if default_mpiprocs_per_machine is not None: raise ConfigurationError( 'default_mpiprocs_per_machine cannot be set ' 'for schedulers that use ParEnvJobResource' ) if self.tot_num_mpiprocs <= 0: raise ValueError('tot_num_mpiprocs must be >= 1')
[docs] def get_tot_num_mpiprocs(self): """ Return the total number of cpus of this job resource. """ return self.tot_num_mpiprocs
[docs] @classmethod def accepts_default_mpiprocs_per_machine(cls): """ Return True if this JobResource accepts a 'default_mpiprocs_per_machine' key, False otherwise. """ return False
[docs]class JobTemplate(DefaultFieldsAttributeDict): # pylint: disable=too-many-instance-attributes """ A template for submitting jobs. This contains all required information to create the job header. The required fields are: working_directory, job_name, num_machines, num_mpiprocs_per_machine, argv. Fields: * ``shebang line``: The first line of the submission script * ``submit_as_hold``: if set, the job will be in a 'hold' status right after the submission * ``rerunnable``: if the job is rerunnable (boolean) * ``job_environment``: a dictionary with environment variables to set before the execution of the code. * ``working_directory``: the working directory for this job. During submission, the transport will first do a 'chdir' to this directory, and then possibly set a scheduler parameter, if this is supported by the scheduler. * ``email``: an email address for sending emails on job events. * ``email_on_started``: if True, ask the scheduler to send an email when the job starts. * ``email_on_terminated``: if True, ask the scheduler to send an email when the job ends. This should also send emails on job failure, when possible. * ``job_name``: the name of this job. The actual name of the job can be different from the one specified here, e.g. if there are unsupported characters, or the name is too long. * ``sched_output_path``: a (relative) file name for the stdout of this job * ``sched_error_path``: a (relative) file name for the stdout of this job * ``sched_join_files``: if True, write both stdout and stderr on the same file (the one specified for stdout) * ``queue_name``: the name of the scheduler queue (sometimes also called partition), on which the job will be submitted. * ``account``: the name of the scheduler account (sometimes also called projectid), on which the job will be submitted. * ``qos``: the quality of service of the scheduler account, on which the job will be submitted. * ``job_resource``: a suitable :py:class:`JobResource` subclass with information on how many nodes and cpus it should use. It must be an instance of the ``aiida.schedulers.Scheduler.job_resource_class`` class. Use the Scheduler.create_job_resource method to create it. * ``num_machines``: how many machines (or nodes) should be used * ``num_mpiprocs_per_machine``: how many MPI procs should be used on each machine (or node). * ``priority``: a priority for this job. Should be in the format accepted by the specific scheduler. * ``max_memory_kb``: The maximum amount of memory the job is allowed to allocate ON EACH NODE, in kilobytes * ``max_wallclock_seconds``: The maximum wall clock time that all processes of a job are allowed to exist, in seconds * ``custom_scheduler_commands``: a string that will be inserted right after the last scheduler command, and before any other non-scheduler command; useful if some specific flag needs to be added and is not supported by the plugin * ``prepend_text``: a (possibly multi-line) string to be inserted in the scheduler script before the main execution line * ``append_text``: a (possibly multi-line) string to be inserted in the scheduler script after the main execution line * ``import_sys_environment``: import the system environment variables * ``codes_info``: a list of aiida.common.datastructures.CalcInfo objects. Each contains the information necessary to run a single code. At the moment, it can contain: * ``cmdline_parameters``: a list of strings with the command line arguments of the program to run. This is the main program to be executed. NOTE: The first one is the executable name. For MPI runs, this will probably be "mpirun" or a similar program; this has to be chosen at a upper level. * ``stdin_name``: the (relative) file name to be used as stdin for the program specified with argv. * ``stdout_name``: the (relative) file name to be used as stdout for the program specified with argv. * ``stderr_name``: the (relative) file name to be used as stderr for the program specified with argv. * ``join_files``: if True, stderr is redirected on the same file specified for stdout. * ``codes_run_mode``: sets the run_mode with which the (multiple) codes have to be executed. For example, parallel execution:: mpirun -np 8 a.x & mpirun -np 8 b.x & wait The serial execution would be without the &'s. Values are given by aiida.common.datastructures.CodeRunMode. """ _default_fields = ( 'shebang', 'submit_as_hold', 'rerunnable', 'job_environment', 'working_directory', 'email', 'email_on_started', 'email_on_terminated', 'job_name', 'sched_output_path', 'sched_error_path', 'sched_join_files', 'queue_name', 'account', 'qos', 'job_resource', 'priority', 'max_memory_kb', 'max_wallclock_seconds', 'custom_scheduler_commands', 'prepend_text', 'append_text', 'import_sys_environment', 'codes_run_mode', 'codes_info', )
[docs]class MachineInfo(DefaultFieldsAttributeDict): """ Similarly to what is defined in the DRMAA v.2 as SlotInfo; this identifies each machine (also called 'node' on some schedulers) on which a job is running, and how many CPUs are being used. (Some of them could be undefined) * ``name``: name of the machine * ``num_cpus``: number of cores used by the job on this machine * ``num_mpiprocs``: number of MPI processes used by the job on this machine """ _default_fields = ( 'name', 'num_mpiprocs', 'num_cpus', )
[docs]class JobInfo(DefaultFieldsAttributeDict): # pylint: disable=too-many-instance-attributes """ Contains properties for a job in the queue. Most of the fields are taken from DRMAA v.2. Note that default fields may be undefined. This is an expected behavior and the application must cope with this case. An example for instance is the exit_status for jobs that have not finished yet; or features not supported by the given scheduler. Fields: * ``job_id``: the job ID on the scheduler * ``title``: the job title, as known by the scheduler * ``exit_status``: the exit status of the job as reported by the operating system on the execution host * ``terminating_signal``: the UNIX signal that was responsible for the end of the job. * ``annotation``: human-readable description of the reason for the job being in the current state or substate. * ``job_state``: the job state (one of those defined in ``aiida.schedulers.datastructures.JobState``) * ``job_substate``: a string with the implementation-specific sub-state * ``allocated_machines``: a list of machines used for the current job. This is a list of :py:class:`aiida.schedulers.datastructures.MachineInfo` objects. * ``job_owner``: the job owner as reported by the scheduler * ``num_mpiprocs``: the *total* number of requested MPI procs * ``num_cpus``: the *total* number of requested CPUs (cores) [may be undefined] * ``num_machines``: the number of machines (i.e., nodes), required by the job. If ``allocated_machines`` is not None, this number must be equal to ``len(allocated_machines)``. Otherwise, for schedulers not supporting the retrieval of the full list of allocated machines, this attribute can be used to know at least the number of machines. * ``queue_name``: The name of the queue in which the job is queued or running. * ``account``: The account/projectid in which the job is queued or running in. * ``qos``: The quality of service in which the job is queued or running in. * ``wallclock_time_seconds``: the accumulated wallclock time, in seconds * ``requested_wallclock_time_seconds``: the requested wallclock time, in seconds * ``cpu_time``: the accumulated cpu time, in seconds * ``submission_time``: the absolute time at which the job was submitted, of type datetime.datetime * ``dispatch_time``: the absolute time at which the job first entered the 'started' state, of type datetime.datetime * ``finish_time``: the absolute time at which the job first entered the 'finished' state, of type datetime.datetime """ _default_fields = ( 'job_id', 'title', 'exit_status', 'terminating_signal', 'annotation', 'job_state', 'job_substate', 'allocated_machines', 'job_owner', 'num_mpiprocs', 'num_cpus', 'num_machines', 'queue_name', 'account', 'qos', 'wallclock_time_seconds', 'requested_wallclock_time_seconds', 'cpu_time', 'submission_time', 'dispatch_time', 'finish_time' ) # If some fields require special serializers, specify them here. # You then need to define also the respective _serialize_FIELDTYPE and # _deserialize_FIELDTYPE methods _special_serializers = { 'submission_time': 'date', 'dispatch_time': 'date', 'finish_time': 'date', 'job_state': 'job_state', }
[docs] @staticmethod def _serialize_job_state(job_state): """Return the serialized value of the JobState instance.""" if not isinstance(job_state, JobState): raise TypeError('invalid type for value {}, should be an instance of `JobState`'.format(job_state)) return job_state.value
[docs] @staticmethod def _deserialize_job_state(job_state): """Return an instance of JobState from the job_state string.""" return JobState(job_state)
[docs] @staticmethod def _serialize_date(value): """ Serialise a data value :param value: The value to serialise :return: The serialised value """ import datetime import pytz if value is None: return value if not isinstance(value, datetime.datetime): raise TypeError('Invalid type for the date, should be a datetime') # is_naive check from django.utils.timezone if value.tzinfo is None or value.tzinfo.utcoffset(value) is None: SCHEDULER_LOGGER.debug('Datetime to serialize in JobInfo is naive, this should be fixed!') # v = v.replace(tzinfo = pytz.utc) return {'date': value.strftime('%Y-%m-%dT%H:%M:%S.%f'), 'timezone': None} return {'date': value.astimezone(pytz.utc).strftime('%Y-%m-%dT%H:%M:%S.%f'), 'timezone': 'UTC'}
[docs] @staticmethod def _deserialize_date(value): """ Deserialise a date :param value: The date vlue :return: The deserialised date """ import datetime import pytz if value is None: return value if value['timezone'] is None: # naive date return datetime.datetime.strptime(value['date'], '%Y-%m-%dT%H:%M:%S.%f') if value['timezone'] == 'UTC': return datetime.datetime.strptime(value['date'], '%Y-%m-%dT%H:%M:%S.%f').replace(tzinfo=pytz.utc) # Try your best return datetime.datetime.strptime(value['date'], '%Y-%m-%dT%H:%M:%S.%f').replace(tzinfo=pytz.timezone(value['timezone']))
[docs] @classmethod def serialize_field(cls, value, field_type): """ Serialise a particular field value :param value: The value to serialise :param field_type: The field type :return: The serialised value """ if field_type is None: return value serializer_method = getattr(cls, '_serialize_{}'.format(field_type)) return serializer_method(value)
[docs] @classmethod def deserialize_field(cls, value, field_type): """ Deserialise the value of a particular field with a type :param value: The value :param field_type: The field type :return: The deserialised value """ if field_type is None: return value deserializer_method = getattr(cls, '_deserialize_{}'.format(field_type)) return deserializer_method(value)
[docs] def serialize(self): """ Serialize the current data (as obtained by ``self.get_dict()``) into a JSON string. :return: A string with serialised representation of the current data. """ from aiida.common import json return json.dumps(self.get_dict())
[docs] def get_dict(self): """ Serialise the current data into a dictionary that is JSON-serializable. :return: A dictionary """ return {k: self.serialize_field(v, self._special_serializers.get(k, None)) for k, v in self.items()}
[docs] @classmethod def load_from_dict(cls, data): """ Create a new instance loading the values from serialised data in dictionary form :param data: The dictionary with the data to load from """ instance = cls() for key, value in data.items(): instance[key] = cls.deserialize_field(value, cls._special_serializers.get(key, None)) return instance
[docs] @classmethod def load_from_serialized(cls, data): """ Create a new instance loading the values from JSON-serialised data as a string :param data: The string with the JSON-serialised data to load from """ from aiida.common import json return cls.load_from_dict(json.loads(data))