aiida.engine.processes.calcjobs package

Module for the CalcJob process and related utilities.

class aiida.engine.processes.calcjobs.CalcJob(*args, **kwargs)[source]

Bases: aiida.engine.processes.process.Process

Implementation of the CalcJob process.

_Process__called = True
__abstractmethods__ = frozenset()
__init__(*args, **kwargs)[source]

Construct a CalcJob instance.

Construct the instance only if it is a sub class of CalcJob, otherwise raise InvalidOperation.

See documentation of aiida.engine.Process.

__module__ = 'aiida.engine.processes.calcjobs.calcjob'
_abc_impl = <_abc_data object>
_node_class

alias of aiida.orm.nodes.process.calculation.calcjob.CalcJobNode

_spec = <aiida.engine.processes.process_spec.CalcJobProcessSpec object>
_spec_class

alias of aiida.engine.processes.process_spec.CalcJobProcessSpec

classmethod define(spec)[source]
classmethod get_state_classes()[source]
on_terminated()[source]

Cleanup the node by deleting the calulation job state.

Note

This has to be done before calling the super because that will seal the node after we cannot change it

options

Return the options of the metadata that were specified when this process instance was launched.

Returns:options dictionary
Return type:dict
parse(retrieved_temporary_folder=None)[source]

Parse a retrieved job calculation.

This is called once it’s finished waiting for the calculation to be finished and the data has been retrieved.

prepare_for_submission(folder)[source]

Prepare files for submission of calculation.

presubmit(folder)[source]

Prepares the calculation folder with all inputs, ready to be copied to the cluster.

Parameters:folder (aiida.common.folders.Folder) – a SandboxFolder, empty in input, that will be filled with calculation input files and the scheduling script.
Return calcinfo:
 the CalcInfo object containing the information needed by the daemon to handle operations.
Rtype calcinfo:aiida.common.CalcInfo
run()[source]

Run the calculation job.

This means invoking the presubmit and storing the temporary folder in the node’s repository. Then we move the process in the Wait state, waiting for the UPLOAD transport task to be started.

spec_options = <aiida.engine.processes.ports.PortNamespace object>

Submodules

Implementation of the CalcJob process.

class aiida.engine.processes.calcjobs.calcjob.CalcJob(*args, **kwargs)[source]

Bases: aiida.engine.processes.process.Process

Implementation of the CalcJob process.

_Process__called = True
__abstractmethods__ = frozenset()
__init__(*args, **kwargs)[source]

Construct a CalcJob instance.

Construct the instance only if it is a sub class of CalcJob, otherwise raise InvalidOperation.

See documentation of aiida.engine.Process.

__module__ = 'aiida.engine.processes.calcjobs.calcjob'
_abc_impl = <_abc_data object>
_node_class

alias of aiida.orm.nodes.process.calculation.calcjob.CalcJobNode

_spec = <aiida.engine.processes.process_spec.CalcJobProcessSpec object>
_spec_class

alias of aiida.engine.processes.process_spec.CalcJobProcessSpec

classmethod define(spec)[source]
classmethod get_state_classes()[source]
on_terminated()[source]

Cleanup the node by deleting the calulation job state.

Note

This has to be done before calling the super because that will seal the node after we cannot change it

options

Return the options of the metadata that were specified when this process instance was launched.

Returns:options dictionary
Return type:dict
parse(retrieved_temporary_folder=None)[source]

Parse a retrieved job calculation.

This is called once it’s finished waiting for the calculation to be finished and the data has been retrieved.

prepare_for_submission(folder)[source]

Prepare files for submission of calculation.

presubmit(folder)[source]

Prepares the calculation folder with all inputs, ready to be copied to the cluster.

Parameters:folder (aiida.common.folders.Folder) – a SandboxFolder, empty in input, that will be filled with calculation input files and the scheduling script.
Return calcinfo:
 the CalcInfo object containing the information needed by the daemon to handle operations.
Rtype calcinfo:aiida.common.CalcInfo
run()[source]

Run the calculation job.

This means invoking the presubmit and storing the temporary folder in the node’s repository. Then we move the process in the Wait state, waiting for the UPLOAD transport task to be started.

spec_options = <aiida.engine.processes.ports.PortNamespace object>

Module containing utilities and classes relating to job calculations running on systems that require transport.

class aiida.engine.processes.calcjobs.manager.JobsList(authinfo, transport_queue, last_updated=None)[source]

Bases: object

Manager of calculation jobs submitted with a specific AuthInfo, i.e. computer configured for a specific user.

This container of active calculation jobs is used to update their status periodically in batches, ensuring that even when a lot of jobs are running, the scheduler update command is not triggered for each job individually.

In addition, the Computer for which the AuthInfo is configured, can define a minimum polling interval. This class will guarantee that the time between update calls to the scheduler is larger or equal to that minimum interval.

Note that since each instance operates on a specific authinfo, the guarantees of batching scheduler update calls and the limiting of number of calls per unit time, through the minimum polling interval, is only applicable for jobs launched with that particular authinfo. If multiple authinfo instances with the same computer, have active jobs these limitations are not respected between them, since there is no communication between JobsList instances. See the JobManager for example usage.

__dict__ = mappingproxy({'__module__': 'aiida.engine.processes.calcjobs.manager', '__doc__': 'Manager of calculation jobs submitted with a specific ``AuthInfo``, i.e. computer configured for a specific user.\n\n This container of active calculation jobs is used to update their status periodically in batches, ensuring that\n even when a lot of jobs are running, the scheduler update command is not triggered for each job individually.\n\n In addition, the :py:class:`~aiida.orm.computers.Computer` for which the :py:class:`~aiida.orm.authinfos.AuthInfo`\n is configured, can define a minimum polling interval. This class will guarantee that the time between update calls\n to the scheduler is larger or equal to that minimum interval.\n\n Note that since each instance operates on a specific authinfo, the guarantees of batching scheduler update calls\n and the limiting of number of calls per unit time, through the minimum polling interval, is only applicable for jobs\n launched with that particular authinfo. If multiple authinfo instances with the same computer, have active jobs\n these limitations are not respected between them, since there is no communication between ``JobsList`` instances.\n See the :py:class:`~aiida.engine.processes.calcjobs.manager.JobManager` for example usage.\n ', '__init__': <function JobsList.__init__>, 'logger': <property object>, 'get_minimum_update_interval': <function JobsList.get_minimum_update_interval>, 'last_updated': <property object>, '_get_jobs_from_scheduler': <function JobsList._get_jobs_from_scheduler>, '_update_job_info': <function JobsList._update_job_info>, 'request_job_info_update': <function JobsList.request_job_info_update>, '_ensure_updating': <function JobsList._ensure_updating>, '_has_job_state_changed': <staticmethod object>, '_get_next_update_delay': <function JobsList._get_next_update_delay>, '_update_requests_outstanding': <function JobsList._update_requests_outstanding>, '_get_jobs_with_scheduler': <function JobsList._get_jobs_with_scheduler>, '__dict__': <attribute '__dict__' of 'JobsList' objects>, '__weakref__': <attribute '__weakref__' of 'JobsList' objects>})
__init__(authinfo, transport_queue, last_updated=None)[source]

Construct an instance for the given authinfo and transport queue.

Parameters:
  • authinfo (aiida.orm.AuthInfo) – The authinfo used to check the jobs list
  • transport_queue – A transport queue
  • last_updated – initialize the last updated timestamp
Type:

aiida.engine.transports.TransportQueue

Type:

float

__module__ = 'aiida.engine.processes.calcjobs.manager'
__weakref__

list of weak references to the object (if defined)

_ensure_updating()[source]

Ensure that we are updating the job list from the remote resource.

This will automatically stop if there are no outstanding requests.

_get_jobs_from_scheduler()[source]

Get the current jobs list from the scheduler.

Returns:a mapping of job ids to JobInfo instances
Return type:dict
_get_jobs_with_scheduler()[source]

Get all the jobs that are currently with scheduler.

Returns:the list of jobs with the scheduler
Return type:list
_get_next_update_delay()[source]

Calculate when we are next allowed to poll the scheduler.

This delay is calculated as the minimum polling interval defined by the authentication info for this instance, minus time elapsed since the last update.

Returns:delay (in seconds) after which the scheduler may be polled again
Return type:float
static _has_job_state_changed(old, new)[source]

Return whether the states old and new are different.

Return type:bool
_update_job_info()[source]

Update all of the job information objects.

This will set the futures for all pending update requests where the corresponding job has a new status compared to the last update.

_update_requests_outstanding()[source]
get_minimum_update_interval()[source]

Get the minimum interval that should be respected between updates of the list.

Returns:the minimum interval
Return type:float
last_updated

Get the timestamp of when the list was last updated as produced by time.time()

Returns:The last update point
Return type:float
logger

Return the logger configured for this instance.

Returns:the logger
request_job_info_update(job_id)[source]

Request job info about a job when the job next changes state.

If the job is not found in the jobs list at the update, the future will resolve to None.

Parameters:job_id – job identifier
Returns:future that will resolve to a JobInfo object when the job changes state
class aiida.engine.processes.calcjobs.manager.JobManager(transport_queue)[source]

Bases: object

A manager for CalcJob submitted to Computer instances.

When a calculation job is submitted to a Computer, it actually uses a specific AuthInfo, which is a computer configured for a User. The JobManager maintains a mapping of JobsList instances for each authinfo that has active calculation jobs. These jobslist instances are then responsible for bundling scheduler updates for all the jobs they maintain (i.e. that all share the same authinfo) and update their status.

As long as a Runner will create a single JobManager instance and use that for its lifetime, the guarantees made by the JobsList about respecting the minimum polling interval of the scheduler will be maintained. Note, however, that since each Runner will create its own job manager, these guarantees only hold per runner.

__dict__ = mappingproxy({'__module__': 'aiida.engine.processes.calcjobs.manager', '__doc__': 'A manager for :py:class:`~aiida.engine.processes.calcjobs.calcjob.CalcJob` submitted to ``Computer`` instances.\n\n When a calculation job is submitted to a :py:class:`~aiida.orm.computers.Computer`, it actually uses a specific\n :py:class:`~aiida.orm.authinfos.AuthInfo`, which is a computer configured for a :py:class:`~aiida.orm.users.User`.\n The ``JobManager`` maintains a mapping of :py:class:`~aiida.engine.processes.calcjobs.manager.JobsList` instances\n for each authinfo that has active calculation jobs. These jobslist instances are then responsible for bundling\n scheduler updates for all the jobs they maintain (i.e. that all share the same authinfo) and update their status.\n\n As long as a :py:class:`~aiida.engine.runners.Runner` will create a single ``JobManager`` instance and use that for\n its lifetime, the guarantees made by the ``JobsList`` about respecting the minimum polling interval of the scheduler\n will be maintained. Note, however, that since each ``Runner`` will create its own job manager, these guarantees\n only hold per runner.\n ', '__init__': <function JobManager.__init__>, 'get_jobs_list': <function JobManager.get_jobs_list>, 'request_job_info_update': <function JobManager.request_job_info_update>, '__dict__': <attribute '__dict__' of 'JobManager' objects>, '__weakref__': <attribute '__weakref__' of 'JobManager' objects>})
__init__(transport_queue)[source]

Initialize self. See help(type(self)) for accurate signature.

__module__ = 'aiida.engine.processes.calcjobs.manager'
__weakref__

list of weak references to the object (if defined)

get_jobs_list(authinfo)[source]

Get or create a new JobLists instance for the given authinfo.

Parameters:authinfo – the AuthInfo
Returns:a JobsList instance
request_job_info_update(authinfo, job_id)[source]

Get a future that will resolve to information about a given job.

This is a context manager so that if the user leaves the context the request is automatically cancelled.

Returns:A tuple containing the JobInfo object and detailed job info. Both can be None.
Return type:tornado.concurrent.Future
class aiida.engine.processes.calcjobs.tasks.Waiting(process, done_callback, msg=None, data=None)[source]

Bases: plumpy.process_states.Waiting

The waiting state for the CalcJob process.

__init__(process, done_callback, msg=None, data=None)[source]
Parameters:state_machine (StateMachine) – The process this state belongs to
__module__ = 'aiida.engine.processes.calcjobs.tasks'
_launch_task(coro, *args, **kwargs)[source]
execute()[source]

Execute the state, performing the actions that this state is responsible for. Return a state to transition to or None if finished.

interrupt(reason)[source]

Interrupt the Waiting state by calling interrupt on the transport task InterruptableFuture.

load_instance_state(saved_state, load_context)[source]
parse(retrieved_temporary_folder)[source]

Return the Running state that will parse the CalcJob.

Parameters:retrieved_temporary_folder – temporary folder used in retrieving that can be used during parsing.
retrieve()[source]

Return the Waiting state that will retrieve the CalcJob.

submit(calc_info, script_filename)[source]

Return the Waiting state that will submit the CalcJob.

update()[source]

Return the Waiting state that will update the CalcJob.

upload(calc_info, script_filename)[source]

Return the Waiting state that will upload the CalcJob.

aiida.engine.processes.calcjobs.tasks.task_kill_job(node, transport_queue, cancellable)[source]

Transport task that will attempt to kill a job calculation

The task will first request a transport from the queue. Once the transport is yielded, the relevant execmanager function is called, wrapped in the exponential_backoff_retry coroutine, which, in case of a caught exception, will retry after an interval that increases exponentially with the number of retries, for a maximum number of retries. If all retries fail, the task will raise a TransportTaskException

Parameters:
  • node – the node that represents the job calculation
  • transport_queue – the TransportQueue from which to request a Transport
  • cancellable (aiida.engine.utils.InterruptableFuture) – the cancelled flag that will be queried to determine whether the task was cancelled
Raises:

Return if the tasks was successfully completed

Raises:

TransportTaskException if after the maximum number of retries the transport task still excepted

aiida.engine.processes.calcjobs.tasks.task_retrieve_job(node, transport_queue, retrieved_temporary_folder, cancellable)[source]

Transport task that will attempt to retrieve all files of a completed job calculation

The task will first request a transport from the queue. Once the transport is yielded, the relevant execmanager function is called, wrapped in the exponential_backoff_retry coroutine, which, in case of a caught exception, will retry after an interval that increases exponentially with the number of retries, for a maximum number of retries. If all retries fail, the task will raise a TransportTaskException

Parameters:
  • node – the node that represents the job calculation
  • transport_queue – the TransportQueue from which to request a Transport
  • cancellable (aiida.engine.utils.InterruptableFuture) – the cancelled flag that will be queried to determine whether the task was cancelled
Raises:

Return if the tasks was successfully completed

Raises:

TransportTaskException if after the maximum number of retries the transport task still excepted

aiida.engine.processes.calcjobs.tasks.task_submit_job(node, transport_queue, calc_info, script_filename, cancellable)[source]

Transport task that will attempt to submit a job calculation

The task will first request a transport from the queue. Once the transport is yielded, the relevant execmanager function is called, wrapped in the exponential_backoff_retry coroutine, which, in case of a caught exception, will retry after an interval that increases exponentially with the number of retries, for a maximum number of retries. If all retries fail, the task will raise a TransportTaskException

Parameters:
  • node – the node that represents the job calculation
  • transport_queue – the TransportQueue from which to request a Transport
  • calc_info – the calculation info datastructure returned by CalcJobNode._presubmit
  • script_filename – the job launch script returned by CalcJobNode._presubmit
  • cancellable (aiida.engine.utils.InterruptableFuture) – the cancelled flag that will be queried to determine whether the task was cancelled
Raises:

Return if the tasks was successfully completed

Raises:

TransportTaskException if after the maximum number of retries the transport task still excepted

aiida.engine.processes.calcjobs.tasks.task_update_job(node, job_manager, cancellable)[source]

Transport task that will attempt to update the scheduler status of the job calculation

The task will first request a transport from the queue. Once the transport is yielded, the relevant execmanager function is called, wrapped in the exponential_backoff_retry coroutine, which, in case of a caught exception, will retry after an interval that increases exponentially with the number of retries, for a maximum number of retries. If all retries fail, the task will raise a TransportTaskException

Parameters:
Raises:

Return containing True if the tasks was successfully completed, False otherwise

aiida.engine.processes.calcjobs.tasks.task_upload_job(node, transport_queue, calc_info, script_filename, cancellable)[source]

Transport task that will attempt to upload the files of a job calculation to the remote

The task will first request a transport from the queue. Once the transport is yielded, the relevant execmanager function is called, wrapped in the exponential_backoff_retry coroutine, which, in case of a caught exception, will retry after an interval that increases exponentially with the number of retries, for a maximum number of retries. If all retries fail, the task will raise a TransportTaskException

Parameters:
  • node – the node that represents the job calculation
  • transport_queue – the TransportQueue from which to request a Transport
  • calc_info – the calculation info datastructure returned by CalcJobNode._presubmit
  • script_filename – the job launch script returned by CalcJobNode._presubmit
  • cancellable (aiida.engine.utils.InterruptableFuture) – the cancelled flag that will be queried to determine whether the task was cancelled
Raises:

Return if the tasks was successfully completed

Raises:

TransportTaskException if after the maximum number of retries the transport task still excepted