schrodinger.tasks.queue module

class schrodinger.tasks.queue.TaskQueue(*args, _param_type=<object object>, **kwargs)

Bases: schrodinger.tasks.tasks.SignalTask

A task that runs a queue of tasks. The TaskQueue is done when all its added tasks have completed, regardless of whether they completed successfully or failed. To use, add tasks with addTask and then start the task queue.

queuedTaskFinished
queueDone
max_running_tasks: int

Base class for all Param classes. A Param is a descriptor for storing data, which means that a single Param instance will manage the data values for multiple instances of the class that owns it. Example:

class Coord(CompoundParam):
    x: int
    y: int

An instance of the Coord class can be created normally, and Params can be accessed as normal attributes:

coord = Coord()
coord.x = 4

When a Param value is set, the valueChanged signal is emitted. Params can be serialized and deserialized to and from JSON. Params can also be nested:

class Atom(CompoundParam):
    coord: Coord
    element: str
__init__(*args, **kwargs)
addTask(task)
getTasks()

Return all tasks in the queue.

Return type

tuple[tasks.AbstractTask]

setUpMain()
AUTO_TASKDIR = 1
CMDLINE = 1
DEFAULT_TASKDIR_SETTING = None
DONE = 3
DataClass

This class can be used to declare a public attribute on a CompoundParam. Declared public attributes can be used without error.

Example usage:

class Coord(CompoundParam):
    x: int
    y: int
    note = NonParamAttribute()

coord = Coord()
coord.note = "hello" # No error
FAILED = 2
GUI = 2
INTERRUPT_ENABLED = False
RUNNING = 1
TEMP_TASKDIR = 2
WAITING = 0
addFuncToGroup(func, group=None, order=None)

Adds a function to the specified chain. Typically used for adding functions that are not methods of this object.

The function may optionally be decorated with a FuncGroupMarker. If so, the default group and order will be determined by the decorator. Any group or order explicitly passed in to addFuncToGroup will take precedence over the decorator settings.

Parameters
  • func – the function to add

  • group (FuncGroupMarker or None) – the group marker. If the function is decorated with a FuncGoupMarker, that group marker will be the default.

  • order (float or None) – the sorting order. If the function is decorated with a FuncGoupMarker, the order specified in the decorator will be the default.

addPostprocessor(func, order=0)

Adds a postproceessor function to this task instance. If the function has been decorated with @postprocessor, the order specified by the decorator will be used.

Parameters
  • func (typing.Callable) – the function to add

  • order (float) – the sorting order for the function relative to all other preprocessors. Takes precedence over order specified by the preprocessor decorator.

addPreprocessor(func, order=None)

Adds a preproceessor function to this task instance. If the function has been decorated with @preprocessor, the order specified by the decorator will be used as the default.

Parameters
  • func – the function to add

  • order (float) – the sorting order for the function relative to all other preprocessors. Takes precedence over order specified by the preprocessor decorator.

classmethod addSubParam(name, param, update_owner=True)
blockSignals(self, b: bool) bool
block_signal_propagation()
calling_context: CallingContext
calling_contextChanged
calling_contextReplaced
childEvent(self, a0: QChildEvent)
children(self) List[QObject]
classmethod configureParam()

Override this class method to set up the abstract param class (e.g. setParamReference on child params.)

connectNotify(self, signal: QMetaMethod)
customEvent(self, a0: QEvent)
classmethod defaultValue()

Returns the default value for this abstract param:

default_atom = Atom.defaultValue()
assert Atom.coord.x == 0
deleteLater(self)
destroyed

destroyed(self, object: typing.Optional[QObject] = None) [signal]

disconnect(a0: QMetaObject.Connection) bool
disconnect(self) None
disconnectNotify(self, signal: QMetaMethod)
dumpObjectInfo(self)
dumpObjectTree(self)
dynamicPropertyNames(self) List[QByteArray]
event(self, a0: QEvent) bool
eventFilter(self, a0: QObject, a1: QEvent) bool
failure_info

This class can be used to declare a public attribute on a CompoundParam. Declared public attributes can be used without error.

Example usage:

class Coord(CompoundParam):
    x: int
    y: int
    note = NonParamAttribute()

coord = Coord()
coord.note = "hello" # No error
findChild(self, type: type, name: str = '', options: Qt.FindChildOption = Qt.FindChildrenRecursively) QObject
findChild(self, types: Tuple, name: str = '', options: Qt.FindChildOption = Qt.FindChildrenRecursively) QObject
findChildren(self, type: type, name: str = '', options: Qt.FindChildOption = Qt.FindChildrenRecursively) List[QObject]
findChildren(self, types: Tuple, name: str = '', options: Qt.FindChildOption = Qt.FindChildrenRecursively) List[QObject]
findChildren(self, type: type, re: QRegularExpression, options: Qt.FindChildOption = Qt.FindChildrenRecursively) List[QObject]
findChildren(self, types: Tuple, re: QRegularExpression, options: Qt.FindChildOption = Qt.FindChildrenRecursively) List[QObject]
classmethod fromJson(json_obj)

A factory method which constructs a new object from a given dict loaded from a json string or file.

Parameters

json_obj (dict) – A json-loaded dictionary to create an object from.

Returns

An instance of this class.

Return type

cls

classmethod fromJsonFilename(filename)
classmethod fromJsonImplementation(json_dict)

Sets the value of this compound param value object from a JSON dict.

Warning

This should never be called directly.

getAbstractParam()

Return the corresponding abstract param for this instance.

getAddedFuncs(group=None)
getDebugString()
getFuncGroup(group=None)

Retrieve the functions belonging to the specified group.

Parameters

group (FuncGroupMarker) – the group marker

Returns

the functions in the specified group, in order

Return type

list

classmethod getJsonBlacklist()

Override to customize what params are serialized.

Implementations should return a list of abstract params that should be omitted from serialization.

..NOTE

Returned abstract params must be direct child params of cls, e.g. cls.name, not cls.coord.x.

classmethod getParamSignal(obj, signal_type='Changed')
classmethod getParamValue(obj)

Enables access to a param value on a compound param via an abstract param reference:

a = Atom()
assert Atom.coord.x.getParamValue(a) == 0 # ints default to 0
a.coord.x = 3
assert Atom.coord.x.getParamValue(a) == 3
Parameters

param (CompoundParam) – The owner param to get a param value from

classmethod getSubParam(name)

Get the value of a subparam using the string name:

c = Coord()
assert c.getSubParam('x') == 0

Note

Using the string name to access params is generally discouraged, but can be useful for serializing/deserializing param data.

Parameters

name (str) – The name of the subparam to get the value for.

classmethod getSubParams()

Return a dictionary mapping subparam names to their values.

getTaskDir()

Returns the full path of the task directory. This is only available if the task directory exists (after creation of the taskdir or, if no task dir is specified, any time).

getTaskFilename(fname)

Return the appropriate absolute path for an input or output file in the taskdir.

getTypeHint()
get_version()

Method to get the version of a particular object. Defaults to the current version of mmshare. This class can be overridden for custom versioning behavior.

guard()

Context manager that saves any Exception raised inside

static guard_method(func)
inherits(self, classname: str) bool
initAbstract()
initConcrete()

Override to customize initialization of concrete params.

initializeValue()

@overrides: parameters.CompoundParam

input: parameters.CompoundParam

All CompoundParam instances are automatically serializable if their subparams are serializable. To serialize and deserialize, use the schrodinger json module:

from schrodinger.models import json
class Coord(parameters.CompoundParam):
    x: int
    y: int

c1 = Coord(x=1, y=2)
c1_string = json.dumps(c1)
c2 = json.loads(c1_string, DataClass=Coord)
assert c1 == c2
inputChanged
inputReplaced
installEventFilter(self, a0: QObject)
classmethod isAbstract()

Whether the param is an “abstract” param.

isDebugEnabled()
isDefault()

Whether the current value of this instance matches the default value.

isInterruptionRequested()
isRunning()
isSignalConnected(self, signal: QMetaMethod) bool
isStartable()
isWidgetType(self) bool
isWindowType(self) bool
kill()

Implementations are responsible for immediately stopping the task. No threads or processes should be running after this method is complete.

This method should be called sparingly since in many contexts the task will be forced to terminate without a chance to clean up or free resources.

killTimer(self, id: int)
mainDone
max_progress: int

Base class for all Param classes. A Param is a descriptor for storing data, which means that a single Param instance will manage the data values for multiple instances of the class that owns it. Example:

class Coord(CompoundParam):
    x: int
    y: int

An instance of the Coord class can be created normally, and Params can be accessed as normal attributes:

coord = Coord()
coord.x = 4

When a Param value is set, the valueChanged signal is emitted. Params can be serialized and deserialized to and from JSON. Params can also be nested:

class Atom(CompoundParam):
    coord: Coord
    element: str
max_progressChanged
max_progressReplaced
max_running_tasksChanged
max_running_tasksReplaced
metaObject(self) QMetaObject
moveToThread(self, thread: QThread)
name: str

Base class for all Param classes. A Param is a descriptor for storing data, which means that a single Param instance will manage the data values for multiple instances of the class that owns it. Example:

class Coord(CompoundParam):
    x: int
    y: int

An instance of the Coord class can be created normally, and Params can be accessed as normal attributes:

coord = Coord()
coord.x = 4

When a Param value is set, the valueChanged signal is emitted. Params can be serialized and deserialized to and from JSON. Params can also be nested:

class Atom(CompoundParam):
    coord: Coord
    element: str
nameChanged
nameReplaced
objectName(self) str
objectNameChanged

objectNameChanged(self, objectName: str) [signal]

output: parameters.CompoundParam

All CompoundParam instances are automatically serializable if their subparams are serializable. To serialize and deserialize, use the schrodinger json module:

from schrodinger.models import json
class Coord(parameters.CompoundParam):
    x: int
    y: int

c1 = Coord(x=1, y=2)
c1_string = json.dumps(c1)
c2 = json.loads(c1_string, DataClass=Coord)
assert c1 == c2
outputChanged
outputReplaced
classmethod owner()

Get the owner of the param:

# Can be called on an abstract param:
assert Coord.x.owner() == Coord

# ...or on an instance of a CompoundParam
a = Atom()
assert a.coord.owner() == a
classmethod ownerChain()

Returns a list of param owners starting from the toplevel param and ending with self. Examples:

foo.bar.atom.coord.ownerChain() will return [foo, bar, atom, coord] where every item is a concrete param.

Foo.bar.atom.coord.x.ownerChain() will return [Foo, Foo.bar, Foo.atom.coord, Foo.atom.coord.x] where every item is an abstract params.

classmethod paramName()

Get the name of the param:

# Can be called on an abstract param:
print(Coord.x.paramName()) # 'x'

# ...or on an instance of a CompoundParam
a = Atom()
a.coord.paramName() # 'coord'
parent(self) QObject
postprocessors()
Returns

A list of postprocessors, both decorated methods on the task and external functions that have been added via addPostprocessor()

Return type

list[typing.Callable]

preprocessors()
Returns

A list of preprocessors (both decorated methods on the task and external functions that have been added via addPreprocessor)

printDebug(*args)
processFuncChain(chain=None, result_callback=None)

Execute each function in the specified chain sequentially in order.

The result_callback is called after each function with the return value of that function. This can be used to respond to the return value (e.g. present information to the user, get user feedback, log the result, etc.)

The return value of the result_callback determines whether processing will proceeed to the next function.

Parameters
  • chain (FuncChainDecorator) – which chain to process

  • result_callback – the callback that will get called with the result of each function in the chain

Returns

a list of the results from the functions

progress: int

Base class for all Param classes. A Param is a descriptor for storing data, which means that a single Param instance will manage the data values for multiple instances of the class that owns it. Example:

class Coord(CompoundParam):
    x: int
    y: int

An instance of the Coord class can be created normally, and Params can be accessed as normal attributes:

coord = Coord()
coord.x = 4

When a Param value is set, the valueChanged signal is emitted. Params can be serialized and deserialized to and from JSON. Params can also be nested:

class Atom(CompoundParam):
    coord: Coord
    element: str
progressChanged
progressReplaced
progress_string: str

Base class for all Param classes. A Param is a descriptor for storing data, which means that a single Param instance will manage the data values for multiple instances of the class that owns it. Example:

class Coord(CompoundParam):
    x: int
    y: int

An instance of the Coord class can be created normally, and Params can be accessed as normal attributes:

coord = Coord()
coord.x = 4

When a Param value is set, the valueChanged signal is emitted. Params can be serialized and deserialized to and from JSON. Params can also be nested:

class Atom(CompoundParam):
    coord: Coord
    element: str
progress_stringChanged
progress_stringReplaced
property(self, name: str) Any
pyqtConfigure(...)

Each keyword argument is either the name of a Qt property or a Qt signal. For properties the property is set to the given value which should be of an appropriate type. For signals the signal is connected to the given value which should be a callable.

receivers(self, signal: PYQT_SIGNAL) int
removeEventFilter(self, a0: QObject)
replicate()

Create a new task with the same input and settings (but no output)

requestInterruption()

Request the task to stop.

To enable this feature, subclasses should periodically check whether an interruption has been requested and terminate if it has been. If such logic has been included, INTERRUPT_ENABLED should be set to True.

reset(*args, **kwargs)

Resets this compound param to its default value:

class Line(CompoundParam):
    start = Coord(x=1, y=2)
    end = Coord(x=4, y=5)
line = Line()
line.start.x = line.end.x = 10
assert line.start.x == line.end.x == 10
line.reset()
assert line.start.x == 1
assert line.end.x == 4

Any number of abstract params may be passed in to perform a partial reset of only the specified params:

line.start.x = line.end.x = 10
line.reset(Line.start.x)  # resets just start.x
assert line.start.x == 1
assert line.end.x == 10

line.reset(Line.end)      # resets the entire end point
assert line.end.x == 4

line.start.y = line.end.y = 10
line.reset(Line.start.y, Line.end.y)  # resets the y-coord of both
assert line.start.y == 2
assert line.end.y == 5
run()
classmethod runFromCmdLine()
runPreprocessing(callback=None, calling_context=None)

Run the preprocessors one-by-one. By default, any failing preprocessor will raise a TaskFailure exception and terminate processing. This behavior may be customized by supplying a callback function which will be called after each preprocessor with the result of that preprocessor.

This method is “final” so that all preprocessing logic will be enclosed in the try/finally block.

Parameters
  • callback – a function that takes result and returns a bool that indicates whether to continue on to the next preprocessor

  • calling_context – specify a value here to indicate the context in which this preprocessing is being called. This value will be stored in an instance variable, self.calling_context, which can be accessed from any preprocessor method on this task. Typically this value will be either self.GUI, self.CMDLINE, or None, but any value may be supplied here and checked for in the preprocessor methods. self.calling_context always reverts back to None at the end of runPreprocessing.

sender(self) QObject
senderSignalIndex(self) int
setObjectName(self, name: str)
classmethod setParamValue(obj, value)

Set the value of a param on an object by specifying the instance and the value:

# Setting the param value of a basic param
a = Atom()
Atom.coord.x.setParamValue(a, 5)
assert a.coord.x == 5

# setParamValue can also be used to set the value of CompoundParams
c = Coord()
c.x = 10
atom.coord.setParamValue(a, c)
assert atom.coord.x == 10
Parameters
  • param – The owner param to set a subparam value of.

  • value – The value to set the subparam value to.

setParent(self, a0: QObject)
setProperty(self, name: str, value: Any) bool
classmethod setReference(param1, param2)

Call this class method from configureParam to indicate that two params should be kept in sync. The initial values will start with the default value of param1. Example:

class Square(CompoundParam):
    width: float = 5
    height: float = 10

    @classmethod
    def configureParam(cls):
        super().configureParam()
        cls.setReference(cls.width, cls.height)

square = Square()
assert square.width == square.height == 5 # Default value of width
                                          # takes priority
square.height = 7
assert square.width == square.height == 7
square.width = 6
assert square.width == square.height == 6
Parameters
  • param1 – The first abstract param to keep synced

  • param2 – The second abstract param. After instantiation, this param will take on the value of param1.

setValue(value=None, **kwargs)

Set the value of this CompoundParam to match value.

Parameters
  • value – The value to set this CompoundParam to. It should be the same type as this CompoundParam.

  • kwargs – For internal use only.

signalsBlocked(self) bool
skip_eq_check()
specifyTaskDir(taskdir_spec)

Specify the taskdir creation behavior. Use one of the following options:

A directory name (string). This may be a relative or absolute path

None - no taskdir is requested. The task will use the CWD as its taskdir

AUTO_TASKDIR - a new subdirectory will be created in the CWD using the task name as the directory name.

TEMP_TASKDIR - a temporary directory will be created in the schrodinger temp dir. This directory is cleaned up when the task is deleted.

Parameters

taskdir_spec – one of the four options listed above

start(skip_preprocessing=False)

This is the main method for starting a task. Start will check if a task is not already running, run preprocessing, and then run the task.

Failures in preprocessing will interrupt the task start, and the task will never enter the RUNNING state.

Parameters

skip_preprocessing (bool) – whether to skip preprocessing. This can be useful if preprocessing was already performed prior to calling start.

startTimer(self, interval: int, timerType: Qt.TimerType = Qt.CoarseTimer) int
staticMetaObject = <PyQt6.QtCore.QMetaObject object>
status: Status
statusChanged
statusReplaced
taskDirSetting()

Returns the taskdir spec. See specifyTaskDir() for details.

taskDone
taskFailed
taskStarted
thread(self) QThread
timerEvent(self, a0: QTimerEvent)
toDict()

Return a dictionary version of this CompoundParam. The returned dictionary is fully nested and contains no CompoundParam instances

a = Atom()
a_dict = a.toDict()
assert a_dict['coord']['x'] == 0
assert a_dict['coord'] == {'x':0, 'y':0}
toJson(_mark_version=True)

Create and returns a data structure made up of jsonable items.

Return type

An instance of one the classes from NATIVE_JSON_DATATYPES

toJsonImplementation()

Returns a JSON representation of this value object.

Warning

This should never be called directly.

tr(sourceText: str, disambiguation: typing.Optional[str] = None, n: int = - 1) str
valueChanged
wait(timeout=None)

Block until the task is finished executing or timeout seconds have passed.

Warning

This should not be called directly from GUI code - see PANEL-18317. It is safe to call inside a subprocess or job. Run git grep "task.wait(" to see safe examples annotated with “# OK”.

Parameters

timeout (NoneType or int) – Amount of time in seconds to wait before timing out. If None or a negative number, this method will wait until the task is finished.

class schrodinger.tasks.queue.TaskDJ(*args, task_timeout=None, **kwargs)

Bases: schrodinger.job.queue.JobDJ

WARNING: This class is slated for removal. To run tasks in parallel, consider using the run_tasks_in_parallel below, or else use JobDJ directly by using task.runToCmd and task.setJob

A subclass of JobDJ that supports running tasks.

NOTE: In order for remote jobs to be distributed properly, ensure that task.job_config.host_settings.host is set to None, otherwise TaskDJ will not add a -HOST option.

__init__(*args, task_timeout=None, **kwargs)

Constructor.

Parameters
  • hosts – A list of hosts to run on. Items of the list can be a (<hostname>, <maximum_concurrent_subjobs>) tuple that gives a host name and the maximum number of processors that can be used on that host. Or, items of the list can be a ResourceHost object that provides a detailed listing of the number of processors on each host that can be used for jobs requesting specific resources. The list can mix tuple and ResourceHost items. If a host appears more than once (whether as a tuple or ResourceHost), the information in the multiple entries is added together. Processors in the tuple form are only considered usable by jobs that have resource_requirement=None (The default state for jobs). The default value of None for this parameter means the host information is determined automatically from the command line and is usually desired.

  • local – No longer functional in JOB_SERVER.

  • max_retries – Number of allowed retries per subjob. If this is set, it is never overridden by the SCHRODINGER_MAX_RETRIES environment variable. If it is not set, the value in default_max_retries is used, and SCHRODINGER_MAX_RETRIES is allowed to override. If you wish to disable restarting altogether, set this value to zero.

  • default_max_retries – Number of allowed retries per subjob. This value can always be overridden by the SCHRODINGER_MAX_RETRIES environment variable. Default is zero.

  • max_failures – Total number of allowed subjob failures before JobDJ exits. If it is not defined, a default of zero will be used (exit on any failure after attempting to restart), but this can be overridden with the SCHRODINGER_MAX_FAILURES environment variable. To allow an unlimited number of subjob failures, set max_failures to the module level NOLIMIT constant.

  • verbosity – There are three allowed verbosity levels: “quiet” - only warnings and errors are printed; “normal” - JobDJ progress is printed; and “verbose” - additional debugging info is printed. Default is “quiet”.

  • job_class – The class to use as the default job constructor when the addJob argument is not a BaseJob instance.

  • update_delay – The number of seconds to wait between job control database reads for JobControlJob jobs. (This delay is for an individual job, not for any job database read.) Default is None, which causes the module level constant UPDATE_DELAY to be used.

  • smart_dist_resources – Custom resources that the localhost provides for smart distribution. Jobs that require a custom resource that is not included in this list will not be run on the localhost even if smart distribution is turned on. This information is unused if smart distribution is off. Each item of the list is a string that defines a custom resource.

Raises

HostTypeError – if the host argument or one of its items is of the wrong type

addTask(task)
updatedTasks()
property active_jobs: List[schrodinger.job.queue.BaseJob]
addJob(job: Union[schrodinger.job.queue.BaseJob, List], add_connected: bool = True, **kwargs)

Add a job to run. If job is not a BaseJob instance, a BaseJob instance is constructed with job as the first argument. The default BaseJob class for the JobDJ instance can be specified in the constructor for JobDJ.

Additional keyword arguments are passed on to the job constructor.

All job prerequisites and dependencies need to be specified before adding a job to JobDJ.

Parameters

add_connected – If True, for jobs with dependencies only one job per connected group should be added and all connected jobs will be discovered and added automatically. If False, it is the user’s responsibility to make sure that any prerequisites of a job are also added.

property all_jobs: List[schrodinger.job.queue.BaseJob]
static createResourceHost(host_info: Tuple[str, int], resource: Optional[str] = None) schrodinger.job.queue.ResourceHost

Given a tuple of (hostname, procs), return a ResourceHost.

Parameters
  • host_info (tuple) – The hostname and number of processors it has

  • resource (str) – The resource the host provides, if any. If not given, the host will only run jobs that have no resource_requirement.

Return type

ResourceHost

Returns

The ResourceHost created from in the input data

Raises

HostTypeError – If the provided host_info has the wrong format

disableSmartDistribution()

Disable smart distribution of jobs.

Smart distribution allows subjobs to run on the machine that JobDJ is running on when JobDJ itself is running under a queuing system. This is usually desirable since the JobDJ process doesn’t generally consume significant computational resources and you don’t want to leave a queue slot mostly idle.

property done_jobs: List[schrodinger.job.queue.BaseJob]

Successfully completed jobs, sorted into the order they were marked as completed by JobDJ.

dump(filename: pathlib.Path)

Pickle the JobDJ instance to the specified file name.

property failed_jobs: List[schrodinger.job.queue.BaseJob]
getActiveProcCounts() Dict[str, int]

Return a dictionary containing the number of active jobs on each host.

Any resource requirement of the active jobs is ignored.

getActiveProcsOnHost(hostname: str, requirement: Optional[Union[str, schrodinger.job.resource.ComputeType]]) int

Get the current number of processors on the given host that are currently occupied by jobs that request the given requirement

Parameters
  • hostname – The host to check

  • requirement – The job requirement to check

Returns

The number of currently used processors on hostname that provide requirement

getFirstHost()

Get first host from the lists of hosts.

Return type

str

Returns

First host

hasStarted() bool

Returns True if JobDJ has started already

isComplete() bool

Returns True if JobDJ has completed, False otherwise.

killJobs()

Kill all active jobs

markForRestart(job: schrodinger.job.queue.BaseJob, action: str)

Mark a job as dead, but make sure that it gets restarted.

Parameters

action – Describes the reason the job is being restarted.

printStatus(job: Optional[schrodinger.job.queue.BaseJob] = None, action: Optional[str] = None)

Prints the status of JobDJ and the action/status for the job.

If no job is specified, prints the status header.

If no action is specified, the status_string attribute of the job is used.

run(*, status_change_callback: Optional[Callable[[schrodinger.job.queue.BaseJob], None]] = None, periodic_callback: Optional[Callable[[schrodinger.job.queue.BaseJob], None]] = None, callback_interval: int = 300, restart_failed: bool = True)

Call this method to run all jobs that have been added. The method will return control when all jobs have completed.

Parameters
  • status_change_callback – A function to call every time a job status changes. For example, JobState.RUNNING->JobState.DONE. This function takes a single argument of a schrodinger.job.queue.BaseJob object.

  • periodic_callback – A command to call periodically, regardless of whether job status has changed or not. The function will be called without any arguments.

  • callback_interval – The interval at which the periodic interval will be called. This time is only approximately enforced and will depend on the timing delay settings (e.g. MONITOR_DELAY).

  • restart_failed – True (default) if previously failed jobs should be restarted, False if not.

setHostList(host_list: List[Union[Tuple[str, int], schrodinger.job.queue.ResourceHost]])

Define compute hosts to run subjobs on.

Active jobs are not affected by a change in the host list.

Parameters

host_list – A list of hosts to run on. See the documentation for the hosts parameter to the __init__ method of this class for additional information.

Raises

HostTypeError – If one of the host_list items is not of the correct type

setSmartDistribution(state: bool)

Set smart distribution of jobs.

Parameters

state (bool) – Whether to enable smart distribution

property total_active: int

The number of jobs currently running.

property total_added: int

The number of individual jobs that have been added to the JobDJ instance.

property total_failed: int

The number of jobs that have failed.

property total_finished: int

The number of jobs that have finished successfully.

property waiting_jobs: List[schrodinger.job.queue.BaseJob]

Jobs waiting to be started.

class schrodinger.tasks.queue.AutoFileMode(value)

Bases: enum.IntEnum

An enumeration.

FAILED_LOG = 1
FAILED_ALL = 2
NONE = 3
LOG = 4
ALL = 5
schrodinger.tasks.queue.run_tasks_in_parallel(task_list, autoname=True, basename=None)

This functions provides a convenient way of launching multiple tasks in parallel while taking care of boilerplate and prevents common mistakes.

By default, this function will:

  • Give each task a unique name

  • Give each task its own directory

  • Create a new JobDJ or TaskQueue

  • Run it with the provided tasks

  • Register the log files of any failed tasks with jobserver if using

    jobtasks

This can be used with either JobTasks or SubprocessTasks. SubprocessTasks will be run on a TaskQueue while JobTasks will be run on JobDJ.

Parameters
  • task_list (list of tasks.AbstractTask) – the tasks to run

  • autoname (bool) – whether to automatically give each task a unique name

  • basename (str) – basename to be used in autonaming each task. Has no effect if autoname is set to False

Returns

the list of tasks that completed successfully. This return value is useful for downstream processing, so the caller doesn’t need to filter out failed tasks

Return type

list of tasks.AbstractTask

schrodinger.tasks.queue.run_tasks_on_queue(task_list, autoname=True, basename=None, max_running_tasks=None)

Launch multiple SubprocessTasks on a TaskQueue.

See run_tasks_in_parallel for more details. :param max_running_tasks: specifies how many tasks can be run simultaneously :type max_running_tasks: int

schrodinger.tasks.queue.run_tasks_on_dj(task_list, dj=None, autoname=True, basename=None, auto_file_mode=AutoFileMode.FAILED_LOG, dj_addJob_kwargs: Optional[dict] = None, dj_run_kwargs: Optional[dict] = None)

Launch multiple jobtasks on a jobdj. Use this function over run_tasks_in_parallel if you know you’ll be running jobtasks and you want to further customize how they’re run.

See run_tasks_in_parallel for details on more params.

Parameters
  • dj (schrodinger.job.queue.JobDJ) – A JobDJ to run the tasks on. This is optional and useful if want a jobdj with specific settings.

  • auto_file_mode (AutoFileMode) – what subtask files to register with the parent job to be copied back by jobcontrol. Only has an effect if this function is called from inside a jobcontrol backend.

  • dj_run_kwargs – A dictionary of keyword arguments to pass to JobDJ.run

  • dj_addJob_kwargs – A dictionary of keyword arguments to pass to JobDJ.addJob for each jobtask.

schrodinger.tasks.queue.setup_dj_tasks(task_list, dj=None, autoname=True, basename=None, addJob_kwargs: Optional[dict] = None)

Sets up a TaskDJ with a collection of tasks. See run_tasks_on_dj for details

Parameters

addJob_kwargs – A dictionary of keyword arguments to pass to JobDJ.addJob for each jobtask.

schrodinger.tasks.queue.autoname_tasks(task_list, basename=None)

Sets unique names on all the tasks provided with an incrementing integer suffix. See run_tasks_on_dj for details.

schrodinger.tasks.queue.auto_register_files(task_list, auto_file_mode=AutoFileMode.FAILED_LOG)

Automatically registers files from the subtask with the parent job (if applicable).