Dask jobqueue

amltk.scheduling.executors.dask_jobqueue

Dask Jobqueue Executors.

These are essentially wrappers around the dask_jobqueue classes. We use them to provide a consistent interface for all the different jobqueue implementations and get access to their executors.

Documentation from dask_jobqueue

See the dask jobqueue documentation specifically:

• Example deployments
• Tips and Tricks
• Debugging

DaskJobqueueExecutor

DaskJobqueueExecutor(
    cluster: _JQC,
    *,
    n_workers: int,
    adaptive: bool = False,
    submit_command: str | None = None,
    cancel_command: str | None = None
)

Bases: Executor, Generic[_JQC]

A concurrent.futures Executor that executes tasks on a dask_jobqueue cluster.

Implementations

Prefer to use the class methods to create an instance of this class.

• DaskJobqueueExecutor.SLURM()
• DaskJobqueueExecutor.HTCondor()
• DaskJobqueueExecutor.LSF()
• DaskJobqueueExecutor.OAR()
• DaskJobqueueExecutor.PBS()
• DaskJobqueueExecutor.SGE()
• DaskJobqueueExecutor.Moab()

PARAMETER	DESCRIPTION
cluster	The implementation of a dask_jobqueue.JobQueueCluster. TYPE: _JQC
n_workers	The number of workers to maximally adapt to on the cluster. TYPE: int
adaptive	Whether to use the adaptive scaling of the cluster or fixed allocate all workers. This will specifically use the dask_jobqueue.SLURMCluster.adapt method to dynamically scale the cluster to the number of workers specified. TYPE: bool DEFAULT: False
submit_command	To overwrite the submission command if necessary. TYPE: str | None DEFAULT: None
cancel_command	To overwrite the cancel command if necessary. TYPE: str | None DEFAULT: None

Source code in src/amltk/scheduling/executors/dask_jobqueue.py

def __init__(
    self,
    cluster: _JQC,
    *,
    n_workers: int,
    adaptive: bool = False,
    submit_command: str | None = None,
    cancel_command: str | None = None,
):
    """Initialize a DaskJobqueueExecutor.


    !!! note "Implementations"

        Prefer to use the class methods to create an instance of this class.

        * [`DaskJobqueueExecutor.SLURM()`][amltk.scheduling.executors.dask_jobqueue.DaskJobqueueExecutor.SLURM]
        * [`DaskJobqueueExecutor.HTCondor()`][amltk.scheduling.executors.dask_jobqueue.DaskJobqueueExecutor.HTCondor]
        * [`DaskJobqueueExecutor.LSF()`][amltk.scheduling.executors.dask_jobqueue.DaskJobqueueExecutor.LSF]
        * [`DaskJobqueueExecutor.OAR()`][amltk.scheduling.executors.dask_jobqueue.DaskJobqueueExecutor.OAR]
        * [`DaskJobqueueExecutor.PBS()`][amltk.scheduling.executors.dask_jobqueue.DaskJobqueueExecutor.PBS]
        * [`DaskJobqueueExecutor.SGE()`][amltk.scheduling.executors.dask_jobqueue.DaskJobqueueExecutor.SGE]
        * [`DaskJobqueueExecutor.Moab()`][amltk.scheduling.executors.dask_jobqueue.DaskJobqueueExecutor.Moab]

    Args:
        cluster: The implementation of a
            [dask_jobqueue.JobQueueCluster](https://jobqueue.dask.org/en/latest/api.html).
        n_workers: The number of workers to maximally adapt to on the cluster.
        adaptive: Whether to use the adaptive scaling of the cluster or fixed allocate all workers.
            This will specifically use the
            [dask_jobqueue.SLURMCluster.adapt](https://jobqueue.dask.org/en/latest/index.html?highlight=adapt#adaptivity)
            method to dynamically scale the cluster to the number of workers specified.
        submit_command: To overwrite the submission command if necessary.
        cancel_command: To overwrite the cancel command if necessary.
    """
    super().__init__()
    self.cluster = cluster
    self.adaptive = adaptive
    if submit_command:
        self.cluster.job_cls.submit_command = submit_command  # type: ignore

    if cancel_command:
        self.cluster.job_cls.cancel_command = cancel_command  # type: ignore

    if adaptive:
        self.cluster.adapt(minimum=0, maximum=n_workers)
    else:
        self.cluster.scale(n_workers)

    self.n_workers = n_workers
    self.executor: ClientExecutor = self.cluster.get_client().get_executor()

HTCondor classmethod

HTCondor(
    *,
    n_workers: int,
    adaptive: bool = False,
    submit_command: str | None = None,
    cancel_command: str | None = None,
    **kwargs: Any
) -> DaskJobqueueExecutor[HTCondorCluster]

Create a DaskJobqueueExecutor for a HTCondor cluster.

See the dask_jobqueue.HTCondorCluster documentation for more information on the available keyword arguments.

Source code in src/amltk/scheduling/executors/dask_jobqueue.py

@classmethod
def HTCondor(
    cls,
    *,
    n_workers: int,
    adaptive: bool = False,
    submit_command: str | None = None,
    cancel_command: str | None = None,
    **kwargs: Any,
) -> DaskJobqueueExecutor[HTCondorCluster]:
    """Create a DaskJobqueueExecutor for a HTCondor cluster.

    See the [dask_jobqueue.HTCondorCluster documentation][dask_jobqueue.HTCondorCluster] for
    more information on the available keyword arguments.
    """
    return cls(  # type: ignore
        HTCondorCluster(**kwargs),
        submit_command=submit_command,
        cancel_command=cancel_command,
        adaptive=adaptive,
        n_workers=n_workers,
    )

LSF classmethod

LSF(
    *,
    n_workers: int,
    adaptive: bool = False,
    submit_command: str | None = None,
    cancel_command: str | None = None,
    **kwargs: Any
) -> DaskJobqueueExecutor[LSFCluster]

Create a DaskJobqueueExecutor for a LSF cluster.

See the dask_jobqueue.LSFCluster documentation for more information on the available keyword arguments.

Source code in src/amltk/scheduling/executors/dask_jobqueue.py

@classmethod
def LSF(
    cls,
    *,
    n_workers: int,
    adaptive: bool = False,
    submit_command: str | None = None,
    cancel_command: str | None = None,
    **kwargs: Any,
) -> DaskJobqueueExecutor[LSFCluster]:
    """Create a DaskJobqueueExecutor for a LSF cluster.

    See the [dask_jobqueue.LSFCluster documentation][dask_jobqueue.LSFCluster] for
    more information on the available keyword arguments.
    """
    return cls(  # type: ignore
        LSFCluster(**kwargs),
        submit_command=submit_command,
        cancel_command=cancel_command,
        adaptive=adaptive,
        n_workers=n_workers,
    )

Moab classmethod

Moab(
    *,
    n_workers: int,
    adaptive: bool = False,
    submit_command: str | None = None,
    cancel_command: str | None = None,
    **kwargs: Any
) -> DaskJobqueueExecutor[MoabCluster]

Create a DaskJobqueueExecutor for a Moab cluster.

See the dask_jobqueue.MoabCluster documentation for more information on the available keyword arguments.

Source code in src/amltk/scheduling/executors/dask_jobqueue.py

@classmethod
def Moab(
    cls,
    *,
    n_workers: int,
    adaptive: bool = False,
    submit_command: str | None = None,
    cancel_command: str | None = None,
    **kwargs: Any,
) -> DaskJobqueueExecutor[MoabCluster]:
    """Create a DaskJobqueueExecutor for a Moab cluster.

    See the [dask_jobqueue.MoabCluster documentation][dask_jobqueue.MoabCluster] for
    more information on the available keyword arguments.
    """
    return cls(  # type: ignore
        MoabCluster(**kwargs),
        submit_command=submit_command,
        cancel_command=cancel_command,
        adaptive=adaptive,
        n_workers=n_workers,
    )

OAR classmethod

OAR(
    *,
    n_workers: int,
    adaptive: bool = False,
    submit_command: str | None = None,
    cancel_command: str | None = None,
    **kwargs: Any
) -> DaskJobqueueExecutor[OARCluster]

Create a DaskJobqueueExecutor for a OAR cluster.

See the dask_jobqueue.OARCluster documentation for more information on the available keyword arguments.

Source code in src/amltk/scheduling/executors/dask_jobqueue.py

@classmethod
def OAR(
    cls,
    *,
    n_workers: int,
    adaptive: bool = False,
    submit_command: str | None = None,
    cancel_command: str | None = None,
    **kwargs: Any,
) -> DaskJobqueueExecutor[OARCluster]:
    """Create a DaskJobqueueExecutor for a OAR cluster.

    See the [dask_jobqueue.OARCluster documentation][dask_jobqueue.OARCluster] for
    more information on the available keyword arguments.
    """
    return cls(  # type: ignore
        OARCluster(**kwargs),
        submit_command=submit_command,
        cancel_command=cancel_command,
        adaptive=adaptive,
        n_workers=n_workers,
    )

PBS classmethod

PBS(
    *,
    n_workers: int,
    adaptive: bool = False,
    submit_command: str | None = None,
    cancel_command: str | None = None,
    **kwargs: Any
) -> DaskJobqueueExecutor[PBSCluster]

Create a DaskJobqueueExecutor for a PBS cluster.

See the dask_jobqueue.PBSCluster documentation for more information on the available keyword arguments.

Source code in src/amltk/scheduling/executors/dask_jobqueue.py

@classmethod
def PBS(
    cls,
    *,
    n_workers: int,
    adaptive: bool = False,
    submit_command: str | None = None,
    cancel_command: str | None = None,
    **kwargs: Any,
) -> DaskJobqueueExecutor[PBSCluster]:
    """Create a DaskJobqueueExecutor for a PBS cluster.

    See the [dask_jobqueue.PBSCluster documentation][dask_jobqueue.PBSCluster] for
    more information on the available keyword arguments.
    """
    return cls(  # type: ignore
        PBSCluster(**kwargs),
        submit_command=submit_command,
        cancel_command=cancel_command,
        adaptive=adaptive,
        n_workers=n_workers,
    )

SGE classmethod

SGE(
    *,
    n_workers: int,
    adaptive: bool = False,
    submit_command: str | None = None,
    cancel_command: str | None = None,
    **kwargs: Any
) -> DaskJobqueueExecutor[SGECluster]

Create a DaskJobqueueExecutor for a SGE cluster.

See the dask_jobqueue.SGECluster documentation for more information on the available keyword arguments.

Source code in src/amltk/scheduling/executors/dask_jobqueue.py

@classmethod
def SGE(
    cls,
    *,
    n_workers: int,
    adaptive: bool = False,
    submit_command: str | None = None,
    cancel_command: str | None = None,
    **kwargs: Any,
) -> DaskJobqueueExecutor[SGECluster]:
    """Create a DaskJobqueueExecutor for a SGE cluster.

    See the [dask_jobqueue.SGECluster documentation][dask_jobqueue.SGECluster] for
    more information on the available keyword arguments.
    """
    return cls(  # type: ignore
        SGECluster(**kwargs),
        submit_command=submit_command,
        cancel_command=cancel_command,
        adaptive=adaptive,
        n_workers=n_workers,
    )

SLURM classmethod

SLURM(
    *,
    n_workers: int,
    adaptive: bool = False,
    submit_command: str | None = None,
    cancel_command: str | None = None,
    **kwargs: Any
) -> DaskJobqueueExecutor[SLURMCluster]

Create a DaskJobqueueExecutor for a SLURM cluster.

See the dask_jobqueue.SLURMCluster documentation for more information on the available keyword arguments.

Source code in src/amltk/scheduling/executors/dask_jobqueue.py

@classmethod
def SLURM(
    cls,
    *,
    n_workers: int,
    adaptive: bool = False,
    submit_command: str | None = None,
    cancel_command: str | None = None,
    **kwargs: Any,
) -> DaskJobqueueExecutor[SLURMCluster]:
    """Create a DaskJobqueueExecutor for a SLURM cluster.

    See the [dask_jobqueue.SLURMCluster documentation][dask_jobqueue.SLURMCluster] for
    more information on the available keyword arguments.
    """
    return cls(  # type: ignore
        SLURMCluster(**kwargs),
        submit_command=submit_command,
        cancel_command=cancel_command,
        n_workers=n_workers,
        adaptive=adaptive,
    )

from_str classmethod

from_str(
    name: DJQ_NAMES,
    *,
    n_workers: int,
    adaptive: bool = False,
    submit_command: str | None = None,
    cancel_command: str | None = None,
    **kwargs: Any
) -> DaskJobqueueExecutor

Create a DaskJobqueueExecutor using a string lookup.

PARAMETER	DESCRIPTION
name	The name of cluster to create, must be one of ["slurm", "htcondor", "lsf", "oar", "pbs", "sge", "moab"]. TYPE: DJQ_NAMES
n_workers	The number of workers to maximally adapt to on the cluster. TYPE: int
adaptive	Whether to use the adaptive scaling of the cluster or fixed allocate all workers. This will specifically use the dask_jobqueue.SLURMCluster.adapt method to dynamically scale the cluster to the number of workers specified. TYPE: bool DEFAULT: False
submit_command	Overwrite the submit command of workers if necessary. TYPE: str | None DEFAULT: None
cancel_command	Overwrite the cancel command of workers if necessary. TYPE: str | None DEFAULT: None
kwargs	The keyword arguments to pass to the cluster constructor. TYPE: Any DEFAULT: {}

RAISES	DESCRIPTION
KeyError	If name is not one of the supported cluster types.

RETURNS	DESCRIPTION
DaskJobqueueExecutor	A DaskJobqueueExecutor for the requested cluster type.

Source code in src/amltk/scheduling/executors/dask_jobqueue.py

@classmethod
def from_str(
    cls,
    name: DJQ_NAMES,
    *,
    n_workers: int,
    adaptive: bool = False,
    submit_command: str | None = None,
    cancel_command: str | None = None,
    **kwargs: Any,
) -> DaskJobqueueExecutor:
    """Create a DaskJobqueueExecutor using a string lookup.

    Args:
        name: The name of cluster to create, must be one of
            ["slurm", "htcondor", "lsf", "oar", "pbs", "sge", "moab"].
        n_workers: The number of workers to maximally adapt to on the cluster.
        adaptive: Whether to use the adaptive scaling of the cluster or fixed allocate all workers.
            This will specifically use the
            [dask_jobqueue.SLURMCluster.adapt](https://jobqueue.dask.org/en/latest/index.html?highlight=adapt#adaptivity)
            method to dynamically scale the cluster to the number of workers specified.
        submit_command: Overwrite the submit command of workers if necessary.
        cancel_command: Overwrite the cancel command of workers if necessary.
        kwargs: The keyword arguments to pass to the cluster constructor.

    Raises:
        KeyError: If `name` is not one of the supported cluster types.

    Returns:
        A DaskJobqueueExecutor for the requested cluster type.
    """
    methods = {
        "slurm": cls.SLURM,
        "htcondor": cls.HTCondor,
        "lsf": cls.LSF,
        "oar": cls.OAR,
        "pbs": cls.PBS,
        "sge": cls.SGE,
        "moab": cls.Moab,
    }
    method = methods.get(name.lower())
    if method is None:
        raise KeyError(
            f"Unknown cluster name: {name}, must be from {list(methods)}",
        )

    return method(
        n_workers=n_workers,
        submit_command=submit_command,
        cancel_command=cancel_command,
        adaptive=adaptive,
        **kwargs,
    )

map

map(
    fn: Callable[..., R],
    *iterables: Iterable,
    timeout: float | None = None,
    chunksize: int = 1
) -> Iterator[R]

See concurrent.futures.Executor.map.

Source code in src/amltk/scheduling/executors/dask_jobqueue.py

@override
def map(
    self,
    fn: Callable[..., R],
    *iterables: Iterable,
    timeout: float | None = None,
    chunksize: int = 1,
) -> Iterator[R]:
    """See [concurrent.futures.Executor.map][]."""
    return self.executor.map(  # type: ignore
        fn,
        *iterables,
        timeout=timeout,
        chunksize=chunksize,
    )

shutdown

shutdown(wait: bool = True, **kwargs: Any) -> None

See concurrent.futures.Executor.shutdown.

Source code in src/amltk/scheduling/executors/dask_jobqueue.py

@override
def shutdown(
    self,
    wait: bool = True,
    **kwargs: Any,
) -> None:
    """See [concurrent.futures.Executor.shutdown][]."""
    self.executor.shutdown(wait=wait, **kwargs)

submit

submit(
    fn: Callable[P, R], /, *args: args, **kwargs: kwargs
) -> Future[R]

See concurrent.futures.Executor.submit.

Source code in src/amltk/scheduling/executors/dask_jobqueue.py

@override
def submit(
    self,
    fn: Callable[P, R],
    /,
    *args: P.args,
    **kwargs: P.kwargs,
) -> Future[R]:
    """See [concurrent.futures.Executor.submit][]."""
    future = self.executor.submit(fn, *args, **kwargs)
    assert isinstance(future, Future)
    return future