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decorators

Workflow decorators.

Flow module-attribute 

Flow = TypeVar('Flow')

Job module-attribute 

Job = TypeVar('Job')

Subflow module-attribute 

Subflow = TypeVar('Subflow')

Delayed_ 

Delayed_(func)

A small Dask-compatible, serializable object to wrap delayed functions that we don't want to execute.

Source code in quacc/wflow_tools/decorators.py 
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def __init__(self, func):
    self.func = func

__slots__ class-attribute instance-attribute 

__slots__ = ('func')

func instance-attribute 

func = func

__call__ 

__call__(*args, **kwargs)
Source code in quacc/wflow_tools/decorators.py 
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def __call__(self, *args, **kwargs):
    return self.func(*args, **kwargs)

__reduce__ 

__reduce__()
Source code in quacc/wflow_tools/decorators.py 
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def __reduce__(self):
    return (Delayed_, (self.func,))

flow 

flow(_func: Callable | None = None, **kwargs) -> Flow

Decorator for workflows, which consist of at least one compute job. This is a @flow decorator.

Quacc Covalent Parsl Dask Prefect Redun Jobflow
flow ct.lattice No effect No effect flow task No effect

All @flow-decorated functions are transformed into their corresponding decorator.

from quacc import flow, job


@job
def add(a, b):
    return a + b


@flow
def workflow(a, b, c):
    return add(add(a, b), c)


workflow(1, 2, 3)

... is the same as doing

import covalent as ct


@ct.electron
def add(a, b):
    return a + b


@ct.lattice
def workflow(a, b, c):
    return add(add(a, b), c)


workflow(1, 2, 3)

from dask import delayed


@delayed
def add(a, b):
    return a + b


def workflow(a, b, c):
    return add(add(a, b), c)


workflow(1, 2, 3)

from parsl import python_app


@python_app
def add(a, b):
    return a + b


def workflow(a, b, c):
    return add(add(a, b), c)


workflow(1, 2, 3)

from prefect import flow, task


@task
def add(a, b):
    return a + b


@flow
def workflow(a, b, c):
    return add.submit(add.submit(a, b), c)


workflow(1, 2, 3)

from redun import task


@task
def add(a, b):
    return a + b


@task
def workflow(a, b, c):
    return add(add(a, b), c)


workflow(1, 2, 3)

Warning

This decorator is not meant to be used with Jobflow at this time.

Parameters:

  • _func (Callable | None, default: None ) –

    The function to decorate. This is not meant to be supplied by the user.

  • **kwargs –

    Keyword arguments to pass to the decorator.

Returns:

  • Flow –

    The @flow-decorated function.

Source code in quacc/wflow_tools/decorators.py 
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def flow(_func: Callable | None = None, **kwargs) -> Flow:
    """
    Decorator for workflows, which consist of at least one compute job. This is a
    `#!Python @flow` decorator.

    | Quacc  | Covalent     | Parsl     | Dask      | Prefect | Redun  | Jobflow   |
    | ------ | ------------ | --------- | --------- | ------- | ------ | --------- |
    | `flow` | `ct.lattice` | No effect | No effect | `flow`  | `task` | No effect |

    All `#!Python @flow`-decorated functions are transformed into their corresponding
    decorator.

    ```python
    from quacc import flow, job


    @job
    def add(a, b):
        return a + b


    @flow
    def workflow(a, b, c):
        return add(add(a, b), c)


    workflow(1, 2, 3)
    ```

    ... is the same as doing

    === "Covalent"

        ```python
        import covalent as ct


        @ct.electron
        def add(a, b):
            return a + b


        @ct.lattice
        def workflow(a, b, c):
            return add(add(a, b), c)


        workflow(1, 2, 3)
        ```

    === "Dask"

        ```python
        from dask import delayed


        @delayed
        def add(a, b):
            return a + b


        def workflow(a, b, c):
            return add(add(a, b), c)


        workflow(1, 2, 3)
        ```

    === "Parsl"

        ```python
        from parsl import python_app


        @python_app
        def add(a, b):
            return a + b


        def workflow(a, b, c):
            return add(add(a, b), c)


        workflow(1, 2, 3)
        ```

    === "Prefect"

        ```python
        from prefect import flow, task


        @task
        def add(a, b):
            return a + b


        @flow
        def workflow(a, b, c):
            return add.submit(add.submit(a, b), c)


        workflow(1, 2, 3)
        ```

    === "Redun"

        ```python
        from redun import task


        @task
        def add(a, b):
            return a + b


        @task
        def workflow(a, b, c):
            return add(add(a, b), c)


        workflow(1, 2, 3)
        ```

    === "Jobflow"

        !!! Warning

            This decorator is not meant to be used with Jobflow at this time.

    Parameters
    ----------
    _func
        The function to decorate. This is not meant to be supplied by the user.
    **kwargs
        Keyword arguments to pass to the decorator.

    Returns
    -------
    Flow
        The `#!Python @flow`-decorated function.
    """
    from quacc import SETTINGS

    if _func is None:
        return partial(flow, **kwargs)

    elif SETTINGS.WORKFLOW_ENGINE == "covalent":
        import covalent as ct

        return ct.lattice(_func, **kwargs)
    elif SETTINGS.WORKFLOW_ENGINE == "redun":
        from redun import task

        return task(_func, namespace=_func.__module__, **kwargs)
    elif SETTINGS.WORKFLOW_ENGINE == "prefect":
        from prefect import flow as prefect_flow

        return prefect_flow(_func, validate_parameters=False, **kwargs)
    else:
        return _func

job 

job(_func: Callable | None = None, **kwargs) -> Job

Decorator for individual compute jobs. This is a @job decorator. Think of each @job-decorated function as an individual SLURM job, if that helps.

Quacc Covalent Parsl Dask Prefect Redun Jobflow
job ct.electron python_app delayed task task job

All @job-decorated functions are transformed into their corresponding decorator.

from quacc import job


@job
def add(a, b):
    return a + b


add(1, 2)

... is the same as doing

import covalent as ct


@ct.electron
def add(a, b):
    return a + b


add(1, 2)

from dask import delayed


@delayed
def add(a, b):
    return a + b


add(1, 2)

from parsl import python_app


@python_app
def add(a, b):
    return a + b


add(1, 2)

from prefect import task


@task
def add(a, b):
    return a + b


add.submit(1, 2)

from redun import task


@task
def add(a, b):
    return a + b


add(1, 2)

import jobflow as jf


@jf.job
def add(a, b):
    return a + b


add(1, 2)

Parameters:

  • _func (Callable | None, default: None ) –

    The function to decorate. This is not meant to be supplied by the user.

  • **kwargs –

    Keyword arguments to pass to the workflow engine decorator.

Returns:

  • Job –

    The @job-decorated function.

Source code in quacc/wflow_tools/decorators.py 
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def job(_func: Callable | None = None, **kwargs) -> Job:
    """
    Decorator for individual compute jobs. This is a `#!Python @job` decorator. Think of
    each `#!Python @job`-decorated function as an individual SLURM job, if that helps.

    | Quacc | Covalent      | Parsl        | Dask      | Prefect | Redun  | Jobflow |
    | ----- | ------------- | ------------ | --------- | ------- | ------ | ------- |
    | `job` | `ct.electron` | `python_app` | `delayed` | `task`  | `task` | `job`   |

    All `#!Python @job`-decorated functions are transformed into their corresponding
    decorator.

    ```python
    from quacc import job


    @job
    def add(a, b):
        return a + b


    add(1, 2)
    ```

    ... is the same as doing

    === "Covalent"

        ```python
        import covalent as ct


        @ct.electron
        def add(a, b):
            return a + b


        add(1, 2)
        ```

    === "Dask"

        ```python
        from dask import delayed


        @delayed
        def add(a, b):
            return a + b


        add(1, 2)
        ```

    === "Parsl"

        ```python
        from parsl import python_app


        @python_app
        def add(a, b):
            return a + b


        add(1, 2)
        ```

    === "Prefect"

        ```python
        from prefect import task


        @task
        def add(a, b):
            return a + b


        add.submit(1, 2)
        ```

    === "Redun"

        ```python
        from redun import task


        @task
        def add(a, b):
            return a + b


        add(1, 2)
        ```

    === "Jobflow"

        ```python
        import jobflow as jf


        @jf.job
        def add(a, b):
            return a + b


        add(1, 2)
        ```

    Parameters
    ----------
    _func
        The function to decorate. This is not meant to be supplied by the user.
    **kwargs
        Keyword arguments to pass to the workflow engine decorator.

    Returns
    -------
    Job
        The @job-decorated function.
    """
    from quacc import SETTINGS

    if _func is None:
        return partial(job, **kwargs)

    elif SETTINGS.WORKFLOW_ENGINE == "covalent":
        import covalent as ct

        return ct.electron(_func, **kwargs)
    elif SETTINGS.WORKFLOW_ENGINE == "dask":
        from dask import delayed

        # See https://github.com/dask/dask/issues/10733

        @wraps(_func)
        def wrapper(*f_args, **f_kwargs):
            return _func(*f_args, **f_kwargs)

        return Delayed_(delayed(wrapper, **kwargs))

    elif SETTINGS.WORKFLOW_ENGINE == "jobflow":
        from jobflow import job as jf_job

        return jf_job(_func, **kwargs)
    elif SETTINGS.WORKFLOW_ENGINE == "parsl":
        from parsl import python_app

        wrapped_fn = _get_parsl_wrapped_func(_func, kwargs)

        return python_app(wrapped_fn, **kwargs)
    elif SETTINGS.WORKFLOW_ENGINE == "redun":
        from redun import task

        return task(_func, namespace=_func.__module__, **kwargs)
    elif SETTINGS.WORKFLOW_ENGINE == "prefect":
        from prefect import task

        if SETTINGS.PREFECT_AUTO_SUBMIT:

            @wraps(_func)
            def wrapper(*f_args, **f_kwargs):
                decorated = task(_func, **kwargs)
                return decorated.submit(*f_args, **f_kwargs)

            return wrapper
        else:
            return task(_func, **kwargs)
    else:
        return _func

subflow 

subflow(_func: Callable | None = None, **kwargs) -> Subflow

Decorator for (dynamic) sub-workflows. This is a @subflow decorator.

Quacc Covalent Parsl Dask Prefect Redun Jobflow
subflow ct.electron(ct.lattice) join_app delayed flow task No effect

All @subflow-decorated functions are transformed into their corresponding decorator.

import random
from quacc import flow, job, subflow


@job
def add(a, b):
    return a + b


@job
def make_more(val):
    return [val] * random.randint(2, 5)


@subflow
def add_distributed(vals, c):
    return [add(val, c) for val in vals]


@flow
def workflow(a, b, c):
    result1 = add(a, b)
    result2 = make_more(result1)
    return add_distributed(result2, c)


workflow(1, 2, 3)

... is the same as doing

import random
import covalent as ct


@ct.electron
def add(a, b):
    return a + b


@ct.electron
def make_more(val):
    return [val] * random.randint(2, 5)


@ct.electron
@ct.lattice
def add_distributed(vals, c):
    return [add(val, c) for val in vals]


@ct.lattice
def workflow(a, b, c):
    result1 = add(a, b)
    result2 = make_more(result1)
    return add_distributed(result2, c)


workflow(1, 2, 3)

It's complicated... see the source code.

import random
from parsl import join_app, python_app


@python_app
def add(a, b):
    return a + b


@python_app
def make_more(val):
    return [val] * random.randint(2, 5)


@join_app
def add_distributed(vals, c):
    return [add(val, c) for val in vals]


def workflow(a, b, c):
    result1 = add(a, b)
    result2 = make_more(result1)
    return add_distributed(result2, c)


workflow(1, 2, 3)

import random
from prefect import flow, task


@task
def add(a, b):
    return a + b


@task
def make_more(val):
    return [val] * random.randint(2, 5)


@flow
def add_distributed(vals, c):
    return [add(val, c) for val in vals]


@flow
def workflow(a, b, c):
    result1 = add.submit(a, b)
    result2 = make_more.submit(result1)
    return add_distributed(result2, c)


workflow(1, 2, 3)

import random
from redun import task


@task
def add(a, b):
    return a + b


@task
def make_more(val):
    return [val] * random.randint(2, 5)


@task
def add_distributed(vals, c):
    return [add(val, c) for val in vals]


@task
def workflow(a, b, c):
    result1 = add(a, b)
    result2 = make_more(result1)
    return add_distributed(result2, c)


workflow(1, 2, 3)

Warning

This decorator is not meant to be used with Jobflow at this time.

Parameters:

  • _func (Callable | None, default: None ) –

    The function to decorate. This is not meant to be supplied by the user.

  • **kwargs –

    Keyword arguments to pass to the decorator.

Returns:

Source code in quacc/wflow_tools/decorators.py 
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def subflow(_func: Callable | None = None, **kwargs) -> Subflow:
    """
    Decorator for (dynamic) sub-workflows. This is a `#!Python @subflow` decorator.

    | Quacc     | Covalent                  | Parsl      | Dask      | Prefect | Redun  | Jobflow   |
    | --------- | ------------------------- | ---------- | --------- | ------- |------- | --------- |
    | `subflow` | `ct.electron(ct.lattice)` | `join_app` | `delayed` | `flow`  | `task` | No effect |

    All `#!Python @subflow`-decorated functions are transformed into their corresponding
    decorator.

    ```python
    import random
    from quacc import flow, job, subflow


    @job
    def add(a, b):
        return a + b


    @job
    def make_more(val):
        return [val] * random.randint(2, 5)


    @subflow
    def add_distributed(vals, c):
        return [add(val, c) for val in vals]


    @flow
    def workflow(a, b, c):
        result1 = add(a, b)
        result2 = make_more(result1)
        return add_distributed(result2, c)


    workflow(1, 2, 3)
    ```

    ... is the same as doing

    === "Covalent"

        ```python
        import random
        import covalent as ct


        @ct.electron
        def add(a, b):
            return a + b


        @ct.electron
        def make_more(val):
            return [val] * random.randint(2, 5)


        @ct.electron
        @ct.lattice
        def add_distributed(vals, c):
            return [add(val, c) for val in vals]


        @ct.lattice
        def workflow(a, b, c):
            result1 = add(a, b)
            result2 = make_more(result1)
            return add_distributed(result2, c)


        workflow(1, 2, 3)
        ```

    === "Dask"

        It's complicated... see the source code.

    === "Parsl"

        ```python
        import random
        from parsl import join_app, python_app


        @python_app
        def add(a, b):
            return a + b


        @python_app
        def make_more(val):
            return [val] * random.randint(2, 5)


        @join_app
        def add_distributed(vals, c):
            return [add(val, c) for val in vals]


        def workflow(a, b, c):
            result1 = add(a, b)
            result2 = make_more(result1)
            return add_distributed(result2, c)


        workflow(1, 2, 3)
        ```

    === "Prefect"

        ```python
        import random
        from prefect import flow, task


        @task
        def add(a, b):
            return a + b


        @task
        def make_more(val):
            return [val] * random.randint(2, 5)


        @flow
        def add_distributed(vals, c):
            return [add(val, c) for val in vals]


        @flow
        def workflow(a, b, c):
            result1 = add.submit(a, b)
            result2 = make_more.submit(result1)
            return add_distributed(result2, c)


        workflow(1, 2, 3)
        ```

    === "Redun"

        ```python
        import random
        from redun import task


        @task
        def add(a, b):
            return a + b


        @task
        def make_more(val):
            return [val] * random.randint(2, 5)


        @task
        def add_distributed(vals, c):
            return [add(val, c) for val in vals]


        @task
        def workflow(a, b, c):
            result1 = add(a, b)
            result2 = make_more(result1)
            return add_distributed(result2, c)


        workflow(1, 2, 3)
        ```

    === "Jobflow"

        !!! Warning

            This decorator is not meant to be used with Jobflow at this time.

    Parameters
    ----------
    _func
        The function to decorate. This is not meant to be supplied by the user.
    **kwargs
        Keyword arguments to pass to the decorator.

    Returns
    -------
    callable
        The decorated function.
    """
    from quacc import SETTINGS

    if _func is None:
        return partial(subflow, **kwargs)

    elif SETTINGS.WORKFLOW_ENGINE == "covalent":
        import covalent as ct

        return ct.electron(ct.lattice(_func), **kwargs)
    elif SETTINGS.WORKFLOW_ENGINE == "dask":
        from dask import delayed
        from dask.distributed import worker_client

        # See https://github.com/dask/dask/issues/10733

        @wraps(_func)
        def wrapper(*f_args, **f_kwargs):
            with worker_client() as client:
                futures = client.compute(_func(*f_args, **f_kwargs))
                return client.gather(futures)

        return delayed(wrapper, **kwargs)
    elif SETTINGS.WORKFLOW_ENGINE == "parsl":
        from parsl import join_app

        wrapped_fn = _get_parsl_wrapped_func(_func, kwargs)

        return join_app(wrapped_fn, **kwargs)
    elif SETTINGS.WORKFLOW_ENGINE == "prefect":
        from prefect import flow as prefect_flow

        return prefect_flow(_func, validate_parameters=False, **kwargs)
    elif SETTINGS.WORKFLOW_ENGINE == "redun":
        from redun import task

        return task(_func, namespace=_func.__module__, **kwargs)
    else:
        return _func