map

docarray.utils.map

map_docs(docs, func, backend='thread', num_worker=None, pool=None, show_progress=False)

Return an iterator that applies func to every Document in docs in parallel, yielding the results.

---

from docarray import DocList
from docarray.documents import ImageDoc
from docarray.utils.map import map_docs


def load_url_to_tensor(img: ImageDoc) -> ImageDoc:
    img.tensor = img.url.load()
    return img


url = (
    'https://upload.wikimedia.org/wikipedia/commons/8/80/'
    'Dag_Sebastian_Ahlander_at_G%C3%B6teborg_Book_Fair_2012b.jpg'
)

docs = DocList[ImageDoc]([ImageDoc(url=url) for _ in range(100)])
docs = DocList[ImageDoc](
    list(map_docs(docs, load_url_to_tensor, backend='thread'))
)  # threading is usually a good option for IO-bound tasks such as loading an
# ImageDoc from url

for doc in docs:
    assert doc.tensor is not None

---

Parameters:

Name	Type	Description	Default
docs	T	DocList to apply function to	required
func	Callable[[T_doc], T_doc]	a function that takes a BaseDoc as input and outputs a BaseDoc.	required
backend	str	thread for multithreading and process for multiprocessing. Defaults to thread. In general, if func is IO-bound then thread is a good choice. On the other hand, if func is CPU-bound, then you may use process. In practice, you should try yourselves to figure out the best value. However, if you wish to modify the elements in-place, regardless of IO/CPU-bound, you should always use thread backend. Note that computation that is offloaded to non-python code (e.g. through np/torch/tf) falls under the "IO-bound" category. !!! warning When using process backend, your func should not modify elements in-place. This is because the multiprocessing backend passes the variable via pickle and works in another process. The passed object and the original object do not share the same memory.	'thread'
num_worker	Optional[int]	the number of parallel workers. If not given, the number of CPUs in the system will be used.	None
pool	Optional[Union[Pool, ThreadPool]]	use an existing/external process or thread pool. If given, you will be responsible for closing the pool.	None
show_progress	bool	show a progress bar. Defaults to False.	False

Returns:

Type	Description
Generator[T_doc, None, None]	yield Documents returned from func

Source code in docarray/utils/map.py

def map_docs(
    docs: T,
    func: Callable[[T_doc], T_doc],
    backend: str = 'thread',
    num_worker: Optional[int] = None,
    pool: Optional[Union[Pool, ThreadPool]] = None,
    show_progress: bool = False,
) -> Generator[T_doc, None, None]:
    """
    Return an iterator that applies `func` to every Document in `docs` in parallel,
    yielding the results.

    ---

    ```python
    from docarray import DocList
    from docarray.documents import ImageDoc
    from docarray.utils.map import map_docs


    def load_url_to_tensor(img: ImageDoc) -> ImageDoc:
        img.tensor = img.url.load()
        return img


    url = (
        'https://upload.wikimedia.org/wikipedia/commons/8/80/'
        'Dag_Sebastian_Ahlander_at_G%C3%B6teborg_Book_Fair_2012b.jpg'
    )

    docs = DocList[ImageDoc]([ImageDoc(url=url) for _ in range(100)])
    docs = DocList[ImageDoc](
        list(map_docs(docs, load_url_to_tensor, backend='thread'))
    )  # threading is usually a good option for IO-bound tasks such as loading an
    # ImageDoc from url

    for doc in docs:
        assert doc.tensor is not None
    ```

    ---

    :param docs: DocList to apply function to
    :param func: a function that takes a [`BaseDoc`][docarray.base_doc.doc.BaseDoc]
        as input and outputs a [`BaseDoc`][docarray.base_doc.doc.BaseDoc].
    :param backend: `thread` for multithreading and `process` for multiprocessing.
        Defaults to `thread`.
        In general, if `func` is IO-bound then `thread` is a good choice.
        On the other hand, if `func` is CPU-bound, then you may use `process`.
        In practice, you should try yourselves to figure out the best value.
        However, if you wish to modify the elements in-place, regardless of IO/CPU-bound,
        you should always use `thread` backend.
        Note that computation that is offloaded to non-python code (e.g. through np/torch/tf)
        falls under the "IO-bound" category.

        !!! warning
            When using `process` backend, your `func` should not modify elements in-place.
            This is because the multiprocessing backend passes the variable via pickle
            and works in another process.
            The passed object and the original object do **not** share the same memory.

    :param num_worker: the number of parallel workers. If not given, the number of CPUs
        in the system will be used.
    :param pool: use an existing/external process or thread pool. If given, you will
        be responsible for closing the pool.
    :param show_progress: show a progress bar. Defaults to False.

    :return: yield Documents returned from `func`
    """

    if backend == 'process' and _is_lambda_or_partial_or_local_function(func):
        raise ValueError(
            f'Multiprocessing does not allow functions that are local, lambda or partial: {func}'
        )

    context_pool: Union[nullcontext, Union[Pool, ThreadPool]]
    if pool:
        p = pool
        context_pool = nullcontext()
    else:
        p = _get_pool(backend, num_worker)
        context_pool = p

    with context_pool:
        imap = p.imap(func, docs)
        for x in track(imap, total=len(docs), disable=not show_progress):
            yield x

map_docs_batched(docs, func, batch_size, backend='thread', num_worker=None, shuffle=False, pool=None, show_progress=False)

Return an iterator that applies func to every minibatch of iterable in parallel, yielding the results. Each element in the returned iterator is an AnyDocArray.

---

from docarray import BaseDoc, DocList
from docarray.utils.map import map_docs_batched


class MyDoc(BaseDoc):
    name: str


def upper_case_name(docs: DocList[MyDoc]) -> DocList[MyDoc]:
    docs.name = [n.upper() for n in docs.name]
    return docs


batch_size = 16
docs = DocList[MyDoc]([MyDoc(name='my orange cat') for _ in range(100)])
it = map_docs_batched(docs, upper_case_name, batch_size=batch_size)
for i, d in enumerate(it):
    docs[i * batch_size : (i + 1) * batch_size] = d

assert len(docs) == 100
print(docs.name[:3])

---

['MY ORANGE CAT', 'MY ORANGE CAT', 'MY ORANGE CAT']

---

Parameters:

Name	Type	Description	Default
docs	T	DocList to apply function to	required
batch_size	int	Size of each generated batch (except the last one, which might be smaller).	required
shuffle	bool	If set, shuffle the Documents before dividing into minibatches.	False
func	Callable[[T], Union[T, T_doc]]	a function that takes an :class:AnyDocArray as input and outputs an :class:AnyDocArray or a :class:BaseDoc.	required
backend	str	thread for multithreading and process for multiprocessing. Defaults to thread. In general, if func is IO-bound then thread is a good choice. On the other hand, if func is CPU-bound, then you may use process. In practice, you should try yourselves to figure out the best value. However, if you wish to modify the elements in-place, regardless of IO/CPU-bound, you should always use thread backend. Note that computation that is offloaded to non-python code (e.g. through np/torch/tf) falls under the "IO-bound" category. !!! warning When using process backend, your func should not modify elements in-place. This is because the multiprocessing backend passes the variable via pickle and works in another process. The passed object and the original object do not share the same memory.	'thread'
num_worker	Optional[int]	the number of parallel workers. If not given, then the number of CPUs in the system will be used.	None
show_progress	bool	show a progress bar	False
pool	Optional[Union[Pool, ThreadPool]]	use an existing/external pool. If given, backend is ignored and you will be responsible for closing the pool.	None

Returns:

Type	Description
Generator[Union[T, T_doc], None, None]	yield DocLists returned from func

Source code in docarray/utils/map.py

def map_docs_batched(
    docs: T,
    func: Callable[[T], Union[T, T_doc]],
    batch_size: int,
    backend: str = 'thread',
    num_worker: Optional[int] = None,
    shuffle: bool = False,
    pool: Optional[Union[Pool, ThreadPool]] = None,
    show_progress: bool = False,
) -> Generator[Union[T, T_doc], None, None]:
    """
    Return an iterator that applies `func` to every **minibatch** of iterable in parallel,
    yielding the results.
    Each element in the returned iterator is an `AnyDocArray`.

    ---

    ```python
    from docarray import BaseDoc, DocList
    from docarray.utils.map import map_docs_batched


    class MyDoc(BaseDoc):
        name: str


    def upper_case_name(docs: DocList[MyDoc]) -> DocList[MyDoc]:
        docs.name = [n.upper() for n in docs.name]
        return docs


    batch_size = 16
    docs = DocList[MyDoc]([MyDoc(name='my orange cat') for _ in range(100)])
    it = map_docs_batched(docs, upper_case_name, batch_size=batch_size)
    for i, d in enumerate(it):
        docs[i * batch_size : (i + 1) * batch_size] = d

    assert len(docs) == 100
    print(docs.name[:3])
    ```

    ---

    ```
    ['MY ORANGE CAT', 'MY ORANGE CAT', 'MY ORANGE CAT']
    ```

    ---

    :param docs: DocList to apply function to
    :param batch_size: Size of each generated batch (except the last one, which might
        be smaller).
    :param shuffle: If set, shuffle the Documents before dividing into minibatches.
    :param func: a function that takes an :class:`AnyDocArray` as input and outputs
        an :class:`AnyDocArray` or a :class:`BaseDoc`.
    :param backend: `thread` for multithreading and `process` for multiprocessing.
        Defaults to `thread`.
        In general, if `func` is IO-bound then `thread` is a good choice.
        On the other hand, if `func` is CPU-bound, then you may use `process`.
        In practice, you should try yourselves to figure out the best value.
        However, if you wish to modify the elements in-place, regardless of IO/CPU-bound,
        you should always use `thread` backend.
        Note that computation that is offloaded to non-python code (e.g. through np/torch/tf)
        falls under the "IO-bound" category.

        !!! warning
            When using `process` backend, your `func` should not modify elements in-place.
            This is because the multiprocessing backend passes the variable via pickle
            and works in another process.
            The passed object and the original object do **not** share the same memory.

    :param num_worker: the number of parallel workers. If not given, then the number of CPUs
        in the system will be used.
    :param show_progress: show a progress bar
    :param pool: use an existing/external pool. If given, `backend` is ignored and you will
        be responsible for closing the pool.

    :return: yield DocLists returned from `func`
    """
    if backend == 'process' and _is_lambda_or_partial_or_local_function(func):
        raise ValueError(
            f'Multiprocessing does not allow functions that are local, lambda or partial: {func}'
        )

    context_pool: Union[nullcontext, Union[Pool, ThreadPool]]
    if pool:
        p = pool
        context_pool = nullcontext()
    else:
        p = _get_pool(backend, num_worker)
        context_pool = p

    with context_pool:
        imap = p.imap(func, docs._batch(batch_size=batch_size, shuffle=shuffle))
        for x in track(
            imap, total=ceil(len(docs) / batch_size), disable=not show_progress
        ):
            yield x