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']