Some formatting & grammar fixes for the multiprocessing doc

This commit is contained in:
Eli Bendersky 2011-12-31 07:22:27 +02:00
commit ca6b7a1a73
1 changed files with 14 additions and 14 deletions

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@ -491,7 +491,7 @@ into Python 2.5's :class:`queue.Queue` class.
If you use :class:`JoinableQueue` then you **must** call
:meth:`JoinableQueue.task_done` for each task removed from the queue or else the
semaphore used to count the number of unfinished tasks may eventually overflow
semaphore used to count the number of unfinished tasks may eventually overflow,
raising an exception.
Note that one can also create a shared queue by using a manager object -- see
@ -509,7 +509,7 @@ Note that one can also create a shared queue by using a manager object -- see
If a process is killed using :meth:`Process.terminate` or :func:`os.kill`
while it is trying to use a :class:`Queue`, then the data in the queue is
likely to become corrupted. This may cause any other processes to get an
likely to become corrupted. This may cause any other process to get an
exception when it tries to use the queue later on.
.. warning::
@ -711,7 +711,7 @@ Miscellaneous
(By default :data:`sys.executable` is used). Embedders will probably need to
do some thing like ::
setExecutable(os.path.join(sys.exec_prefix, 'pythonw.exe'))
set_executable(os.path.join(sys.exec_prefix, 'pythonw.exe'))
before they can create child processes. (Windows only)
@ -730,7 +730,7 @@ Connection Objects
Connection objects allow the sending and receiving of picklable objects or
strings. They can be thought of as message oriented connected sockets.
Connection objects usually created using :func:`Pipe` -- see also
Connection objects are usually created using :func:`Pipe` -- see also
:ref:`multiprocessing-listeners-clients`.
.. class:: Connection
@ -751,7 +751,7 @@ Connection objects usually created using :func:`Pipe` -- see also
.. method:: fileno()
Returns the file descriptor or handle used by the connection.
Return the file descriptor or handle used by the connection.
.. method:: close()
@ -775,7 +775,7 @@ Connection objects usually created using :func:`Pipe` -- see also
If *offset* is given then data is read from that position in *buffer*. If
*size* is given then that many bytes will be read from buffer. Very large
buffers (approximately 32 MB+, though it depends on the OS) may raise a
ValueError exception
:exc:`ValueError` exception
.. method:: recv_bytes([maxlength])
@ -1353,7 +1353,7 @@ Customized managers
>>>>>>>>>>>>>>>>>>>
To create one's own manager, one creates a subclass of :class:`BaseManager` and
use the :meth:`~BaseManager.register` classmethod to register new types or
uses the :meth:`~BaseManager.register` classmethod to register new types or
callables with the manager class. For example::
from multiprocessing.managers import BaseManager
@ -1604,9 +1604,9 @@ with the :class:`Pool` class.
.. method:: apply(func[, args[, kwds]])
Call *func* with arguments *args* and keyword arguments *kwds*. It blocks
till the result is ready. Given this blocks, :meth:`apply_async` is better
suited for performing work in parallel. Additionally, the passed in
function is only executed in one of the workers of the pool.
until the result is ready. Given this blocks, :meth:`apply_async` is
better suited for performing work in parallel. Additionally, *func*
is only executed in one of the workers of the pool.
.. method:: apply_async(func[, args[, kwds[, callback[, error_callback]]]])
@ -1627,7 +1627,7 @@ with the :class:`Pool` class.
.. method:: map(func, iterable[, chunksize])
A parallel equivalent of the :func:`map` built-in function (it supports only
one *iterable* argument though). It blocks till the result is ready.
one *iterable* argument though). It blocks until the result is ready.
This method chops the iterable into a number of chunks which it submits to
the process pool as separate tasks. The (approximate) size of these
@ -2102,7 +2102,7 @@ Better to inherit than pickle/unpickle
On Windows many types from :mod:`multiprocessing` need to be picklable so
that child processes can use them. However, one should generally avoid
sending shared objects to other processes using pipes or queues. Instead
you should arrange the program so that a process which need access to a
you should arrange the program so that a process which needs access to a
shared resource created elsewhere can inherit it from an ancestor process.
Avoid terminating processes
@ -2181,7 +2181,7 @@ Explicitly pass resources to child processes
for i in range(10):
Process(target=f, args=(lock,)).start()
Beware replacing sys.stdin with a "file like object"
Beware of replacing :data:`sys.stdin` with a "file like object"
:mod:`multiprocessing` originally unconditionally called::
@ -2299,7 +2299,7 @@ Synchronization types like locks, conditions and queues:
An example showing how to use queues to feed tasks to a collection of worker
process and collect the results:
processes and collect the results:
.. literalinclude:: ../includes/mp_workers.py