.. _parallel_details:

==========================================
Details of Parallel Computing with IPython
==========================================

.. note::

    There are still many sections to fill out in this doc


Caveats
=======

First, some caveats about the detailed workings of parallel computing with 0MQ and IPython.

Non-copying sends and numpy arrays
----------------------------------

When numpy arrays are passed as arguments to apply or via data-movement methods, they are not
copied. This means that you must be careful if you are sending an array that you intend to work
on. PyZMQ does allow you to track when a message has been sent so you can know when it is safe
to edit the buffer, but IPython only allows for this.

It is also important to note that the non-copying receive of a message is *read-only*. That
means that if you intend to work in-place on an array that you have sent or received, you must
copy it. This is true for both numpy arrays sent to engines and numpy arrays retrieved as
results.

The following will fail:

.. sourcecode:: ipython

    In [3]: A = numpy.zeros(2)
    
    In [4]: def setter(a):
       ...:   a[0]=1
       ...:   return a

    In [5]: rc[0].apply_sync(setter, A)
    ---------------------------------------------------------------------------
    RuntimeError                              Traceback (most recent call last)<string> in <module>()
    <ipython-input-12-c3e7afeb3075> in setter(a)
    RuntimeError: array is not writeable

If you do need to edit the array in-place, just remember to copy the array if it's read-only.
The :attr:`ndarray.flags.writeable` flag will tell you if you can write to an array.

.. sourcecode:: ipython

    In [3]: A = numpy.zeros(2)
    
    In [4]: def setter(a):
       ...:     """only copy read-only arrays"""
       ...:     if not a.flags.writeable:
       ...:         a=a.copy()
       ...:     a[0]=1
       ...:     return a

    In [5]: rc[0].apply_sync(setter, A)
    Out[5]: array([ 1.,  0.])
    
    # note that results will also be read-only:
    In [6]: _.flags.writeable
    Out[6]: False

If you want to safely edit an array in-place after *sending* it, you must use the `track=True`
flag. IPython always performs non-copying sends of arrays, which return immediately. You must
instruct IPython track those messages *at send time* in order to know for sure that the send has
completed. AsyncResults have a :attr:`sent` property, and :meth:`wait_on_send` method for
checking and waiting for 0MQ to finish with a buffer.

.. sourcecode:: ipython

    In [5]: A = numpy.random.random((1024,1024))
    
    In [6]: view.track=True
    
    In [7]: ar = view.apply_async(lambda x: 2*x, A)
    
    In [8]: ar.sent
    Out[8]: False
    
    In [9]: ar.wait_on_send() # blocks until sent is True


What is sendable?
-----------------

If IPython doesn't know what to do with an object, it will pickle it. There is a short list of
objects that are not pickled: ``buffers/memoryviews``, ``bytes`` objects, and ``numpy``
arrays. These are handled specially by IPython in order to prevent extra in-memory copies of data. Sending
bytes or numpy arrays will result in exactly zero in-memory copies of your data (unless the data
is very small).

If you have an object that provides a Python buffer interface, then you can always send that
buffer without copying - and reconstruct the object on the other side in your own code. It is
possible that the object reconstruction will become extensible, so you can add your own
non-copying types, but this does not yet exist.

Closures
********

Just about anything in Python is pickleable. The one notable exception is objects (generally
functions) with *closures*. Closures can be a complicated topic, but the basic principle is that
functions that refer to variables in their parent scope have closures.

An example of a function that uses a closure:

.. sourcecode:: python

    def f(a):
        def inner():
            # inner will have a closure
            return a
        return inner
    
    f1 = f(1)
    f2 = f(2)
    f1() # returns 1
    f2() # returns 2

``f1`` and ``f2`` will have closures referring to the scope in which `inner` was defined,
because they use the variable 'a'. As a result, you would not be able to send ``f1`` or ``f2``
with IPython. Note that you *would* be able to send `f`. This is only true for interactively
defined functions (as are often used in decorators), and only when there are variables used
inside the inner function, that are defined in the outer function. If the names are *not* in the
outer function, then there will not be a closure, and the generated function will look in
``globals()`` for the name:

.. sourcecode:: python

    def g(b):
        # note that `b` is not referenced in inner's scope
        def inner():
            # this inner will *not* have a closure
            return a
        return inner
    g1 = g(1)
    g2 = g(2)
    g1() # raises NameError on 'a'
    a=5
    g2() # returns 5

`g1` and `g2` *will* be sendable with IPython, and will treat the engine's namespace as
globals().  The :meth:`pull` method is implemented based on this principle.  If we did not
provide pull, you could implement it yourself with `apply`, by simply returning objects out
of the global namespace:

.. sourcecode:: ipython

    In [10]: view.apply(lambda : a)
    
    # is equivalent to
    In [11]: view.pull('a')


You can send functions with closures if you enable using dill or cloudpickle:

.. sourcecode:: ipython

    In [10]: rc[:].use_cloudpickle()

which will use a more advanced pickling library, which covers things like closures.


Running Code
============

There are two principal units of execution in Python: strings of Python code (e.g. 'a=5'),
and Python functions.  IPython is designed around the use of functions via the core
Client method, called `apply`.

Apply
-----

The principal method of remote execution is :meth:`apply`, of
:class:`~ipyparallel.client.view.View` objects. The Client provides the full execution and
communication API for engines via its low-level :meth:`send_apply_message` method, which is used
by all higher level methods of its Views.

f : function
    The function to be called remotely
args : tuple/list
    The positional arguments passed to `f`
kwargs : dict
    The keyword arguments passed to `f`

flags for all views:

block : bool (default: view.block)
    Whether to wait for the result, or return immediately.

    False:
        returns AsyncResult
    True:
        returns actual result(s) of ``f(*args, **kwargs)``

        if multiple targets:
            list of results, matching `targets`

track : bool [default view.track]
    whether to track non-copying sends.

targets : int,list of ints, 'all', None [default view.targets]
    Specify the destination of the job.

    if 'all' or None:
        Run on all active engines
    if list:
        Run on each specified engine
    if int:
        Run on single engine

.. note::

    :class:`LoadBalancedView` uses targets to restrict possible destinations.
    LoadBalanced calls will always execute in just one location.

flags only in LoadBalancedViews:

after : Dependency or collection of msg_ids
    Only for load-balanced execution (targets=None)
    Specify a list of msg_ids as a time-based dependency.
    This job will only be run *after* the dependencies
    have been met.

follow : Dependency or collection of msg_ids
    Only for load-balanced execution (targets=None)
    Specify a list of msg_ids as a location-based dependency.
    This job will only be run on an engine where this dependency
    is met.

timeout : float/int or None
    Only for load-balanced execution (targets=None)
    Specify an amount of time (in seconds) for the scheduler to
    wait for dependencies to be met before failing with a
    DependencyTimeout.

execute and run
---------------

For executing strings of Python code, :class:`DirectView` 's also provide an :meth:`execute` and
a :meth:`run` method, which rather than take functions and arguments, take simple strings.
`execute` simply takes a string of Python code to execute, and sends it to the Engine(s). `run`
is the same as `execute`, but for a *file*, rather than a string. It is simply a wrapper that
does something very similar to ``execute(open(f).read())``.

.. note::

    TODO: Examples for execute and run

Views
=====

The principal extension of the :class:`~parallel.Client` is the :class:`~parallel.View`
class. The client is typically a singleton for connecting to a cluster, and presents a
low-level interface to the Hub and Engines. Most real usage will involve creating one or more
:class:`~parallel.View` objects for working with engines in various ways.


DirectView
----------

The :class:`.DirectView` is the class for the IPython :ref:`Multiplexing Interface
<parallel_multiengine>`.

Creating a DirectView
*********************

DirectViews can be created in two ways, by index access to a client, or by a client's
:meth:`view` method.  Index access to a Client works in a few ways.  First, you can create
DirectViews to single engines simply by accessing the client by engine id:

.. sourcecode:: ipython

    In [2]: rc[0]
    Out[2]: <DirectView 0>

You can also create a DirectView with a list of engines:

.. sourcecode:: ipython

    In [2]: rc[0,1,2]
    Out[2]: <DirectView [0,1,2]>

Other methods for accessing elements, such as slicing and negative indexing, work by passing
the index directly to the client's :attr:`ids` list, so:

.. sourcecode:: ipython

    # negative index
    In [2]: rc[-1]
    Out[2]: <DirectView 3>
    
    # or slicing:
    In [3]: rc[::2]
    Out[3]: <DirectView [0,2]>

are always the same as:

.. sourcecode:: ipython

    In [2]: rc[rc.ids[-1]]
    Out[2]: <DirectView 3>
    
    In [3]: rc[rc.ids[::2]]
    Out[3]: <DirectView [0,2]>

Also note that the slice is evaluated at the time of construction of the DirectView, so the 
targets will not change over time if engines are added/removed from the cluster.

Execution via DirectView
************************

The DirectView is the simplest way to work with one or more engines directly (hence the name).

For instance, to get the process ID of all your engines:

.. sourcecode:: ipython

    In [5]: import os
    
    In [6]: dview.apply_sync(os.getpid)
    Out[6]: [1354, 1356, 1358, 1360]

Or to see the hostname of the machine they are on:

.. sourcecode:: ipython

    In [5]: import socket
    
    In [6]: dview.apply_sync(socket.gethostname)
    Out[6]: ['tesla', 'tesla', 'edison', 'edison', 'edison']

.. note::

    TODO: expand on direct execution

Data movement via DirectView
****************************

Since a Python namespace is just a :class:`dict`, :class:`DirectView` objects provide
dictionary-style access by key and methods such as :meth:`get` and
:meth:`update` for convenience. This make the remote namespaces of the engines
appear as a local dictionary. Underneath, these methods call :meth:`apply`:

.. sourcecode:: ipython

    In [51]: dview['a']=['foo','bar']

    In [52]: dview['a']
    Out[52]: [ ['foo', 'bar'], ['foo', 'bar'], ['foo', 'bar'], ['foo', 'bar'] ]

Scatter and gather
------------------

Sometimes it is useful to partition a sequence and push the partitions to
different engines. In MPI language, this is know as scatter/gather and we
follow that terminology. However, it is important to remember that in
IPython's :class:`Client` class, :meth:`scatter` is from the
interactive IPython session to the engines and :meth:`gather` is from the
engines back to the interactive IPython session. For scatter/gather operations
between engines, MPI should be used:

.. sourcecode:: ipython

    In [58]: dview.scatter('a',range(16))
    Out[58]: [None,None,None,None]

    In [59]: dview['a']
    Out[59]: [ [0, 1, 2, 3], [4, 5, 6, 7], [8, 9, 10, 11], [12, 13, 14, 15] ]

    In [60]: dview.gather('a')
    Out[60]: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]

Push and pull
-------------

:meth:`~ipyparallel.client.view.DirectView.push`

:meth:`~ipyparallel.client.view.DirectView.pull`

.. note::

    TODO: write this section


LoadBalancedView
----------------

The :class:`~.LoadBalancedView` is the class for load-balanced execution via the task scheduler.
These views always run tasks on exactly one engine, but let the scheduler determine where that
should be, allowing load-balancing of tasks. The LoadBalancedView does allow you to specify
restrictions on where and when tasks can execute, for more complicated load-balanced workflows.

Data Movement
=============

Since the :class:`~.LoadBalancedView` does not know where execution will take place, explicit
data movement methods like push/pull and scatter/gather do not make sense, and are not provided.

Results
=======

AsyncResults
------------

Our primary representation of the results of remote execution is the :class:`~.AsyncResult`
object, based on the object of the same name in the built-in :mod:`multiprocessing.pool`
module. Our version provides a superset of that interface.

The basic principle of the AsyncResult is the encapsulation of one or more results not yet completed.  Execution methods (including data movement, such as push/pull) will all return
AsyncResults when `block=False`.

The mp.pool.AsyncResult interface
---------------------------------

The basic interface of the AsyncResult is exactly that of the AsyncResult in :mod:`multiprocessing.pool`, and consists of four methods:

.. AsyncResult spec directly from docs.python.org

.. class:: AsyncResult

   The stdlib AsyncResult spec
   
   .. method:: wait([timeout])

      Wait until the result is available or until *timeout* seconds pass. This
      method always returns ``None``.

   .. method:: ready()

      Return whether the call has completed.

   .. method:: successful()

      Return whether the call completed without raising an exception.  Will
      raise :exc:`AssertionError` if the result is not ready.

   .. method:: get([timeout])

      Return the result when it arrives.  If *timeout* is not ``None`` and the
      result does not arrive within *timeout* seconds then
      :exc:`TimeoutError` is raised.  If the remote call raised
      an exception then that exception will be reraised as a :exc:`RemoteError`
      by :meth:`get`.


While an AsyncResult is not done, you can check on it with its :meth:`ready` method, which will
return whether the AR is done. You can also wait on an AsyncResult with its :meth:`wait` method.
This method blocks until the result arrives. If you don't want to wait forever, you can pass a
timeout (in seconds) as an argument to :meth:`wait`. :meth:`wait` will *always return None*, and
should never raise an error.

:meth:`ready` and :meth:`wait` are insensitive to the success or failure of the call. After a
result is done, :meth:`successful` will tell you whether the call completed without raising an
exception.

If you actually want the result of the call, you can use :meth:`get`. Initially, :meth:`get`
behaves just like :meth:`wait`, in that it will block until the result is ready, or until a
timeout is met. However, unlike :meth:`wait`, :meth:`get` will raise a :exc:`TimeoutError` if
the timeout is reached and the result is still not ready. If the result arrives before the
timeout is reached, then :meth:`get` will return the result itself if no exception was raised,
and will raise an exception if there was.

Here is where we start to expand on the multiprocessing interface. Rather than raising the
original exception, a RemoteError will be raised, encapsulating the remote exception with some
metadata. If the AsyncResult represents multiple calls (e.g. any time `targets` is plural), then
a CompositeError, a subclass of RemoteError, will be raised.

.. seealso::

    For more information on remote exceptions, see :ref:`the section in the Direct Interface
    <parallel_exceptions>`.

Extended interface
******************


Other extensions of the AsyncResult interface include convenience wrappers for :meth:`get`.
AsyncResults have a property, :attr:`result`, with the short alias :attr:`r`, which simply call
:meth:`get`. Since our object is designed for representing *parallel* results, it is expected
that many calls (any of those submitted via DirectView) will map results to engine IDs. We
provide a :meth:`get_dict`, which is also a wrapper on :meth:`get`, which returns a dictionary
of the individual results, keyed by engine ID.

You can also prevent a submitted job from actually executing, via the AsyncResult's
:meth:`abort` method. This will instruct engines to not execute the job when it arrives.

The larger extension of the AsyncResult API is the :attr:`metadata` attribute.  The metadata
is a dictionary (with attribute access) that contains, logically enough, metadata about the
execution.

Metadata keys:

timestamps

submitted
    When the task left the Client
started
    When the task started execution on the engine
completed
    When execution finished on the engine
received
    When the result arrived on the Client

    note that it is not known when the result arrived in 0MQ on the client, only when it
    arrived in Python via :meth:`Client.spin`, so in interactive use, this may not be
    strictly informative.

Information about the engine

engine_id
    The integer id
engine_uuid
    The UUID of the engine

output of the call

error
    Python exception, if there was one
execute_input
    The code (str) that was executed
execute_result
    Python output of an execute request (not apply),
    as a Jupyter message dictionary.
stderr
    stderr stream
stdout
    stdout (e.g. print) stream

And some extended information

status
    either 'ok' or 'error'
msg_id
    The UUID of the message
after
    For tasks: the time-based msg_id dependencies
follow
    For tasks: the location-based msg_id dependencies

While in most cases, the Clients that submitted a request will be the ones using the results,
other Clients can also request results directly from the Hub. This is done via the Client's
:meth:`get_result` method. This method will *always* return an AsyncResult object. If the call
was not submitted by the client, then it will be a subclass, called :class:`AsyncHubResult`.
These behave in the same way as an AsyncResult, but if the result is not ready, waiting on an
AsyncHubResult polls the Hub, which is much more expensive than the passive polling used
in regular AsyncResults.


The Client keeps track of all results
history, results, metadata

Querying the Hub
================

The Hub sees all traffic that may pass through the schedulers between engines and clients.
It does this so that it can track state, allowing multiple clients to retrieve results of
computations submitted by their peers, as well as persisting the state to a database.

queue_status

    You can check the status of the queues of the engines with this command.

result_status

    check on results

purge_results

    forget results (conserve resources)

Controlling the Engines
=======================

There are a few actions you can do with Engines that do not involve execution.  These
messages are sent via the Control socket, and bypass any long queues of waiting execution
jobs

abort

    Sometimes you may want to prevent a job you have submitted from actually running. The method
    for this is :meth:`abort`. It takes a container of msg_ids, and instructs the Engines to not
    run the jobs if they arrive. The jobs will then fail with an AbortedTask error.

clear

    You may want to purge the Engine(s) namespace of any data you have left in it.  After
    running `clear`, there will be no names in the Engine's namespace

shutdown

    You can also instruct engines (and the Controller) to terminate from a Client.  This 
    can be useful when a job is finished, since you can shutdown all the processes with a
    single command.

Synchronization
===============

Since the Client is a synchronous object, events do not automatically trigger in your
interactive session - you must poll the 0MQ sockets for incoming messages.  Note that 
this polling *does not* actually make any network requests.  It simply performs a `select`
operation, to check if messages are already in local memory, waiting to be handled.

The method that handles incoming messages is :meth:`spin`. This method flushes any waiting
messages on the various incoming sockets, and updates the state of the Client.

If you need to wait for particular results to finish, you can use the :meth:`wait` method,
which will call :meth:`spin` until the messages are no longer outstanding. Anything that
represents a collection of messages, such as a list of msg_ids or one or more AsyncResult
objects, can be passed as argument to wait. A timeout can be specified, which will prevent
the call from blocking for more than a specified time, but the default behavior is to wait
forever.

The client also has an ``outstanding`` attribute - a ``set`` of msg_ids that are awaiting
replies. This is the default if wait is called with no arguments - i.e. wait on *all*
outstanding messages.


.. note::

    TODO wait example

Map
===

Many parallel computing problems can be expressed as a ``map``, or running a single program with
a variety of different inputs. Python has a built-in :py:func:`map`, which does exactly this,
and many parallel execution tools in Python, such as the built-in
:py:class:`multiprocessing.Pool` object provide implementations of `map`. All View objects
provide a :meth:`map` method as well, but the load-balanced and direct implementations differ.

Views' map methods can be called on any number of sequences, but they can also take the `block`
and `bound` keyword arguments, just like :meth:`~client.apply`, but *only as keywords*.

.. sourcecode:: python

    dview.map(*sequences, block=None)


* iter, map_async, reduce

Decorators and RemoteFunctions
==============================

.. note::

    TODO: write this section

:func:`~ipyparallel.client.remotefunction.@parallel`

:func:`~ipyparallel.client.remotefunction.@remote`

:class:`~ipyparallel.client.remotefunction.RemoteFunction`

:class:`~ipyparallel.client.remotefunction.ParallelFunction`

Dependencies
============

.. note::

    TODO: write this section

:func:`~ipyparallel.controller.dependency.@depend`

:func:`~ipyparallel.controller.dependency.@require`

:class:`~ipyparallel.controller.dependency.Dependency`
