(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:
```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, 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.
```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.
```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:
- 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:
# 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 returning objects out
of the global namespace:
```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:
`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
: The function to be called remotely
args
: The positional arguments passed to f
kwargs
: The keyword arguments passed to f
flags for all views:
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
: whether to track non-copying sends.
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 on exactly one engine.
`
flags only in LoadBalancedViews:
after
: 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
: 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
: 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}`~.DirectView.execute` and
a {meth}`~.DirectView.run` method, which rather than take functions and arguments, take Python strings.
execute
takes a string of Python code to execute, and sends it to the Engine(s). run
is the same as execute
, but for a _filename_ rather than a string. It is 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-direct>`.
#### 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 by accessing the client by engine id:
`ipython
In [2]: rc[0]
Out[2]: <DirectView 0>
`
You can also create a DirectView with a list of engines:
`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:
```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:
```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:
In [6]: dview.apply_sync(os.getpid) Out[6]: [1354, 1356, 1358, 1360] ```
Or to see the hostname of the machine they are on:
```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 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`:
```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:
```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 {py:mod}`multiprocessing.pool` module. Our version provides a superset of that interface, and starting in 6.0 is a subclass of {class}`concurrent.futures.Future`.
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 {py:mod}`multiprocessing.pool`, and consists of four methods:
% AsyncResult spec directly from docs.python.org
```{eval-rst} .. class:: AsyncResult
The stdlib AsyncResult spec
- wait([timeout])¶
Wait until the result is available or until timeout seconds pass. This method always returns
None
.
- ready()¶
Return whether the call has completed.
- successful()¶
Return whether the call completed without raising an exception. Will raise
AssertionError
if the result is not ready.
- get([timeout])¶
Return the result when it arrives. If timeout is not
None
and the result does not arrive within timeout seconds thenTimeoutError
is raised. If the remote call raised an exception then that exception will be reraised as aRemoteError
byget()
.
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 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 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 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 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_ make any network requests. It 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 keyword arguments to influence how the work is distributed. What keyword arguments are available depends on the view being used.
```{eval-rst} .. class:: ipyparallel.DirectView
- noindex
```{eval-rst} .. class:: ipyparallel.LoadBalancedView
- noindex
## 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`