IPython’s Direct interface#

The direct interface represents one possible way of working with a set of IPython engines. The basic idea behind the direct interface is that the capabilities of each engine are directly and explicitly exposed to the user. Thus, in the direct interface, each engine is given an id that is used to identify the engine and give it work to do. This interface is very intuitive and is designed with interactive usage in mind, and is the best place for new users of IPython to begin.

Starting the IPython controller and engines#

In general, in this tutorial, each step will start with a fresh cluster.

There is always a choice when starting an interactive session:

Option 1. starting a new cluster

import ipyparallel as ipp
cluster = ipp.Cluster(n=4)
cluster.start_cluster_sync()

Option 2. connecting to an existing cluster, e.g. if it were started via ipcluster start or another notebook, or a JupyterLab extension.

import ipyparallel as ipp
cluster = ipp.Cluster.from_file()

No arguments are required for the default cluster (e.g. ipcluster start with no arguments), but profile and/or cluster_id would be typical arguments to specify a cluster.

For more detailed information about starting the controller and engines, see our introduction to using IPython for parallel computing.

Creating a `DirectView`#

The first step is to connect a Client to your cluster:

In [2]: rc = cluster.connect_client_sync()

To make sure there are engines connected to the controller, users can get a list of engine ids:

In [3]: rc.wait_for_engines(4); rc.ids
Out[3]: [0, 1, 2, 3]

Here we see that there are four engines ready to do work for us.

For direct execution, we will make use of a DirectView object, which can be constructed via list-access to the client:

In [4]: dview = rc[:] # use all engines

See also

For more information, see the in-depth explanation of Views.

Quick and easy parallelism#

In many cases, you want to call a Python function on a sequence of objects, but in parallel. IPython Parallel provides a simple way of accomplishing this: using the DirectView’s map() method.

Parallel map#

Python’s builtin map() functions allows a function to be applied to a sequence element-by-element. This type of code is typically trivial to parallelize. In fact, since IPython’s interface is all about functions anyway, you can use the builtin map() with a RemoteFunction, or a DirectView’s map() method:

In [62]: serial_result = list(map(lambda x:x**10, range(32)))

In [63]: parallel_result = dview.map_sync(lambda x: x**10, range(32))

In [64]: serial_result == parallel_result
Out[64]: True

Note

The DirectView’s version of map() does not do dynamic load balancing. For a load-balanced version, use a LoadBalancedView.

Calling Python functions#

The most basic type of operation that can be performed on the engines is to execute Python code or call Python functions. Executing Python code can be done in blocking or non-blocking mode (non-blocking is default) using the View.execute() method, and calling functions can be done via the View.apply() method.

apply#

The main method for doing remote execution (in fact, almost all methods that communicate with the engines are built on top of it), is View.apply().

We strive to provide the cleanest interface we can, so apply has the following signature:

view.apply(f, *args, **kwargs)

There are some controls to influence the behavior of apply, called flags. Views store the default values for these flags as attributes. The DirectView has these flags:

dv.block: whether to wait for the result, or return an AsyncResult object immediately
dv.track: whether to instruct pyzmq to track when zeromq is done sending the message. This is primarily useful for non-copying sends of numpy arrays that you plan to edit in-place. You need to know when it becomes safe to edit the buffer without corrupting the message. There is a performance cost to enabling tracking, so it is not recommended except for sending very large messages.
dv.targets: The engines associated with this View.

Creating a view is done as if the client is a Python ‘container’ of engines: index-access on a client creates a DirectView.

In [4]: view = rc[1:3]
Out[4]: <DirectView [1, 2]>

In [5]: view.apply<tab>
view.apply  view.apply_async  view.apply_sync

For convenience, you can specify blocking behavior explicitly for a single call with the extra sync/async methods.

Blocking execution#

In blocking mode, the DirectView object (called dview in these examples) submits the command to the controller, which places the command in the engines’ queues for execution. The apply() call then blocks until the engines are done executing the command:

In [2]: dview = rc[:] # A DirectView of all engines
In [3]: dview.block=True
In [4]: dview['a'] = 5

In [5]: dview['b'] = 10

In [6]: dview.apply(lambda x: a+b+x, 27)
Out[6]: [42, 42, 42, 42]

You can also select blocking execution on a call-by-call basis with the apply_sync() method:

In [7]: dview.block = False

In [8]: dview.apply_sync(lambda x: a+b+x, 27)
Out[8]: [42, 42, 42, 42]

Python commands can be executed as strings on specific engines by using a View’s execute method:

In [6]: rc[::2].execute('c = a + b')

In [7]: rc[1::2].execute('c = a - b')

In [8]: dview['c'] # shorthand for dview.pull('c', block=True)
Out[8]: [15, -5, 15, -5]

async execution#

In non-blocking (async) mode, apply() submits the command to be executed and then returns a AsyncResult object immediately. The AsyncResult object gives you a way of getting a result at a later time through its get() method.

See also

Docs on the AsyncResult object.

This allows you to quickly submit long-running commands without blocking your local IPython session:

# define our function
In [6]: def wait(t):
  ....:     import time
  ....:     tic = time.time()
  ....:     time.sleep(t)
  ....:     return time.time()-tic

# In non-blocking mode
In [7]: ar = dview.apply_async(wait, 2)

# Now block for the result
In [8]: ar.get()
Out[8]: [2.0006198883056641, 1.9997570514678955, 1.9996809959411621, 2.0003249645233154]

# Again in non-blocking mode
In [9]: ar = dview.apply_async(wait, 10)

# Poll to see if the result is ready
In [10]: ar.ready()
Out[10]: False

# ask for the result, but wait a maximum of 1 second:
In [45]: ar.get(1)
---------------------------------------------------------------------------
TimeoutError                              Traceback (most recent call last)
/home/you/<ipython-input-45-7cd858bbb8e0> in <module>()
----> 1 ar.get(1)

/path/to/site-packages/IPython/parallel/asyncresult.pyc in get(self, timeout)
     62                 raise self._exception
     63         else:
---> 64             raise error.TimeoutError("Result not ready.")
     65
     66     def ready(self):

TimeoutError: Result not ready.

Note

Note the import inside the function. This is a common model, to ensure that the appropriate modules are imported where the task is run. You can also manually import modules into the engine(s) namespace(s) via view.execute('import numpy').

Often, it is desirable to wait until a set of AsyncResult objects are done. For this, there is a the method wait(). This method takes a collection of AsyncResult objects (or msg_ids or integer indices to the client’s history), and blocks until all of the associated results are ready:

In [72]: dview.block = False

# A trivial list of AsyncResults objects
In [73]: ar_list = [dview.apply_async(wait, 3) for i in range(10)]

# Wait until all of them are done
In [74]: dview.wait(ar_list)

# Then, their results are ready using get()
In [75]: ar_list[0].get()
Out[75]: [2.9982571601867676, 2.9982588291168213, 2.9987530708312988, 2.9990990161895752]

The `block` and `targets` keyword arguments and attributes#

Most DirectView methods (excluding apply()) accept block and targets as keyword arguments. As we have seen above, these keyword arguments control the blocking mode and which engines the command is applied to. The View class also has block and targets attributes that control the default behavior when the keyword arguments are not provided. Thus the following logic is used for block and targets:

If no keyword argument is provided, the instance attributes are used.
The keyword arguments, if provided overrides the instance attributes for the duration of a single call.

The following examples demonstrate how to use the instance attributes:

In [16]: dview.targets = [0, 2]

In [17]: dview.block = False

In [18]: ar = dview.apply(lambda : 10)

In [19]: ar.get()
Out[19]: [10, 10]

In [20]: dview.targets = rc.ids # all engines (4)

In [21]: dview.block = True

In [22]: dview.apply(lambda : 42)
Out[22]: [42, 42, 42, 42]

The block and targets instance attributes of the DirectView also determine the behavior of the parallel magic commands.

See also

See the documentation of the Parallel Magics.

Moving Python objects around#

In addition to calling functions and executing code on engines, you can transfer Python objects between your IPython session and the engines. In IPython, these operations are called push() (sending an object to the engines) and pull() (getting an object from the engines).

Basic push and pull#

Here are some examples of how you use push() and pull():

In [38]: dview.push(dict(a=1.03234, b=3453))
Out[38]: [None, None, None, None]

In [39]: dview.pull('a')
Out[39]: [ 1.03234, 1.03234, 1.03234, 1.03234]

In [40]: dview.pull('b', targets=0)
Out[40]: 3453

In [41]: dview.pull(('a', 'b'))
Out[41]: [ [1.03234, 3453], [1.03234, 3453], [1.03234, 3453], [1.03234, 3453] ]

In [42]: dview.push(dict(c='speed'))
Out[42]: [None, None, None, None]

In non-blocking mode push() and pull() also return AsyncResult objects:

In [48]: ar = dview.pull('a', block=False)

In [49]: ar.get()
Out[49]: [1.03234, 1.03234, 1.03234, 1.03234]

Dictionary interface#

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

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 Client class, scatter() is from the interactive IPython session to the engines and gather() is from the engines back to the interactive IPython session. For scatter/gather operations between engines, MPI, pyzmq, or some other direct interconnect should be used.

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') # This will show you the status of gather.
Out[60]: <AsyncMapResult: gather:finished>
In [61]: dview.gather('a').get() # This will give you the result.
Out[61]: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]
In [62]: dview.gather('a')[3] # You can also direct call the result.
Out[62]: [2]

Other things to look at#

Signaling engines#

New in IPython Parallel 7.0 is the Client.send_signal() method. This lets you directly interrupt engines, which might be running a blocking task that you want to cancel.

This is also available via the Cluster API. Unlike the Cluster API, though, which only allows interrupting whole engine ‘sets’ (usally all engines in the cluster), the client API allows interrupting individual engines.

In [9]: ar = rc[:].apply_async(time.sleep, 5)

In [10]: rc.send_signal(signal.SIGINT)
Out[10]: <Future at 0x7f91a9489fd0 state=pending>

In [11]: ar.get()
[12:apply]:
---------------------------------------------------------------------------
KeyboardInterrupt                         Traceback (most recent call last)
<string> in <module>

KeyboardInterrupt:

[13:apply]:
---------------------------------------------------------------------------
KeyboardInterrupt                         Traceback (most recent call last)
<string> in <module>

KeyboardInterrupt:

[14:apply]:
---------------------------------------------------------------------------
KeyboardInterrupt                         Traceback (most recent call last)
<string> in <module>

KeyboardInterrupt:

[15:apply]:
---------------------------------------------------------------------------
KeyboardInterrupt                         Traceback (most recent call last)
<string> in <module>

KeyboardInterrupt:

Remote function decorators#

Remote functions are like normal functions, but when they are called they execute on one or more engines rather than locally. IPython provides two decorators for producing parallel functions.

The first is @remote, which calls the function on every engine of a view.

In [10]: @dview.remote(block=True)
   ....: def getpid():
   ....:     import os
   ....:     return os.getpid()
   ....:

In [11]: getpid()
Out[11]: [12345, 12346, 12347, 12348]

The @parallel decorator creates parallel functions, that break up an element-wise operations and distribute them, reconstructing the result.

In [12]: import numpy as np

In [13]: A = np.random.random((64,48))

In [14]: @dview.parallel(block=True)
   ....: def pmul(A,B):
   ....:     return A*B

In [15]: C_local = A*A

In [16]: C_remote = pmul(A,A)

In [17]: (C_local == C_remote).all()
Out[17]: True

Calling a @parallel function does not correspond to map. It is used for splitting element-wise operations that operate on a sequence or array. For map behavior, parallel functions have a map method.

call	pfunc(seq)	pfunc.map(seq)
# of tasks	# of engines (1 per engine)	# of engines (1 per engine)
# of remote calls	# of engines (1 per engine)	`len(seq)`
argument to remote	`seq[i:j]` (sub-sequence)	`seq[i]` (single element)

A quick example to illustrate the difference in arguments for the two modes:

In [16]: @dview.parallel(block=True)
   ....: def echo(x):
   ....:     return str(x)

In [17]: echo(range(5))
Out[17]: ['[0, 1]', '[2]', '[3]', '[4]']

In [18]: echo.map(range(5))
Out[18]: ['0', '1', '2', '3', '4']

See also

See the parallel() and remote() decorators for options.

How to do parallel list comprehensions#

In many cases list comprehensions are nicer than using the map function. While we don’t have fully parallel list comprehensions, it is simple to get the basic effect using scatter() and gather():

In [66]: dview.scatter('x',range(64))

In [67]: %px y = [i**10 for i in x]
Parallel execution on engines: [0, 1, 2, 3]

In [68]: y = dview.gather('y')

In [69]: print y
[0, 1, 1024, 59049, 1048576, 9765625, 60466176, 282475249, 1073741824,...]

Remote imports#

Sometimes you may want to import packages both in your interactive session and on your remote engines. This can be done with the context manager created by a DirectView’s sync_imports() method:

In [69]: with dview.sync_imports():
   ....:     import numpy
importing numpy on engine(s)

Any imports made inside the block will also be performed on the view’s engines. sync_imports also takes a local boolean flag that defaults to True, which specifies whether the local imports should also be performed. However, support for local=False has not been implemented, so only packages that can be imported locally will work this way. Note that the usual renaming of the import handle in the same line like in import matplotlib.pyplot as plt does not work on the remote engine, the as plt is ignored remotely, while it executes locally. One could rename the remote handle with %px plt = pyplot though after the import.

You can also specify imports via the @ipp.require decorator. This is a decorator designed for use in dependencies, but can be used to handle remote imports as well. Modules or module names passed to @ipp.require will be imported before the decorated function is called. If they cannot be imported, the decorated function will never execute and will fail with an UnmetDependencyError. Failures of single Engines will be collected and raise a CompositeError, as demonstrated in the next section.

In [70]: @ipp.require('re')
   ....: def findall(pat, x):
   ....:     # re is guaranteed to be available
   ....:     return re.findall(pat, x)

# you can also pass modules themselves, that you already have locally:
In [71]: @ipp.require(time)
   ....: def wait(t):
   ....:     time.sleep(t)
   ....:     return t

Note

sync_imports() does not allow import foo as bar syntax, because the assignment represented by the as bar part is not available to the import hook.

Parallel exceptions#

Parallel commands can raise Python exceptions, just like serial commands. This is complicated by the fact that a single parallel command can raise multiple exceptions (one for each engine the command was run on). To express this idea, we have a CompositeError exception class that will be raised when there are mulitple errors. The CompositeError class is a special type of exception that wraps one or more other exceptions. Here is how it works:

In [78]: dview.block = True

In [79]: dview.execute("1/0")
[0:execute]:
---------------------------------------------------------------------------
ZeroDivisionError                         Traceback (most recent call last)
----> 1 1/0
ZeroDivisionError: integer division or modulo by zero

[1:execute]:
---------------------------------------------------------------------------
ZeroDivisionError                         Traceback (most recent call last)
----> 1 1/0
ZeroDivisionError: integer division or modulo by zero

[2:execute]:
---------------------------------------------------------------------------
ZeroDivisionError                         Traceback (most recent call last)
----> 1 1/0
ZeroDivisionError: integer division or modulo by zero

[3:execute]:
---------------------------------------------------------------------------
ZeroDivisionError                         Traceback (most recent call last)
----> 1 1/0
ZeroDivisionError: integer division or modulo by zero

Notice how the error message printed when CompositeError is raised has information about the individual exceptions that were raised on each engine. If you want, you can even raise one of these original exceptions:

In [80]: try:
   ....:     dview.execute('1/0', block=True)
   ....: except ipp.CompositeError as e:
   ....:     e.raise_exception()
   ....:
   ....:
---------------------------------------------------------------------------
ZeroDivisionError                         Traceback (most recent call last)
----> 1 1/0
ZeroDivisionError: integer division or modulo by zero

If you are working in IPython, you can type %debug after one of these CompositeError exceptions is raised and inspect the exception:

In [81]: dview.execute('1/0')
[0:execute]:
---------------------------------------------------------------------------
ZeroDivisionError                         Traceback (most recent call last)
----> 1 1/0
ZeroDivisionError: integer division or modulo by zero

[1:execute]:
---------------------------------------------------------------------------
ZeroDivisionError                         Traceback (most recent call last)
----> 1 1/0
ZeroDivisionError: integer division or modulo by zero

[2:execute]:
---------------------------------------------------------------------------
ZeroDivisionError                         Traceback (most recent call last)
----> 1 1/0
ZeroDivisionError: integer division or modulo by zero

[3:execute]:
---------------------------------------------------------------------------
ZeroDivisionError                         Traceback (most recent call last)
----> 1 1/0
ZeroDivisionError: integer division or modulo by zero

In [82]: %debug
> /.../site-packages/IPython/parallel/client/asyncresult.py(125)get()
    124             else:
--> 125                 raise self._exception
    126         else:

# Here, self._exception is the CompositeError instance:

ipdb> e = self._exception
ipdb> e
CompositeError(4)

# we can tab-complete on e to see available methods:
ipdb> e.<TAB>
e.args               e.message            e.traceback
e.elist              e.msg
e.ename              e.print_traceback
e.engine_info        e.raise_exception
e.evalue             e.render_traceback

# We can then display the individual tracebacks, if we want:
ipdb> e.print_traceback(1)
[1:execute]:
---------------------------------------------------------------------------
ZeroDivisionError                         Traceback (most recent call last)
----> 1 1/0
ZeroDivisionError: integer division or modulo by zero

If you have 100 engines, you probably don’t want to see 100 identical tracebacks for a NameError because of a small typo. For this reason, CompositeError truncates the list of exceptions it will print to CompositeError.tb_limit (default is five). You can change this limit to suit your needs with:

In [21]: ipp.CompositeError.tb_limit = 1
In [22]: %px x=z
[0:execute]:
---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
----> 1 x=z
NameError: name 'z' is not defined

... 3 more exceptions ...

All of this same error handling magic works the same in non-blocking mode:

In [83]: dview.block=False

In [84]: ar = dview.execute('1/0')

In [85]: ar.get()
[0:execute]:
---------------------------------------------------------------------------
ZeroDivisionError                         Traceback (most recent call last)
----> 1 1/0
ZeroDivisionError: integer division or modulo by zero

... 3 more exceptions ...

Sometimes you still want to get the successful subset, even when there was an error. Like asyncio.gather(), AsyncResult.get() and map functions accept a return_exception argument (new in IPython Parallel 7.0), to return the Exception objects among results instead of raising the first error encountered.

In [89]: ar = dview.apply_async(lambda: 1/0)
In [90]: ar.get(return_exceptions=True)
Out[90]:
[<Remote[0]:ZeroDivisionError(division by zero)>,
 <Remote[1]:ZeroDivisionError(division by zero)>,
 <Remote[2]:ZeroDivisionError(division by zero)>,
 <Remote[3]:ZeroDivisionError(division by zero)>]

```{versionadded} 7.0 The return_exceptions feature

IPython’s Direct interface#

Starting the IPython controller and engines#

Creating a DirectView#

Quick and easy parallelism#

Parallel map#

Calling Python functions#

apply#

Blocking execution#

async execution#

The block and targets keyword arguments and attributes#

Moving Python objects around#

Basic push and pull#

Dictionary interface#

Scatter and gather#

Other things to look at#

Signaling engines#

Remote function decorators#

How to do parallel list comprehensions#

Remote imports#

Parallel exceptions#

Creating a `DirectView`#

The `block` and `targets` keyword arguments and attributes#