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# IPython’s Direct interface

The direct, or multiengine, interface represents one possible way of working with a set of IPython engines. The basic idea behind the multiengine interface is that the capabilities of each engine are directly and explicitly exposed to the user. Thus, in the multiengine 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

To follow along with this tutorial, you will need to start the IPython controller and four IPython engines. The simplest way of doing this is to use the ipcluster command:

    $ ipcluster start -n 4

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

## Creating a `DirectView` instance

The first step is to import the IPython `IPython.parallel` module and then create a `Client` instance:

    In [1]: from IPython.parallel import Client

    In [2]: rc = Client()

This form assumes that the default connection information (stored in `ipcontroller-client.json` found in `IPYTHONDIR/profile_default/security`) is accurate. If the controller was started on a remote machine, you must copy that connection file to the client machine, or enter its contents as arguments to the Client constructor:

    # If you have copied the json connector file from the controller:
    In [2]: rc = Client('/path/to/ipcontroller-client.json')
    # or to connect with a specific profile you have set up:
    In [3]: rc = Client(profile='mpi')

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

    In [3]: 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 simply want to apply a Python function to a sequence of objects, but in parallel. The client interface 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 just use the builtin `map()` with a `RemoteFunction`, or a DirectView’s `map()` method:

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

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

    In [67]: serial_result==parallel_result
    Out[67]: True

Note

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

See also

`map()` is implemented via `ParallelFunction`.

### Remote function decorators

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

    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 do 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.

## 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, 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 various ways to call functions with IPython, and these flags are set as attributes of the View. The `DirectView` has just two of these flags:

dv.block : bool
  whether to wait for the result, or return an `AsyncResult` object immediately
dv.track : bool
  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.
dv.targets : int, list of ints
  which targets this view is associated with.

Creating a view is simple: 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 set block temporarily 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]

### Non-blocking execution

In non-blocking 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 Python/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 tuple of `AsyncResult` objects (or msg_ids or 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]: pr_list = [dview.apply_async(wait, 3) for i in range(10)]

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

    # Then, their results are ready using get() or the `.r` attribute
    In [75]: pr_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 = v.client.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 to and from 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 just 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')
    Out[60]: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]

## Other things to look at

### 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 will want to import packages both in your interactive session and on your remote engines. This can be done with the `ContextManager` 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.

You can also specify imports via the `@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 `@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 [69]: from IPython.parallel import require

    In [70]: @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]: @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

In the multiengine interface, parallel commands can raise Python exceptions, just like serial commands. But it is a little subtle, because a single parallel command can actually 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 in most cases. The `CompositeError` class is a special type of exception that wraps one or more other types of 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 parallel.error.CompositeError, 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 simple type `%debug` after one of these `CompositeError` exceptions is raised, and inspect the exception instance:

    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:

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

    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

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

Since you might have 100 engines, you probably don’t want to see 100 tracebacks for a simple NameError because of a 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 [20]: from IPython.parallel import CompositeError
    In [21]: CompositeError.tb_limit = 1
    In [22]: %px a=b
    [0:execute]:
    ---------------------------------------------------------------------------
    NameError                                 Traceback (most recent call last)
    ----> 1 a=b
    NameError: name 'b' is not defined

    ... 3 more exceptions ...

All of this same error handling magic even works 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 ...

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