# 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