(parallel-direct)=

# 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

``python 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 {command}`ipcluster start` or another notebook, or a JupyterLab extension.

``python 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 {ref}`introduction <parallel-overview>` to using IPython for parallel computing.

## Creating a `DirectView`

The first step is to connect a {class}`.Client` to your cluster:

``ipython 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:

``ipython 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 {class}`DirectView` object, which can be constructed via list-access to the client:

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

``{seealso} For more information, see the in-depth explanation of {ref}`Views <parallel-details>`. ``

## 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 {meth}`~DirectView.map` method.

### Parallel map

Python’s builtin {func}`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 {func}`map` with a {class}`RemoteFunction`, or a DirectView’s {meth}`map` method:

```ipython 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 {class}`DirectView`'s version of {meth}`map` does not do dynamic load balancing. For a load-balanced version, use a {class}`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 {meth}`.View.execute` method, and calling functions can be done via the {meth}`.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 {meth}`View.apply`.

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

``python 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 {class}`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 {class}`.DirectView`.

```ipython 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 {class}`.DirectView` object (called `dview` in these examples) submits the command to the controller, which places the command in the engines’ queues for execution. The {meth}`apply` call then blocks until the engines are done executing the command:

```ipython 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 {meth}`apply_sync` method:

```ipython 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:

```ipython 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, {meth}`apply` submits the command to be executed and then returns a {class}`AsyncResult` object immediately. The {class}`AsyncResult` object gives you a way of getting a result at a later time through its {meth}`get` method.

``{seealso} Docs on the {ref}`AsyncResult <asyncresult>` object. ``

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

```ipython # 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 {class}`AsyncResult` objects are done. For this, there is a the method {meth}`wait`. This method takes a collection of {class}`AsyncResult` objects (or `msg_ids` or integer indices to the client’s history), and blocks until all of the associated results are ready:

```ipython 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 {meth}`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 {class}`View` class also has {attr}`block` and {attr}`targets` attributes that control the default behavior when the keyword arguments are not provided. Thus the following logic is used for {attr}`block` and {attr}`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:

```ipython 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 {attr}`block` and {attr}`targets` instance attributes of the {class}`.DirectView` also determine the behavior of the parallel magic commands.

``{seealso} See the documentation of the {ref}`Parallel Magics <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 {meth}`push` (sending an object to the engines) and {meth}`pull` (getting an object from the engines).

### Basic push and pull

Here are some examples of how you use {meth}`push` and {meth}`pull`:

```ipython 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 {meth}`push` and {meth}`pull` also return {class}`AsyncResult` objects:

```ipython 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 {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, pyzmq, or some other direct interconnect 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’) # 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 {meth}`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.

```ipython 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.

```ipython 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.

```ipython 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:

```ipython 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’] ```

``{seealso} See the {func}`~.remotefunction.parallel` and {func}`~.remotefunction.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 {meth}`scatter` and {meth}`gather`:

```ipython 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 {meth}`sync_imports` method:

```ipython 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.

```ipython 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} {func}`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 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 {exc}`CompositeError` exception class that will be raised when there are mulitple errors. The {exc}`CompositeError` class is a special type of exception that wraps one or more other exceptions. Here is how it works:

```ipython 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 {exc}`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:

```ipython In [80]: try:

> ….: dview.execute(‘1/0’, block=True) ….: except ipp.CompositeError as e: ….: e.raise_exception() ….: ….:

ZeroDivisionError: integer division or modulo by zero ```

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

```ipython 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 {attr}`CompositeError.tb_limit` (default is five). You can change this limit to suit your needs with:

```ipython 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:

```ipython 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 {py:func}`asyncio.gather`, {meth}`.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.

```ipython 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

```

```