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# The IPython task interface

The task interface to the cluster presents the engines as a fault tolerant, dynamic load-balanced system of workers. Unlike the multiengine interface, in the task interface the user have no direct access to individual engines. By allowing the IPython scheduler to assign work, this interface is simultaneously simpler and more powerful.

Best of all, the user can use both of these interfaces running at the same time to take advantage of their respective strengths. When the user can break up the user’s work into segments that do not depend on previous execution, the task interface is ideal. But it also has more power and flexibility, allowing the user to guide the distribution of jobs, without having to assign tasks to engines explicitly.

## 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 `Client` instance

The first step is to import the IPython `IPython.parallel` module and then create a `Client` instance, and we will also be using a `LoadBalancedView`, here called lview:

    In [1]: from IPython.parallel import Client

    In [2]: rc = Client()

This form assumes that the controller was started on localhost with default configuration. If not, the location of the controller must be given as an argument to the constructor:

    # for a visible LAN controller listening on an external port:
    In [2]: rc = Client('tcp://192.168.1.16:10101')
    # or to connect with a specific profile you have set up:
    In [3]: rc = Client(profile='mpi')

For load-balanced execution, we will make use of a `LoadBalancedView` object, which can be constructed via the client’s `load_balanced_view()` method:

    In [4]: lview = rc.load_balanced_view() # default load-balanced view

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. Like the multiengine interface, these can be implemented via the task interface. The exact same tools can perform these actions in load-balanced ways as well as multiplexed ways: a parallel version of `map()` and `@parallel()` function decorator. If one specifies the argument balanced=True, then they are dynamically load balanced. Thus, if the execution time per item varies significantly, you should use the versions in the task interface.

### Parallel map

To load-balance `map()`,simply use a LoadBalancedView:

    In [62]: lview.block = True

        In [63]: serial_result = map(lambda x:x**10, range(32))

        In [64]: parallel_result = lview.map(lambda x:x**10, range(32))

        In [65]: serial_result==parallel_result
        Out[65]: True

### Parallel function decorator

Parallel functions are just like normal function, but they can be called on sequences and in parallel. The multiengine interface provides a decorator that turns any Python function into a parallel function:

    In [10]: @lview.parallel()
       ....: def f(x):
       ....:     return 10.0*x**4
       ....:

    In [11]: f.map(range(32))    # this is done in parallel
    Out[11]: [0.0,10.0,160.0,...]

## Dependencies

Often, pure atomic load-balancing is too primitive for your work. In these cases, you may want to associate some kind of Dependency that describes when, where, or whether a task can be run. In IPython, we provide two types of dependencies: Functional Dependencies and Graph Dependencies

Note

It is important to note that the pure ZeroMQ scheduler does not support dependencies, and you will see errors or warnings if you try to use dependencies with the pure scheduler.

### Functional Dependencies

Functional dependencies are used to determine whether a given engine is capable of running a particular task. This is implemented via a special `Exception` class, `UnmetDependency`, found in IPython.parallel.error. Its use is very simple: if a task fails with an UnmetDependency exception, then the scheduler, instead of relaying the error up to the client like any other error, catches the error, and submits the task to a different engine. This will repeat indefinitely, and a task will never be submitted to a given engine a second time.

You can manually raise the `UnmetDependency` yourself, but IPython has provided some decorators for facilitating this behavior.

There are two decorators and a class used for functional dependencies:

    In [9]: from IPython.parallel import depend, require, dependent

#### @require

The simplest sort of dependency is requiring that a Python module is available. The `@require` decorator lets you define a function that will only run on engines where names you specify are importable:

    In [10]: @require('numpy', 'zmq')
        ...: def myfunc():
        ...:     return dostuff()

Now, any time you apply `myfunc()`, the task will only run on a machine that has numpy and pyzmq available, and when `myfunc()` is called, numpy and zmq will be imported.

#### @depend

The `@depend` decorator lets you decorate any function with any other function to evaluate the dependency. The dependency function will be called at the start of the task, and if it returns `False`, then the dependency will be considered unmet, and the task will be assigned to another engine. If the dependency returns anything other than ``False``, the rest of the task will continue.

    In [10]: def platform_specific(plat):
        ...:    import sys
        ...:    return sys.platform == plat

    In [11]: @depend(platform_specific, 'darwin')
        ...: def mactask():
        ...:    do_mac_stuff()

    In [12]: @depend(platform_specific, 'nt')
        ...: def wintask():
        ...:    do_windows_stuff()

In this case, any time you apply `mytask`, it will only run on an OSX machine. `@depend` is just like `apply`, in that it has a `@depend(f,*args,**kwargs)` signature.

#### dependents

You don’t have to use the decorators on your tasks, if for instance you may want to run tasks with a single function but varying dependencies, you can directly construct the `dependent` object that the decorators use:

### Graph Dependencies

Sometimes you want to restrict the time and/or location to run a given task as a function of the time and/or location of other tasks. This is implemented via a subclass of `set`, called a `Dependency`. A Dependency is just a set of msg_ids corresponding to tasks, and a few attributes to guide how to decide when the Dependency has been met.

The switches we provide for interpreting whether a given dependency set has been met:

any|all
  Whether the dependency is considered met if any of the dependencies are done, or only after all of them have finished. This is set by a Dependency’s `all` boolean attribute, which defaults to `True`.
success [default: True]
  Whether to consider tasks that succeeded as fulfilling dependencies.
failure [default : False]
  Whether to consider tasks that failed as fulfilling dependencies. using failure=True,success=False is useful for setting up cleanup tasks, to be run only when tasks have failed.

Sometimes you want to run a task after another, but only if that task succeeded. In this case, `success` should be `True` and `failure` should be `False`. However sometimes you may not care whether the task succeeds, and always want the second task to run, in which case you should use success=failure=True. The default behavior is to only use successes.

There are other switches for interpretation that are made at the task level. These are specified via keyword arguments to the client’s `apply()` method.

after,follow
  You may want to run a task after a given set of dependencies have been run and/or run it where another set of dependencies are met. To support this, every task has an after dependency to restrict time, and a follow dependency to restrict destination.
timeout
  You may also want to set a time-limit for how long the scheduler should wait before a task’s dependencies are met. This is done via a timeout, which defaults to 0, which indicates that the task should never timeout. If the timeout is reached, and the scheduler still hasn’t been able to assign the task to an engine, the task will fail with a `DependencyTimeout`.

Note

Dependencies only work within the task scheduler. You cannot instruct a load-balanced task to run after a job submitted via the MUX interface.

The simplest form of Dependencies is with all=True,success=True,failure=False. In these cases, you can skip using Dependency objects, and just pass msg_ids or AsyncResult objects as the follow and after keywords to `client.apply()`:

    In [14]: client.block=False

    In [15]: ar = lview.apply(f, args, kwargs)

    In [16]: ar2 = lview.apply(f2)

    In [17]: ar3 = lview.apply_with_flags(f3, after=[ar,ar2])

    In [17]: ar4 = lview.apply_with_flags(f3, follow=[ar], timeout=2.5)

See also

Some parallel workloads can be described as a Directed Acyclic Graph, or DAG. See DAG Dependencies for an example demonstrating how to use map a NetworkX DAG onto task dependencies.

#### Impossible Dependencies

The schedulers do perform some analysis on graph dependencies to determine whether they are not possible to be met. If the scheduler does discover that a dependency cannot be met, then the task will fail with an `ImpossibleDependency` error. This way, if the scheduler realized that a task can never be run, it won’t sit indefinitely in the scheduler clogging the pipeline.

The basic cases that are checked:

- depending on nonexistent messages
- follow dependencies were run on more than one machine and all=True
- any dependencies failed and all=True,success=True,failures=False
- all dependencies failed and all=False,success=True,failure=False

Warning

This analysis has not been proven to be rigorous, so it is likely possible for tasks to become impossible to run in obscure situations, so a timeout may be a good choice.

## Retries and Resubmit

### Retries

Another flag for tasks is retries. This is an integer, specifying how many times a task should be resubmitted after failure. This is useful for tasks that should still run if their engine was shutdown, or may have some statistical chance of failing. The default is to not retry tasks.

### Resubmit

Sometimes you may want to re-run a task. This could be because it failed for some reason, and you have fixed the error, or because you want to restore the cluster to an interrupted state. For this, the `Client` has a `rc.resubmit()` method. This simply takes one or more msg_ids, and returns an `AsyncHubResult` for the result(s). You cannot resubmit a task that is pending - only those that have finished, either successful or unsuccessful.

## Schedulers

There are a variety of valid ways to determine where jobs should be assigned in a load-balancing situation. In IPython, we support several standard schemes, and even make it easy to define your own. The scheme can be selected via the `scheme` argument to ipcontroller, or in the `TaskScheduler.schemename` attribute of a controller config object.

The built-in routing schemes:

To select one of these schemes, simply do:

    $ ipcontroller --scheme=<schemename>
    for instance:
    $ ipcontroller --scheme=lru

lru: Least Recently Used

> Always assign work to the least-recently-used engine. A close relative of round-robin, it will be fair with respect to the number of tasks, agnostic with respect to runtime of each task.

plainrandom: Plain Random

> Randomly picks an engine on which to run.

twobin: Two-Bin Random

> Requires numpy

> Pick two engines at random, and use the LRU of the two. This is known to be better than plain random in many cases, but requires a small amount of computation.

leastload: Least Load

> This is the default scheme

> Always assign tasks to the engine with the fewest outstanding tasks (LRU breaks tie).

weighted: Weighted Two-Bin Random

> Requires numpy

> Pick two engines at random using the number of outstanding tasks as inverse weights, and use the one with the lower load.

### Pure ZMQ Scheduler

For maximum throughput, the ‘pure’ scheme is not Python at all, but a C-level `MonitoredQueue` from PyZMQ, which uses a ZeroMQ `XREQ` socket to perform all load-balancing. This scheduler does not support any of the advanced features of the Python `Scheduler`.

Disabled features when using the ZMQ Scheduler:

- Engine unregistration

    Task farming will be disabled if an engine unregisters. Further, if an engine is unregistered during computation, the scheduler may not recover.

- Dependencies

    Since there is no Python logic inside the Scheduler, routing decisions cannot be made based on message content.

- Early destination notification

    The Python schedulers know which engine gets which task, and notify the Hub. This allows graceful handling of Engines coming and going. There is no way to know where ZeroMQ messages have gone, so there is no way to know what tasks are on which engine until they finish. This makes recovery from engine shutdown very difficult.

Note

TODO: performance comparisons

## More details

The `LoadBalancedView` has many more powerful features that allow quite a bit of flexibility in how tasks are defined and run. The next places to look are in the following classes:

- `LoadBalancedView`
- `AsyncResult`
- `apply()`
- `dependency`

The following is an overview of how to use these classes together:

1. Create a `Client` and `LoadBalancedView`
2. Define some functions to be run as tasks
3. Submit your tasks to using the `apply()` method of your `LoadBalancedView` instance.
4. Use `Client.get_result()` to get the results of the tasks, or use the `AsyncResult.get()` method of the results to wait for and then receive the results.

See also

A demo of DAG Dependencies with NetworkX and IPython.

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