docs/source/parallel/parallel_process.txt

.. _parallel_process:

===========================================
Starting the IPython controller and engines
===========================================

To use IPython for parallel computing, you need to start one instance of
the controller and one or more instances of the engine. The controller
and each engine can run on different machines or on the same machine.
Because of this, there are many different possibilities.

Broadly speaking, there are two ways of going about starting a controller and engines:

* In an automated manner using the :command:`ipcluster` command.
* In a more manual way using the :command:`ipcontroller` and 
  :command:`ipengine` commands.

This document describes both of these methods. We recommend that new users
start with the :command:`ipcluster` command as it simplifies many common usage
cases.

General considerations
======================

Before delving into the details about how you can start a controller and
engines using the various methods, we outline some of the general issues that
come up when starting the controller and engines. These things come up no
matter which method you use to start your IPython cluster.

If you are running engines on multiple machines, you will likely need to instruct the
controller to listen for connections on an external interface. This can be done by specifying
the ``ip`` argument on the command-line, or the ``HubFactory.ip`` configurable in
:file:`ipcontroller_config.py`.

If your machines are on a trusted network, you can safely instruct the controller to listen
on all public interfaces with::

    $> ipcontroller --ip=*

Or you can set the same behavior as the default by adding the following line to your :file:`ipcontroller_config.py`:

.. sourcecode:: python

    c.HubFactory.ip = '*'

.. note::

    Due to the lack of security in ZeroMQ, the controller will only listen for connections on
    localhost by default. If you see Timeout errors on engines or clients, then the first
    thing you should check is the ip address the controller is listening on, and make sure
    that it is visible from the timing out machine.
    
.. seealso::

    Our `notes <parallel_security>`_ on security in the new parallel computing code.

Let's say that you want to start the controller on ``host0`` and engines on
hosts ``host1``-``hostn``. The following steps are then required:

1. Start the controller on ``host0`` by running :command:`ipcontroller` on
   ``host0``.  The controller must be instructed to listen on an interface visible
   to the engine machines, via the ``ip`` command-line argument or ``HubFactory.ip``
   in :file:`ipcontroller_config.py`.
2. Move the JSON file (:file:`ipcontroller-engine.json`) created by the 
   controller from ``host0`` to hosts ``host1``-``hostn``.
3. Start the engines on hosts ``host1``-``hostn`` by running 
   :command:`ipengine`.  This command has to be told where the JSON file 
   (:file:`ipcontroller-engine.json`) is located.

At this point, the controller and engines will be connected. By default, the JSON files
created by the controller are put into the :file:`~/.ipython/profile_default/security`
directory. If the engines share a filesystem with the controller, step 2 can be skipped as
the engines will automatically look at that location.

The final step required to actually use the running controller from a client is to move
the JSON file :file:`ipcontroller-client.json` from ``host0`` to any host where clients
will be run. If these file are put into the :file:`~/.ipython/profile_default/security`
directory of the client's host, they will be found automatically. Otherwise, the full path
to them has to be passed to the client's constructor.

Using :command:`ipcluster`
===========================

The :command:`ipcluster` command provides a simple way of starting a
controller and engines in the following situations:

1. When the controller and engines are all run on localhost. This is useful
   for testing or running on a multicore computer.
2. When engines are started using the :command:`mpiexec` command that comes 
   with most MPI [MPI]_ implementations
3. When engines are started using the PBS [PBS]_ batch system 
   (or other `qsub` systems, such as SGE).
4. When the controller is started on localhost and the engines are started on 
   remote nodes using :command:`ssh`.
5. When engines are started using the Windows HPC Server batch system.

.. note::

    Currently :command:`ipcluster` requires that the
    :file:`~/.ipython/profile_<name>/security` directory live on a shared filesystem that is
    seen by both the controller and engines. If you don't have a shared file
    system you will need to use :command:`ipcontroller` and
    :command:`ipengine` directly.

Under the hood, :command:`ipcluster` just uses :command:`ipcontroller`
and :command:`ipengine` to perform the steps described above.

The simplest way to use ipcluster requires no configuration, and will
launch a controller and a number of engines on the local machine. For instance, 
to start one controller and 4 engines on localhost, just do::

    $ ipcluster start --n=4

To see other command line options, do::

    $ ipcluster -h


Configuring an IPython cluster
==============================

Cluster configurations are stored as `profiles`.  You can create a new profile with::

    $ ipython profile create --parallel --profile=myprofile

This will create the directory :file:`IPYTHONDIR/profile_myprofile`, and populate it
with the default configuration files for the three IPython cluster commands. Once
you edit those files, you can continue to call ipcluster/ipcontroller/ipengine
with no arguments beyond ``profile=myprofile``, and any configuration will be maintained.

There is no limit to the number of profiles you can have, so you can maintain a profile for each
of your common use cases. The default profile will be used whenever the
profile argument is not specified, so edit :file:`IPYTHONDIR/profile_default/*_config.py` to
represent your most common use case.

The configuration files are loaded with commented-out settings and explanations,
which should cover most of the available possibilities.

Using various batch systems with :command:`ipcluster`
-----------------------------------------------------

:command:`ipcluster` has a notion of Launchers that can start controllers
and engines with various remote execution schemes.  Currently supported
models include :command:`ssh`, :command:`mpiexec`, PBS-style (Torque, SGE),
and Windows HPC Server.

.. note::

    The Launchers and configuration are designed in such a way that advanced
    users can subclass and configure them to fit their own system that we
    have not yet supported (such as Condor)

Using :command:`ipcluster` in mpiexec/mpirun mode
--------------------------------------------------


The mpiexec/mpirun mode is useful if you:

1. Have MPI installed.
2. Your systems are configured to use the :command:`mpiexec` or
   :command:`mpirun` commands to start MPI processes.

If these are satisfied, you can create a new profile::

    $ ipython profile create --parallel --profile=mpi

and edit the file :file:`IPYTHONDIR/profile_mpi/ipcluster_config.py`.

There, instruct ipcluster to use the MPIExec launchers by adding the lines:

.. sourcecode:: python

    c.IPClusterEngines.engine_launcher = 'IPython.parallel.apps.launcher.MPIExecEngineSetLauncher'

If the default MPI configuration is correct, then you can now start your cluster, with::

    $ ipcluster start --n=4 --profile=mpi

This does the following:

1. Starts the IPython controller on current host.
2. Uses :command:`mpiexec` to start 4 engines.

If you have a reason to also start the Controller with mpi, you can specify:

.. sourcecode:: python

    c.IPClusterStart.controller_launcher = 'IPython.parallel.apps.launcher.MPIExecControllerLauncher'

.. note::

    The Controller *will not* be in the same MPI universe as the engines, so there is not
    much reason to do this unless sysadmins demand it.

On newer MPI implementations (such as OpenMPI), this will work even if you
don't make any calls to MPI or call :func:`MPI_Init`. However, older MPI
implementations actually require each process to call :func:`MPI_Init` upon
starting. The easiest way of having this done is to install the mpi4py
[mpi4py]_ package and then specify the ``c.MPI.use`` option in :file:`ipengine_config.py`:

.. sourcecode:: python

    c.MPI.use = 'mpi4py'

Unfortunately, even this won't work for some MPI implementations. If you are
having problems with this, you will likely have to use a custom Python
executable that itself calls :func:`MPI_Init` at the appropriate time.
Fortunately, mpi4py comes with such a custom Python executable that is easy to
install and use. However, this custom Python executable approach will not work
with :command:`ipcluster` currently.

More details on using MPI with IPython can be found :ref:`here <parallelmpi>`.


Using :command:`ipcluster` in PBS mode
---------------------------------------

The PBS mode uses the Portable Batch System (PBS) to start the engines. 

As usual, we will start by creating a fresh profile::

    $ ipython profile create --parallel --profile=pbs

And in :file:`ipcluster_config.py`, we will select the PBS launchers for the controller
and engines:

.. sourcecode:: python

    c.IPClusterStart.controller_launcher = \
        'IPython.parallel.apps.launcher.PBSControllerLauncher'
    c.IPClusterEngines.engine_launcher = \
        'IPython.parallel.apps.launcher.PBSEngineSetLauncher'

.. note::

    Note that the configurable is IPClusterEngines for the engine launcher, and
    IPClusterStart for the controller launcher. This is because the start command is a
    subclass of the engine command, adding a controller launcher. Since it is a subclass,
    any configuration made in IPClusterEngines is inherited by IPClusterStart unless it is
    overridden.

IPython does provide simple default batch templates for PBS and SGE, but you may need
to specify your own. Here is a sample PBS script template:

.. sourcecode:: bash

    #PBS -N ipython
    #PBS -j oe
    #PBS -l walltime=00:10:00
    #PBS -l nodes={n/4}:ppn=4
    #PBS -q {queue}

    cd $PBS_O_WORKDIR
    export PATH=$HOME/usr/local/bin
    export PYTHONPATH=$HOME/usr/local/lib/python2.7/site-packages
    /usr/local/bin/mpiexec -n {n} ipengine --profile_dir={profile_dir}

There are a few important points about this template:

1. This template will be rendered at runtime using IPython's :class:`EvalFormatter`.
   This is simply a subclass of :class:`string.Formatter` that allows simple expressions
   on keys.

2. Instead of putting in the actual number of engines, use the notation
   ``{n}`` to indicate the number of engines to be started. You can also use
   expressions like ``{n/4}`` in the template to indicate the number of nodes.
   There will always be ``{n}`` and ``{profile_dir}`` variables passed to the formatter.
   These allow the batch system to know how many engines, and where the configuration
   files reside. The same is true for the batch queue, with the template variable
   ``{queue}``.

3. Any options to :command:`ipengine` can be given in the batch script
   template, or in :file:`ipengine_config.py`.

4. Depending on the configuration of you system, you may have to set
   environment variables in the script template.

The controller template should be similar, but simpler:

.. sourcecode:: bash

    #PBS -N ipython
    #PBS -j oe
    #PBS -l walltime=00:10:00
    #PBS -l nodes=1:ppn=4
    #PBS -q {queue}

    cd $PBS_O_WORKDIR
    export PATH=$HOME/usr/local/bin
    export PYTHONPATH=$HOME/usr/local/lib/python2.7/site-packages
    ipcontroller --profile_dir={profile_dir}


Once you have created these scripts, save them with names like
:file:`pbs.engine.template`. Now you can load them into the :file:`ipcluster_config` with:

.. sourcecode:: python

    c.PBSEngineSetLauncher.batch_template_file = "pbs.engine.template"
    
    c.PBSControllerLauncher.batch_template_file = "pbs.controller.template"
        

Alternately, you can just define the templates as strings inside :file:`ipcluster_config`.

Whether you are using your own templates or our defaults, the extra configurables available are
the number of engines to launch (``{n}``, and the batch system queue to which the jobs are to be
submitted (``{queue}``)). These are configurables, and can be specified in
:file:`ipcluster_config`:

.. sourcecode:: python

    c.PBSLauncher.queue = 'veryshort.q'
    c.IPClusterEngines.n = 64

Note that assuming you are running PBS on a multi-node cluster, the Controller's default behavior
of listening only on localhost is likely too restrictive.  In this case, also assuming the
nodes are safely behind a firewall, you can simply instruct the Controller to listen for
connections on all its interfaces, by adding in :file:`ipcontroller_config`:

.. sourcecode:: python

    c.HubFactory.ip = '*'

You can now run the cluster with::

    $ ipcluster start --profile=pbs --n=128

Additional configuration options can be found in the PBS section of :file:`ipcluster_config`.

.. note::

    Due to the flexibility of configuration, the PBS launchers work with simple changes
    to the template for other :command:`qsub`-using systems, such as Sun Grid Engine,
    and with further configuration in similar batch systems like Condor.


Using :command:`ipcluster` in SSH mode
---------------------------------------


The SSH mode uses :command:`ssh` to execute :command:`ipengine` on remote
nodes and :command:`ipcontroller` can be run remotely as well, or on localhost.

.. note::

    When using this mode it highly recommended that you have set up SSH keys
    and are using ssh-agent [SSH]_ for password-less logins.

As usual, we start by creating a clean profile::

    $ ipython profile create --parallel --profile=ssh

To use this mode, select the SSH launchers in :file:`ipcluster_config.py`:

.. sourcecode:: python

    c.IPClusterEngines.engine_launcher = \
        'IPython.parallel.apps.launcher.SSHEngineSetLauncher'
    # and if the Controller is also to be remote:
    c.IPClusterStart.controller_launcher = \
        'IPython.parallel.apps.launcher.SSHControllerLauncher'
    

The controller's remote location and configuration can be specified:

.. sourcecode:: python

    # Set the user and hostname for the controller
    # c.SSHControllerLauncher.hostname = 'controller.example.com'
    # c.SSHControllerLauncher.user = os.environ.get('USER','username')

    # Set the arguments to be passed to ipcontroller
    # note that remotely launched ipcontroller will not get the contents of 
    # the local ipcontroller_config.py unless it resides on the *remote host*
    # in the location specified by the `profile_dir` argument.
    # c.SSHControllerLauncher.program_args = ['--reuse', '--ip=*', '--profile_dir=/path/to/cd']

.. note::

    SSH mode does not do any file movement, so you will need to distribute configuration
    files manually.  To aid in this, the `reuse_files` flag defaults to True for ssh-launched
    Controllers, so you will only need to do this once, unless you override this flag back
    to False.

Engines are specified in a dictionary, by hostname and the number of engines to be run
on that host.

.. sourcecode:: python

    c.SSHEngineSetLauncher.engines = { 'host1.example.com' : 2,
                'host2.example.com' : 5,
                'host3.example.com' : (1, ['--profile_dir=/home/different/location']),
                'host4.example.com' : 8 }

* The `engines` dict, where the keys are the host we want to run engines on and
  the value is the number of engines to run on that host.
* on host3, the value is a tuple, where the number of engines is first, and the arguments
  to be passed to :command:`ipengine` are the second element.

For engines without explicitly specified arguments, the default arguments are set in
a single location:

.. sourcecode:: python

    c.SSHEngineSetLauncher.engine_args = ['--profile_dir=/path/to/profile_ssh']

Current limitations of the SSH mode of :command:`ipcluster` are:

* Untested on Windows. Would require a working :command:`ssh` on Windows.
  Also, we are using shell scripts to setup and execute commands on remote
  hosts.
* No file movement - This is a regression from 0.10, which moved connection files
  around with scp. This will be improved, but not before 0.11 release.

Using the :command:`ipcontroller` and :command:`ipengine` commands
====================================================================

It is also possible to use the :command:`ipcontroller` and :command:`ipengine`
commands to start your controller and engines. This approach gives you full
control over all aspects of the startup process.

Starting the controller and engine on your local machine
--------------------------------------------------------

To use :command:`ipcontroller` and :command:`ipengine` to start things on your
local machine, do the following.

First start the controller::

    $ ipcontroller
    
Next, start however many instances of the engine you want using (repeatedly)
the command::

    $ ipengine

The engines should start and automatically connect to the controller using the
JSON files in :file:`~/.ipython/profile_default/security`. You are now ready to use the
controller and engines from IPython.

.. warning:: 
    
    The order of the above operations may be important.  You *must*
    start the controller before the engines, unless you are reusing connection
    information (via ``--reuse``), in which case ordering is not important.

.. note::

    On some platforms (OS X), to put the controller and engine into the
    background you may need to give these commands in the form ``(ipcontroller
    &)`` and ``(ipengine &)`` (with the parentheses) for them to work
    properly.

Starting the controller and engines on different hosts
------------------------------------------------------

When the controller and engines are running on different hosts, things are
slightly more complicated, but the underlying ideas are the same:

1. Start the controller on a host using :command:`ipcontroller`. The controller must be
   instructed to listen on an interface visible to the engine machines, via the ``ip``
   command-line argument or ``HubFactory.ip`` in :file:`ipcontroller_config.py`.
2. Copy :file:`ipcontroller-engine.json` from :file:`~/.ipython/profile_<name>/security` on
   the controller's host to the host where the engines will run.
3. Use :command:`ipengine` on the engine's hosts to start the engines.

The only thing you have to be careful of is to tell :command:`ipengine` where
the :file:`ipcontroller-engine.json` file is located. There are two ways you
can do this:

* Put :file:`ipcontroller-engine.json` in the :file:`~/.ipython/profile_<name>/security`
  directory on the engine's host, where it will be found automatically.
* Call :command:`ipengine` with the ``--file=full_path_to_the_file``
  flag.

The ``file`` flag works like this::

    $ ipengine --file=/path/to/my/ipcontroller-engine.json

.. note::
    
    If the controller's and engine's hosts all have a shared file system  
    (:file:`~/.ipython/profile_<name>/security` is the same on all of them), then things
    will just work!

Make JSON files persistent
--------------------------

At fist glance it may seem that that managing the JSON files is a bit
annoying. Going back to the house and key analogy, copying the JSON around
each time you start the controller is like having to make a new key every time
you want to unlock the door and enter your house. As with your house, you want
to be able to create the key (or JSON file) once, and then simply use it at
any point in the future.

To do this, the only thing you have to do is specify the `--reuse` flag, so that
the connection information in the JSON files remains accurate::

    $ ipcontroller --reuse

Then, just copy the JSON files over the first time and you are set. You can
start and stop the controller and engines any many times as you want in the
future, just make sure to tell the controller to reuse the file.

.. note::

    You may ask the question: what ports does the controller listen on if you
    don't tell is to use specific ones? The default is to use high random port
    numbers. We do this for two reasons: i) to increase security through
    obscurity and ii) to multiple controllers on a given host to start and
    automatically use different ports.

Log files
---------

All of the components of IPython have log files associated with them.
These log files can be extremely useful in debugging problems with
IPython and can be found in the directory :file:`~/.ipython/profile_<name>/log`.
Sending the log files to us will often help us to debug any problems.


Configuring `ipcontroller`
---------------------------

The IPython Controller takes its configuration from the file :file:`ipcontroller_config.py`
in the active profile directory.

Ports and addresses
*******************

In many cases, you will want to configure the Controller's network identity.  By default,
the Controller listens only on loopback, which is the most secure but often impractical.
To instruct the controller to listen on a specific interface, you can set the 
:attr:`HubFactory.ip` trait.  To listen on all interfaces, simply specify:

.. sourcecode:: python

    c.HubFactory.ip = '*'

When connecting to a Controller that is listening on loopback or behind a firewall, it may
be necessary to specify an SSH server to use for tunnels, and the external IP of the
Controller. If you specified that the HubFactory listen on loopback, or all interfaces,
then IPython will try to guess the external IP. If you are on a system with VM network
devices, or many interfaces, this guess may be incorrect. In these cases, you will want
to specify the 'location' of the Controller. This is the IP of the machine the Controller
is on, as seen by the clients, engines, or the SSH server used to tunnel connections.

For example, to set up a cluster with a Controller on a work node, using ssh tunnels
through the login node, an example :file:`ipcontroller_config.py` might contain:

.. sourcecode:: python

    # allow connections on all interfaces from engines
    # engines on the same node will use loopback, while engines
    # from other nodes will use an external IP
    c.HubFactory.ip = '*'
    
    # you typically only need to specify the location when there are extra
    # interfaces that may not be visible to peer nodes (e.g. VM interfaces)
    c.HubFactory.location = '10.0.1.5'
    # or to get an automatic value, try this:
    import socket
    ex_ip = socket.gethostbyname_ex(socket.gethostname())[-1][0]
    c.HubFactory.location = ex_ip
    
    # now instruct clients to use the login node for SSH tunnels:
    c.HubFactory.ssh_server = 'login.mycluster.net'

After doing this, your :file:`ipcontroller-client.json` file will look something like this:

.. this can be Python, despite the fact that it's actually JSON, because it's
.. still valid Python

.. sourcecode:: python

    {
      "url":"tcp:\/\/*:43447",
      "exec_key":"9c7779e4-d08a-4c3b-ba8e-db1f80b562c1",
      "ssh":"login.mycluster.net",
      "location":"10.0.1.5"
    }

Then this file will be all you need for a client to connect to the controller, tunneling
SSH connections through login.mycluster.net.

Database Backend
****************

The Hub stores all messages and results passed between Clients and Engines.
For large and/or long-running clusters, it would be unreasonable to keep all
of this information in memory. For this reason, we have two database backends:
[MongoDB]_ via PyMongo_, and SQLite with the stdlib :py:mod:`sqlite`.

MongoDB is our design target, and the dict-like model it uses has driven our design. As far
as we are concerned, BSON can be considered essentially the same as JSON, adding support
for binary data and datetime objects, and any new database backend must support the same
data types.

.. seealso::

    MongoDB `BSON doc <http://www.mongodb.org/display/DOCS/BSON>`_

To use one of these backends, you must set the :attr:`HubFactory.db_class` trait:

.. sourcecode:: python

    # for a simple dict-based in-memory implementation, use dictdb
    # This is the default and the fastest, since it doesn't involve the filesystem
    c.HubFactory.db_class = 'IPython.parallel.controller.dictdb.DictDB'
    
    # To use MongoDB:
    c.HubFactory.db_class = 'IPython.parallel.controller.mongodb.MongoDB'
    
    # and SQLite:
    c.HubFactory.db_class = 'IPython.parallel.controller.sqlitedb.SQLiteDB'

When using the proper databases, you can actually allow for tasks to persist from
one session to the next by specifying the MongoDB database or SQLite table in
which tasks are to be stored.  The default is to use a table named for the Hub's Session,
which is a UUID, and thus different every time.

.. sourcecode:: python

    # To keep persistant task history in MongoDB:
    c.MongoDB.database = 'tasks'
    
    # and in SQLite:
    c.SQLiteDB.table = 'tasks'


Since MongoDB servers can be running remotely or configured to listen on a particular port,
you can specify any arguments you may need to the PyMongo `Connection 
<http://api.mongodb.org/python/1.9/api/pymongo/connection.html#pymongo.connection.Connection>`_:

.. sourcecode:: python

    # positional args to pymongo.Connection
    c.MongoDB.connection_args = []
    
    # keyword args to pymongo.Connection
    c.MongoDB.connection_kwargs = {}

.. _MongoDB: http://www.mongodb.org
.. _PyMongo: http://api.mongodb.org/python/1.9/

Configuring `ipengine`
-----------------------

The IPython Engine takes its configuration from the file :file:`ipengine_config.py`

The Engine itself also has some amount of configuration. Most of this
has to do with initializing MPI or connecting to the controller.

To instruct the Engine to initialize with an MPI environment set up by
mpi4py, add:

.. sourcecode:: python

    c.MPI.use = 'mpi4py'

In this case, the Engine will use our default mpi4py init script to set up
the MPI environment prior to exection.  We have default init scripts for
mpi4py and pytrilinos.  If you want to specify your own code to be run
at the beginning, specify `c.MPI.init_script`.

You can also specify a file or python command to be run at startup of the
Engine:

.. sourcecode:: python

    c.IPEngineApp.startup_script = u'/path/to/my/startup.py'
    
    c.IPEngineApp.startup_command = 'import numpy, scipy, mpi4py'

These commands/files will be run again, after each

It's also useful on systems with shared filesystems to run the engines
in some scratch directory.  This can be set with:

.. sourcecode:: python

    c.IPEngineApp.work_dir = u'/path/to/scratch/'



.. [MongoDB] MongoDB database http://www.mongodb.org

.. [PBS] Portable Batch System http://www.openpbs.org

.. [SSH] SSH-Agent http://en.wikipedia.org/wiki/ssh-agent