This documentation is for an old version of IPython. You can find docs for newer versions here.

Overview and getting started


We have various example scripts and notebooks for using IPython.parallel in our examples/Parallel%20Computing directory, or they can be viewed using nbviewer. Some of these are covered in more detail in the examples section.


This section gives an overview of IPython’s sophisticated and powerful architecture for parallel and distributed computing. This architecture abstracts out parallelism in a very general way, which enables IPython to support many different styles of parallelism including:

  • Single program, multiple data (SPMD) parallelism.
  • Multiple program, multiple data (MPMD) parallelism.
  • Message passing using MPI.
  • Task farming.
  • Data parallel.
  • Combinations of these approaches.
  • Custom user defined approaches.

Most importantly, IPython enables all types of parallel applications to be developed, executed, debugged and monitored interactively. Hence, the I in IPython. The following are some example usage cases for IPython:

  • Quickly parallelize algorithms that are embarrassingly parallel using a number of simple approaches. Many simple things can be parallelized interactively in one or two lines of code.
  • Steer traditional MPI applications on a supercomputer from an IPython session on your laptop.
  • Analyze and visualize large datasets (that could be remote and/or distributed) interactively using IPython and tools like matplotlib/TVTK.
  • Develop, test and debug new parallel algorithms (that may use MPI) interactively.
  • Tie together multiple MPI jobs running on different systems into one giant distributed and parallel system.
  • Start a parallel job on your cluster and then have a remote collaborator connect to it and pull back data into their local IPython session for plotting and analysis.
  • Run a set of tasks on a set of CPUs using dynamic load balancing.


At the SciPy 2011 conference in Austin, Min Ragan-Kelley presented a complete 4-hour tutorial on the use of these features, and all the materials for the tutorial are now available online. That tutorial provides an excellent, hands-on oriented complement to the reference documentation presented here.

Architecture overview


The IPython architecture consists of four components:

  • The IPython engine.
  • The IPython hub.
  • The IPython schedulers.
  • The controller client.

These components live in the IPython.parallel package and are installed with IPython. They do, however, have additional dependencies that must be installed. For more information, see our installation documentation.

IPython engine

The IPython engine is a Python instance that takes Python commands over a network connection. Eventually, the IPython engine will be a full IPython interpreter, but for now, it is a regular Python interpreter. The engine can also handle incoming and outgoing Python objects sent over a network connection. When multiple engines are started, parallel and distributed computing becomes possible. An important feature of an IPython engine is that it blocks while user code is being executed. Read on for how the IPython controller solves this problem to expose a clean asynchronous API to the user.

IPython controller

The IPython controller processes provide an interface for working with a set of engines. At a general level, the controller is a collection of processes to which IPython engines and clients can connect. The controller is composed of a Hub and a collection of Schedulers. These Schedulers are typically run in separate processes but on the same machine as the Hub, but can be run anywhere from local threads or on remote machines.

The controller also provides a single point of contact for users who wish to utilize the engines connected to the controller. There are different ways of working with a controller. In IPython, all of these models are implemented via the View.apply() method, after constructing View objects to represent subsets of engines. The two primary models for interacting with engines are:

  • A Direct interface, where engines are addressed explicitly.
  • A LoadBalanced interface, where the Scheduler is trusted with assigning work to appropriate engines.

Advanced users can readily extend the View models to enable other styles of parallelism.


A single controller and set of engines can be used with multiple models simultaneously. This opens the door for lots of interesting things.

The Hub

The center of an IPython cluster is the Hub. This is the process that keeps track of engine connections, schedulers, clients, as well as all task requests and results. The primary role of the Hub is to facilitate queries of the cluster state, and minimize the necessary information required to establish the many connections involved in connecting new clients and engines.


All actions that can be performed on the engine go through a Scheduler. While the engines themselves block when user code is run, the schedulers hide that from the user to provide a fully asynchronous interface to a set of engines.

IPython client and views

There is one primary object, the Client, for connecting to a cluster. For each execution model, there is a corresponding View. These views allow users to interact with a set of engines through the interface. Here are the two default views:

  • The DirectView class for explicit addressing.
  • The LoadBalancedView class for destination-agnostic scheduling.


IPython uses ZeroMQ for networking, which has provided many advantages, but one of the setbacks is its utter lack of security [ZeroMQ]. By default, no IPython connections are encrypted, but open ports only listen on localhost. The only source of security for IPython is via ssh-tunnel. IPython supports both shell (openssh) and paramiko based tunnels for connections. There is a key necessary to submit requests, but due to the lack of encryption, it does not provide significant security if loopback traffic is compromised.

In our architecture, the controller is the only process that listens on network ports, and is thus the main point of vulnerability. The standard model for secure connections is to designate that the controller listen on localhost, and use ssh-tunnels to connect clients and/or engines.

To connect and authenticate to the controller an engine or client needs some information that the controller has stored in a JSON file. Thus, the JSON files need to be copied to a location where the clients and engines can find them. Typically, this is the ~/.ipython/profile_default/security directory on the host where the client/engine is running (which could be a different host than the controller). Once the JSON files are copied over, everything should work fine.

Currently, there are two JSON files that the controller creates:

This JSON file has the information necessary for an engine to connect to a controller.
The client’s connection information. This may not differ from the engine’s, but since the controller may listen on different ports for clients and engines, it is stored separately.

ipcontroller-client.json will look something like this, under default localhost circumstances:


If, however, you are running the controller on a work node on a cluster, you will likely need to use ssh tunnels to connect clients from your laptop to it. You will also probably need to instruct the controller to listen for engines coming from other work nodes on the cluster. An example of ipcontroller-client.json, as created by:

$> ipcontroller --ip=* --ssh=login.mycluster.com

More details of how these JSON files are used are given below.

A detailed description of the security model and its implementation in IPython can be found here.


Even at its most secure, the Controller listens on ports on localhost, and every time you make a tunnel, you open a localhost port on the connecting machine that points to the Controller. If localhost on the Controller’s machine, or the machine of any client or engine, is untrusted, then your Controller is insecure. There is no way around this with ZeroMQ.

Getting Started

To use IPython for parallel computing, you need to start one instance of the controller and one or more instances of the engine. Initially, it is best to simply start a controller and engines on a single host using the ipcluster command. To start a controller and 4 engines on your localhost, just do:

$ ipcluster start -n 4

More details about starting the IPython controller and engines can be found here

Once you have started the IPython controller and one or more engines, you are ready to use the engines to do something useful. To make sure everything is working correctly, try the following commands:

In [1]: from IPython.parallel import Client

In [2]: c = Client()

In [4]: c.ids
Out[4]: set([0, 1, 2, 3])

In [5]: c[:].apply_sync(lambda : "Hello, World")
Out[5]: [ 'Hello, World', 'Hello, World', 'Hello, World', 'Hello, World' ]

When a client is created with no arguments, the client tries to find the corresponding JSON file in the local ~/.ipython/profile_default/security directory. Or if you specified a profile, you can use that with the Client. This should cover most cases:

In [2]: c = Client(profile='myprofile')

If you have put the JSON file in a different location or it has a different name, create the client like this:

In [2]: c = Client('/path/to/my/ipcontroller-client.json')

Remember, a client needs to be able to see the Hub’s ports to connect. So if they are on a different machine, you may need to use an ssh server to tunnel access to that machine, then you would connect to it with:

In [2]: c = Client('/path/to/my/ipcontroller-client.json', sshserver='me@myhub.example.com')

Where ‘myhub.example.com’ is the url or IP address of the machine on which the Hub process is running (or another machine that has direct access to the Hub’s ports).

The SSH server may already be specified in ipcontroller-client.json, if the controller was instructed at its launch time.

You are now ready to learn more about the Direct and LoadBalanced interfaces to the controller.

[ZeroMQ]ZeroMQ. http://www.zeromq.org