IPython
Introduction
Overview
Enhanced interactive Python shell
Decoupled two-process model
Interactive parallel computing
What’s new in IPython
Development version
1.0 Series
Issues closed in the 1.0 development cycle
0.13 Series
Issues closed in the 0.13 development cycle
0.12 Series
Issues closed in the 0.12 development cycle
0.11 Series
Issues closed in the 0.11 development cycle
0.10 series
0.9 series
0.8 series
Installation
Quickstart
Overview
Installing IPython itself
Basic optional dependencies
Dependencies for IPython.parallel (parallel computing)
Dependencies for IPython.kernel.zmq
Dependencies for the IPython QT console
Dependencies for the IPython HTML notebook
Dependencies for nbconvert (converting notebooks to various formats)
Using IPython for interactive work
Introducing IPython
IPython Tips & Tricks
IPython reference
IPython as a system shell
A Qt Console for IPython
The IPython Notebook
Converting notebooks to other formats
Running a notebook server
Using IPython for parallel computing
Overview and getting started
Starting the IPython controller and engines
IPython’s Direct interface
Parallel Magic Commands
The IPython task interface
The AsyncResult object
Using MPI with IPython
IPython’s Task Database
Security details of IPython
Getting started with Windows HPC Server 2008
Parallel examples
DAG Dependencies
Details of Parallel Computing with IPython
Transitioning from IPython.kernel to IPython.parallel
Configuration and customization
Overview of the IPython configuration system
IPython extensions
Configuring the
ipython
command line application
Integrating your objects with IPython
Editor configuration
Custom input transformation
Outdated configuration information that might still be useful
IPython developer’s guide
Working with IPython source code
Messaging in IPython
Messaging for Parallel Computing
Connection Diagrams of The IPython ZMQ Cluster
The IPython API
About IPython
Credits
History
License and Copyright
IPython
Docs
»
Using IPython for parallel computing
Edit on GitHub
Using IPython for parallel computing
ΒΆ
Overview and getting started
Examples
Introduction
Architecture overview
Getting Started
Starting the IPython controller and engines
General considerations
Using
ipcluster
Configuring an IPython cluster
IPython on EC2 with StarCluster
Using the
ipcontroller
and
ipengine
commands
IPython’s Direct interface
Starting the IPython controller and engines
Creating a
DirectView
instance
Quick and easy parallelism
Calling Python functions
Moving Python objects around
Other things to look at
Parallel Magic Commands
The Magics
Multiple Active Views
Engines as Kernels
The IPython task interface
Starting the IPython controller and engines
Creating a
LoadBalancedView
instance
Quick and easy parallelism
Dependencies
Retries and Resubmit
Schedulers
More details
The AsyncResult object
Beyond multiprocessing’s AsyncResult
Metadata
Map results are iterable!
Using MPI with IPython
Additional installation requirements
Starting the engines with MPI enabled
Actually using MPI
IPython’s Task Database
Enabling a DB Backend
Using the Task Database
Example Queries
Cost
Security details of IPython
Process and network topology
Application logic
Secure network connections
Specific security vulnerabilities
Other security measures
Summary
Getting started with Windows HPC Server 2008
Introduction
Setting up your Windows cluster
Installation of IPython and its dependencies
Starting an IPython cluster
Performing a simple interactive parallel computation
Parallel examples
150 million digits of pi
Conclusion
DAG Dependencies
Why are DAGs good for task dependencies?
A Sample DAG
Details of Parallel Computing with IPython
Caveats
Running Code
Views
Data Movement
Results
Querying the Hub
Controlling the Engines
Synchronization
Map
Decorators and RemoteFunctions
Dependencies
Transitioning from IPython.kernel to IPython.parallel
Processes
Creating a Client
Apply
MultiEngine to DirectView
Task to LoadBalancedView