ipyparallel
Changes in IPython Parallel
6.0.2
6.0.1
6.0
5.2
5.1
5.0
4.1
4.0
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
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 ipyparallel
Processes
Creating a Client
Apply
MultiEngine to DirectView
Task to LoadBalancedView
Messaging for Parallel Computing
The Controller
The Hub
Schedulers
Control Messages
Implementation
Connection Diagrams of The IPython ZMQ Cluster
All Connections
ipyparallel
Classes
Decorators
Exceptions
ipyparallel
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6.0.2
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6.0.0
5.2.0
5.1.1
5.1.0
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5.0.0
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