Tutorial¶

  • Overview and getting started
    • Examples
    • Introduction
    • Architecture overview
    • Getting Started
  • Starting the IPython controller and engines
    • General considerations
    • Managing Clusters
    • Configuring an IPython cluster
  • IPython’s Direct interface
    • Starting the IPython controller and engines
    • Creating a DirectView
    • 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
    • Creating a LoadBalancedView
    • Quick and easy parallelism
    • Dependencies
    • Retries and Resubmit
    • Schedulers
    • More details
  • The AsyncResult object
    • Beyond stdlib AsyncResult and Future
    • Metadata
    • Map results are iterable!
  • Parallel examples
    • 150 million digits of pi
    • Conclusion

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Navigation

  • Tutorial
    • Overview and getting started
    • Starting the IPython controller and engines
    • IPython’s Direct interface
    • Parallel Magic Commands
    • The IPython task interface
    • The AsyncResult object
    • Parallel examples
  • Reference
  • examples
  • Changelog
  • API Reference

Related Topics

  • Documentation overview
    • Previous: Using IPython for parallel computing
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