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