问题
I'm looking to run a long-running python analysis process on a few Amazon EC2 instances. The code already runs using the python multiprocessing module and can take advantage of all cores on a single machine.
The analysis is completely parellel and each instance does not need to communicate with any of the others. All of the work is "file-based" and each process works on each file indivually ... so I was planning on just mounting the same S3 volume across all of the nodes.
I was wondering if anyone knew of any tutorials (or had any suggestions) for setting up the multiprocessing environment so I can run it on an arbitrary number of compute-instances at the same time.
回答1:
the docs give you a good setup for running multiprocessing on multiple machines. Using s3 is a good way to share files across ec2 instances, but with multiprocessing you can share queues and pass data.
if you can use hadoop for parallel tasks, it is a very good way to extract parallelism across machines, but if you need a lot of IPC then building your own solution with multiprocessing isn't that bad.
just make sure you put your machines in the same security groups :-)
回答2:
I would use dumbo. It is a python wrapper for Hadoop that is compatible with Amazon Elastic MapReduce. Write a little wrapper around your code to integrate with dumbo. Note that you probably need a map-only job with no reduce step.
回答3:
I've been digging into IPython recently, and it looks like it supports parallel processing accross multiple hosts right out of the box:
http://ipython.org/ipython-doc/stable/html/parallel/index.html
来源：https://stackoverflow.com/questions/6457091/python-multiprocessing-between-amazon-cloud-instances