Python Multiprocess Using Pool Fails On Aws Ubuntu

I have a simple string matching script that tests just fine for multiprocessing with up to 8 Pool workers on my local mac with 4 cores. However, the same script on an AWS c1.xlarge

Solution 1:

The MemoryError means you're running out of system-wide virtual memory. How much virtual memory you have is an abstract thing, based on the actual physical RAM plus swapfile size plus stuff that's paged into memory from other files and stuff that isn't paged anywhere because the OS is being clever and so on.

According to your comments, each process averages 0.75GB of real memory, and 4GB of virtual memory. So, your total VM usage is 32GB.

One common reason for this is that each process might peak at 4GB, but spend almost all of its time using a lot less than that. Python rarely releases memory to the OS; it'll just get paged out.

Anyway, 6GB of real memory is no problem on an 8GB Mac or a 7GB c1.xlarge instance.

And 32GB of VM is no problem on a Mac. A typical OS X system has virtually unlimited VM size—if you actually try to use all of it, it'll start creating more swap space automatically, paging like mad, and slowing your system to a crawl and/or running out of disk space, but that isn't going to affect you in this case.

But 32GB of VM is likely to be a problem on linux. A typical linux system has fixed-size swap, and doesn't let you push the VM beyond what it can handle. (It has a different trick that avoids creating probably-unnecessary pages in the first place… but once you've created the pages, you have to have room for them.) I'm not sure what an xlarge comes configured for, but the swapon tool will tell you how much swap you've got (and how much you're using).

Anyway, the easy solution is to create and enable an extra 32GB swapfile on your xlarge.

However, a better solution would be to reduce your VM use. Often each subprocess is doing a whole lot of setup work that creates intermediate data that's never needed again; you can use multiprocessing to push that setup into different processes that quit as soon as they're done, freeing up the VM. Or maybe you can find a way to do the processing more lazily, to avoid needing all that intermediate data in the first place.