Hi @Jenna_Shelton, thanks @thermokarst.
I’ve just tried running these classifiers on my MacBook Pro that has identical memory to yours with the 2017.7 build.
I used the rep-seas.qza from the tutorial and the classifiers from the Data resources.
$ qiime feature-classifier classify-sklearn --i-classifier silva-119-99-515-806-nb-classifier.qza --i-reads rep-seqs.qza --o-classification blah.qza
and
$ qiime feature-classifier classify-sklearn --i-classifier silva-119-99-nb-classifier.qza --i-reads rep-seqs.qza --o-classification blah.qza --verbose
both ran to completion (with a few deprecation warnings). They both peaked out at less than 11GB of memory.
The rep-seas.qza that I used only contains 776 sequences. Is yours much larger than that? If so, you could try reducing chunk size further to, say, 776. If you get it running and performance is important you could increase that later.
The only other thing I can think of is that there might be something else on your system that is causing you to run out of memory. This seems unlikely, though, because my machine will happily use up ~40GB of memory (by using swap) before bad things start happening, and before that point you would observe that your machine becomes unresponsive (which is the first of the bad things).
So I guess check the version of qiime 2 that you’re using, try reducing the chunk size again, try a different machine (it wouldn’t have to be a very impressive machine if my laptop can handle it), and let us know how you go.