Taxonomic memory issue

[thermokarst]

@Chozinentropy

I attempted prior steps with different parameters, but was met by the same error. I think My memory allocation is messed up after attempting a $TMPDIR fix. It is allocated to my 1tb dynamically allocated partition, but that doesn’t seem to be it. I’m not sure of where to allocate the memory.

Let's slow down and take a step back here.

2 days ago you reported this:

@Chozinentropy

qiime feature-classifier classify-sklearn --i-classifier silva-132-99-515-806-nb-classifier.qza --i-reads rep-seqs.qza --o-classification taxonomy.qza
I ran this command and received “Plugin error from feature-classifier: The operation has run out of available memory.”

Okay, so we we initially started with an out-of-memory issue - not too uncommon.

@Chozinentropy

qiime feature-classifier classify-sklearn --i-classifier silva-132-99-nb-classifier.qza --i-reads rep-seqs.qza --o-classification taxonomy.qza --p-n-jobs 1
and
qiime feature-classifier classify-sklearn --i-classifier silva-132-99-nb-classifier.qza --i-reads rep-seqs.qza --o-classification taxonomy.qza --p-reads-per-batch 1000
I still get
Invalid value for “–i-classifier”: ‘silva-132-99-nb-classifier.qza’ is
not a QIIME 2 Artifact (.qza)
Invalid value for “–i-reads”: ‘rep-seqs.qza’ is not a QIIME 2 Artifact
(.qza)

Okay, so this is a completely different error message.

Before we sort out the memory error, let's address the "not a QIIME 2 Artifact" issues.

Can you please run the following, and copy-and-paste the complete error message:

qiime tools validate silva-132-99-nb-classifier.qza

qiime tools validate rep-seqs.qza

In general, you need to provide the entire error message, rather than just cherry-picking what you think might be relevant - more often then not there is a significant amount of context available in the complete error messages, which is why we ask for them.

Keep us posted.

PS - I am about to address the question you have asked about over here: TaxonomicClassifier. One thing I think might present as a confounding issue is that you have mutated your QIIME 2 environment, by installing a random version of scikit-learn in it. If you're using the same env in both of these posts, that might be problematic. I would suggest deleting the env and reinstalling (it should be pretty quick - conda will cache all the packages from the first env you created).