Batch processing in training of NN ensemble - base project suggest calls

[juhoinkinen]

This PR experiments with implementing batched suggest calls for the base projects in NN ensemble backend.

Unfortunately there is no notable performance gain in real use, at least with MLLM, fastText, and Omikuji base projects (as in YSO projects of Finto AI), but actually a performance regression. Performance gain is seen when using only Omikuji as the base project, which is the only one of the backends in Finto AI YSO base models having the batch suggest method implemented.

Below results are from for runs at kj-kk using 16 jobs training on corpora/fulltext-train/fi/*/.

MLLM, fastText, and Omikuji base projects

1000 docs, 1 epoch

	user time	wall time	max rss
before (master)	1268.63	2:25.07	14863072
after (PR)	1260.37	2:24.00	14791464

2000 docs, 10 epochs

	user time	wall time	max rss
before (master)	4205.64	4:41.01	15718876
after (PR)	4152.09	4:58.89	15712064

Omikuji base project only

2000 docs, 1 epoch

	user time	wall time	max rss
before (master)	511.38	1:23.13	7827672
after (PR)	336.72	1:13.68	7612384

[codecov]

Codecov Report

Patch coverage: 100.00% and no project coverage change

Comparison is base (f280342) 99.57% compared to head (38c6784) 99.57%.

Additional details and impacted files

@@           Coverage Diff           @@
##           master     #676   +/-   ##
=======================================
  Coverage   99.57%   99.57%           
=======================================
  Files          87       87           
  Lines        6157     6164    +7     
=======================================
+ Hits         6131     6138    +7     
  Misses         26       26           

Impacted Files	Coverage Δ
annif/parallel.py	100.00% <ø> (ø)
annif/backend/nn_ensemble.py	100.00% <100.00%> (ø)
tests/test_backend_nn_ensemble.py	100.00% <100.00%> (ø)

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[sonarcloud]

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0 Bugs
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1 Code Smell

No Coverage information
0.0% Duplication