codecov[bot]
commented
1 year ago 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
```diff @@ 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](https://codecov.io/gh/NatLibFi/Annif/pull/676?src=pr&el=tree&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=NatLibFi) | Coverage Δ | | |---|---|---| | [annif/parallel.py](https://codecov.io/gh/NatLibFi/Annif/pull/676?src=pr&el=tree&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=NatLibFi#diff-YW5uaWYvcGFyYWxsZWwucHk=) | `100.00% <ø> (ø)` | | | [annif/backend/nn\_ensemble.py](https://codecov.io/gh/NatLibFi/Annif/pull/676?src=pr&el=tree&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=NatLibFi#diff-YW5uaWYvYmFja2VuZC9ubl9lbnNlbWJsZS5weQ==) | `100.00% <100.00%> (ø)` | | | [tests/test\_backend\_nn\_ensemble.py](https://codecov.io/gh/NatLibFi/Annif/pull/676?src=pr&el=tree&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=NatLibFi#diff-dGVzdHMvdGVzdF9iYWNrZW5kX25uX2Vuc2VtYmxlLnB5) | `100.00% <100.00%> (ø)` | | Help us with your feedback. Take ten seconds to tell us [how you rate us](https://about.codecov.io/nps?utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=NatLibFi). Have a feature suggestion? [Share it here.](https://app.codecov.io/gh/feedback/?utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=NatLibFi):umbrella: View full report at Codecov.
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sonarcloud[bot]
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
2000 docs, 10 epochs
Omikuji base project only
2000 docs, 1 epoch