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clarify runtime expectations #3381

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merged 4 commits into from
Dec 7, 2022
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clarify runtime expectations #3381

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b12d16a
clarify runtime expectations
gojomo Aug 17, 2022
618a279
Merge remote-tracking branch 'upstream/develop' into RaRe-Technologie…
mpenkov Aug 22, 2022
d955a81
update doc files made stale by this PR
mpenkov Dec 7, 2022
248931f
Merge branch 'develop' into gojomo-doc2vec-lee-runtime
mpenkov Dec 7, 2022
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12 changes: 9 additions & 3 deletions docs/src/gallery/tutorials/run_doc2vec_lee.py
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Original file line number Diff line number Diff line change
Expand Up @@ -215,9 +215,15 @@ def read_corpus(fname, tokens_only=False):

###############################################################################
# Next, train the model on the corpus.
# If optimized Gensim (with BLAS library) is being used, this should take no more than 3 seconds.
# If the BLAS library is not being used, this should take no more than 2
# minutes, so use optimized Gensim with BLAS if you value your time.
# In the usual case, where Gensim installation found a BLAS library for optimized
# bulk vector operations, this training on this tiny 300 document, ~60k word corpus
# should take just a few seconds. (More realistic datasets of tens-of-millions
# of words or more take proportionately longer.) If for some reason a BLAS library
# isn't available, training uses a fallback approach that takes 60x-120x longer,
# so even this tiny training will take minutes rather than seconds. (And, in that
# case, you should also notice a warning in the logging letting you know there's
# something worth fixing.) So, be sure your installation uses the BLAS-optimized
# Gensim if you value your time.
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@piskvorky piskvorky Aug 17, 2022 •
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A concrete set of instructions for how to actually perform such check would be helpful here. E.g. through the FAST_VERSION attribute.

I'd also personally drop all the brackets, but I realize that's your writing style :)

#
model.train(train_corpus, total_examples=model.corpus_count, epochs=model.epochs)

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