tvalentyn commented on code in PR #31052:
URL: https://github.com/apache/beam/pull/31052#discussion_r1575502012
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sdks/python/apache_beam/ml/inference/pytorch_inference.py:
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@@ -234,6 +235,9 @@ def __init__(
memory pressure if you load multiple copies. Given a model that
consumes N memory and a machine with W cores and M memory, you should
set this to True if N*W > M.
+ model_copies: The exact number of models that you would like loaded
+ onto your machine. This can be useful if you exactly know your CPU or
Review Comment:
ah my concern was not an incorrect configuration but cognitive burden for
users: would they be thinking if they should set only one param, or both in
their use case, while in the end it doesn't matter. but now it also seems that
`large_model` becomes redundant as it is equivalent to passing `model_copies =
1`, right?
Possibly except the fact that using model_copies is currently disallowed
with KeyedMH, and large_model might still allow that.
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