damccorm commented on code in PR #31052:
URL: https://github.com/apache/beam/pull/31052#discussion_r1576181499
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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:
> but now it also seems that large_model becomes redundant as it is
equivalent to passing model_copies = 1, right?
That's right, though I think long term I would like for us to do smart
things here (e.g. `large_model` becomes "pack as many models as you can fit).
There's some conversation on this general idea in the design doc
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