automatic batch size for dp test
#1165
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Resolves deepmodeling#1149. We start nbatch * natoms from 1024 (or we can set a different number), and iteratively multiply it by 2 until catching the OOM error. A small issue is that it's a bit slow to catch the TF OOM error. It's a problem of TF and I don't know how to resolve it. Luckily we only need to catch once.
Codecov Report
@@ Coverage Diff @@
## devel #1165 +/- ##
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+ Coverage 75.94% 76.08% +0.13%
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Files 90 91 +1
Lines 7172 7226 +54
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+ Hits 5447 5498 +51
- Misses 1725 1728 +3
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amcadmus
reviewed
Sep 23, 2021
deepmd/utils/batch_size.py
Outdated
| self.maximum_working_batch_size = 0 | ||
| self.minimal_not_working_batch_size = 2**31 | ||
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| def execuate(self, callable: Callable, start_index: int, natoms: int) -> Tuple[int, tuple]: |
amcadmus
reviewed
Sep 23, 2021
amcadmus
approved these changes
Sep 24, 2021
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Resolves #1149.
We start nbatch * natoms from 1024 (or we can set a different number), and iteratively multiply it by 2 until catching the OOM error.
A small issue is that it's a bit slow to catch the TF OOM error. It's a problem of TF and I don't know how to resolve it. Luckily we only need to catch once.