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Posted to github@beam.apache.org by "damccorm (via GitHub)" <gi...@apache.org> on 2023/08/31 14:55:54 UTC
[GitHub] [beam] damccorm opened a new pull request, #28263: Add support for limiting number of models in memory
damccorm opened a new pull request, #28263:
URL: https://github.com/apache/beam/pull/28263
This will allow users to set an upper bound on the number of models in memory to help avoid OOMs. See https://docs.google.com/document/d/1kj3FyWRbJu1KhViX07Z0Gk0MU0842jhYRhI-DMhhcv4/edit#heading=h.iaih85l3wghw for more detail
Part of https://github.com/apache/beam/issues/27628
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[GitHub] [beam] github-actions[bot] commented on pull request #28263: Add support for limiting number of models in memory
Posted by "github-actions[bot] (via GitHub)" <gi...@apache.org>.
github-actions[bot] commented on PR #28263:
URL: https://github.com/apache/beam/pull/28263#issuecomment-1701420346
Stopping reviewer notifications for this pull request: review requested by someone other than the bot, ceding control
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[GitHub] [beam] damccorm commented on pull request #28263: Add support for limiting number of models in memory
Posted by "damccorm (via GitHub)" <gi...@apache.org>.
damccorm commented on PR #28263:
URL: https://github.com/apache/beam/pull/28263#issuecomment-1701418351
R: @riteshghorse
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[GitHub] [beam] damccorm commented on pull request #28263: Add support for limiting number of models in memory
Posted by "damccorm (via GitHub)" <gi...@apache.org>.
damccorm commented on PR #28263:
URL: https://github.com/apache/beam/pull/28263#issuecomment-1701416984
Looks like error in last precommit was due to low memory. Should be solved by moving to GHA (in general, seems like GHA has been less flaky in my recent experience)
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[GitHub] [beam] damccorm commented on a diff in pull request #28263: Add support for limiting number of models in memory
Posted by "damccorm (via GitHub)" <gi...@apache.org>.
damccorm commented on code in PR #28263:
URL: https://github.com/apache/beam/pull/28263#discussion_r1311974702
##########
sdks/python/apache_beam/ml/inference/base.py:
##########
@@ -308,17 +308,13 @@ class _ModelManager:
parameter, if that is set it will only hold that many models in memory at
once before evicting one (using LRU logic).
Review Comment:
Done, thanks!
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[GitHub] [beam] codecov[bot] commented on pull request #28263: Add support for limiting number of models in memory
Posted by "codecov[bot] (via GitHub)" <gi...@apache.org>.
codecov[bot] commented on PR #28263:
URL: https://github.com/apache/beam/pull/28263#issuecomment-1701454463
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```diff
@@ Coverage Diff @@
## master #28263 +/- ##
==========================================
+ Coverage 72.28% 72.37% +0.08%
==========================================
Files 678 679 +1
Lines 99899 100160 +261
==========================================
+ Hits 72215 72493 +278
+ Misses 26117 26100 -17
Partials 1567 1567
```
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[GitHub] [beam] riteshghorse commented on a diff in pull request #28263: Add support for limiting number of models in memory
Posted by "riteshghorse (via GitHub)" <gi...@apache.org>.
riteshghorse commented on code in PR #28263:
URL: https://github.com/apache/beam/pull/28263#discussion_r1311980103
##########
sdks/python/apache_beam/ml/inference/base.py:
##########
@@ -587,6 +588,14 @@ def run_inference(
keys,
self._unkeyed.run_inference(unkeyed_batch, model, inference_args))
+ # The first time a MultiProcessShared ModelManager is used for inference
+ # from this process, we should increment its max model count
+ if self._max_models_per_worker_hint is not None:
+ lock = threading.Lock()
+ if lock.acquire(blocking=False):
Review Comment:
got it, thanks!
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[GitHub] [beam] damccorm commented on pull request #28263: Add support for limiting number of models in memory
Posted by "damccorm (via GitHub)" <gi...@apache.org>.
damccorm commented on PR #28263:
URL: https://github.com/apache/beam/pull/28263#issuecomment-1701523451
Run Python PreCommit
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[GitHub] [beam] riteshghorse commented on a diff in pull request #28263: Add support for limiting number of models in memory
Posted by "riteshghorse (via GitHub)" <gi...@apache.org>.
riteshghorse commented on code in PR #28263:
URL: https://github.com/apache/beam/pull/28263#discussion_r1311970930
##########
sdks/python/apache_beam/ml/inference/base.py:
##########
@@ -308,17 +308,13 @@ class _ModelManager:
parameter, if that is set it will only hold that many models in memory at
once before evicting one (using LRU logic).
Review Comment:
remove doc statement for `max_models`
##########
sdks/python/apache_beam/ml/inference/base.py:
##########
@@ -587,6 +588,14 @@ def run_inference(
keys,
self._unkeyed.run_inference(unkeyed_batch, model, inference_args))
+ # The first time a MultiProcessShared ModelManager is used for inference
+ # from this process, we should increment its max model count
+ if self._max_models_per_worker_hint is not None:
+ lock = threading.Lock()
+ if lock.acquire(blocking=False):
Review Comment:
curious why are we not using a blocking lock?
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[GitHub] [beam] damccorm commented on pull request #28263: Add support for limiting number of models in memory
Posted by "damccorm (via GitHub)" <gi...@apache.org>.
damccorm commented on PR #28263:
URL: https://github.com/apache/beam/pull/28263#issuecomment-1701415983
Run Python_Coverage PreCommit
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[GitHub] [beam] damccorm commented on a diff in pull request #28263: Add support for limiting number of models in memory
Posted by "damccorm (via GitHub)" <gi...@apache.org>.
damccorm commented on code in PR #28263:
URL: https://github.com/apache/beam/pull/28263#discussion_r1311977323
##########
sdks/python/apache_beam/ml/inference/base.py:
##########
@@ -587,6 +588,14 @@ def run_inference(
keys,
self._unkeyed.run_inference(unkeyed_batch, model, inference_args))
+ # The first time a MultiProcessShared ModelManager is used for inference
+ # from this process, we should increment its max model count
+ if self._max_models_per_worker_hint is not None:
+ lock = threading.Lock()
+ if lock.acquire(blocking=False):
Review Comment:
The goal is to run this exactly once per process. So the idea is that in a given process, a single thread will acquire the lock and increment the max models. Then the remaining threads (or the original thread in the future) will try to acquire the lock and fail, so they will not increment the value.
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[GitHub] [beam] damccorm merged pull request #28263: Add support for limiting number of models in memory
Posted by "damccorm (via GitHub)" <gi...@apache.org>.
damccorm merged PR #28263:
URL: https://github.com/apache/beam/pull/28263
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