AMOOOMA commented on code in PR #40907:
URL: https://github.com/apache/beam/pull/40907#discussion_r4486355539


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sdks/python/apache_beam/ml/inference/autoscaling_metrics.py:
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@@ -0,0 +1,575 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+"""Capacity-based autoscaling signal for model handlers that run a model 
server.
+
+Dataflow's GPU autoscaling reads the process-wide distribution
+``num_loaded_models_<tag>`` in the ``BeamML_ModelManager`` namespace as how
+many requests each worker can handle, and sizes the pool roughly as
+``ceil(backlog / value)``. ``ModelManager`` publishes the number of loaded
+model copies there. A handler that serves inference through an out-of-process
+model server (vLLM, Triton, ...) always has exactly one copy per worker, so it
+publishes an estimate of the worker's capacity under that metric instead.
+
+Nothing here depends on a particular model server. A handler for a model server
+that exposes a completed-requests counter and an in-flight gauge creates a
+:class:`NumLoadedModelsPublisher`, supplying only what it knows: the two metric
+names, how to find the server's port, and the RunInference model identifier::

Review Comment:
   Yep, makes sense, updated to reflect this so we don't confuse others.



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