Add model manager that automatically manage model across processes #37113

AMOOOMA · 2025-12-15T21:54:30Z

Added Model Manager as a util class that offers managed access to models, the client can request models without having to worry about managing GPU OOMs.
Also added various tests that checks the functions of all classes.

Classes

GPUMonitor

start(): Begins background memory polling.
stop(): Stops polling.
reset_peak(): Resets peak usage tracking.
get_stats() -> (current, peak, total): Returns memory stats.

ResourceEstimator

is_unknown(model_tag: str) -> bool: Checks if model needs profiling.
get_estimate(model_tag: str, default_mb: float) -> float: Returns memory cost.
set_initial_estimate(model_tag: str, cost: float): Manually sets cost.
add_observation(active_snapshot, peak_memory): Updates cost model via NNLS solver.

ModelManager

acquire_model(tag: str, loader_func: Callable) -> Any: Gets model instance (handles isolation/concurrency).
release_model(tag: str, instance: Any): Returns model to pool.
force_reset(): Clears all models and caches.
shutdown(): Cleans up resources.

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gemini-code-assist · 2025-12-15T21:54:48Z

Summary of Changes

Hello @AMOOOMA, I'm Gemini Code Assist¹! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!

This pull request introduces a sophisticated model management system for Apache Beam's ML inference capabilities. The core "ModelManager" class, supported by "GPUMonitor" and "ResourceEstimator", intelligently handles the lifecycle of machine learning models, particularly on GPU-accelerated environments. It aims to prevent out-of-memory errors by dynamically estimating model memory requirements, isolating unknown models for profiling, and implementing a demand-aware eviction strategy. This system ensures efficient and concurrent execution of diverse ML models within Beam pipelines, optimizing GPU resource utilization and improving overall stability.

Highlights

ModelManager Introduction: A new "ModelManager" class is added to provide managed access to ML models, handling GPU OOMs and optimizing resource usage.
GPUMonitor for Memory Tracking: Implements "GPUMonitor" to continuously poll and track GPU memory usage, including current, peak, and total memory.
ResourceEstimator for Cost Estimation: Introduces "ResourceEstimator" which uses Non-Negative Least Squares (NNLS) to dynamically estimate the memory cost of models and adapt to fluctuating usage.
Intelligent Model Eviction: The "ModelManager" includes an eviction strategy that prioritizes models based on demand, age, and surplus copies to free up GPU memory when needed.
Isolation Mode for Unknown Models: Unknown models are loaded in an isolated environment to accurately profile their memory footprint without affecting other active models.
Comprehensive Testing: New unit tests are added to validate the functionality of "GPUMonitor", "ResourceEstimator", and "ModelManager", covering capacity checks, isolation, concurrent execution, OOM recovery, and eviction logic.

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AMOOOMA · 2025-12-15T21:54:53Z

R: @damccorm

github-actions · 2025-12-15T21:59:57Z

Stopping reviewer notifications for this pull request: review requested by someone other than the bot, ceding control. If you'd like to restart, comment assign set of reviewers

sdks/python/apache_beam/ml/inference/model_manager.py

damccorm · 2026-01-13T20:02:38Z

sdks/python/apache_beam/ml/inference/model_manager.py

+      logger.info("Initial Profile for %s: %s MB", model_tag, cost)
+
+  def add_observation(
+      self, active_snapshot: Dict[str, int], peak_memory: float):


I'm having a little bit of a hard time following this. Is active_snapshot a map of model tags to the number of models loaded for that tag?

Yep! That's correct.

Could we update the log below to be clear on that? Ideally it would format the dict in a human readable way that makes that clear

Yes for sure, updated the print string.

damccorm

I've been looking at it for a while today and I am still having a hard time understanding the full scope of this PR (even with this being the second long look), and it will probably take a few more passes.

For now, added some things that will help me review better, but if there are pieces we can separate out further to make this more reviewable (either by splitting large functions apart or by pulling classes out of the PR), that would be quite helpful.

damccorm · 2026-01-26T22:02:13Z

sdks/python/apache_beam/ml/inference/model_manager.py

+      logger.info("Initial Profile for %s: %s MB", model_tag, cost)
+
+  def add_observation(
+      self, active_snapshot: Dict[str, int], peak_memory: float):


Could we update the log below to be clear on that? Ideally it would format the dict in a human readable way that makes that clear

damccorm · 2026-01-26T22:06:47Z

sdks/python/apache_beam/ml/inference/model_manager.py

+
+    return evicted_something
+
+  def _perform_eviction(self, key, tag, instance, score):


Can we add types to the args here and elsewhere?

damccorm · 2026-01-26T22:07:04Z

sdks/python/apache_beam/ml/inference/model_manager.py

+        del self._models[tag][i]
+        break
+
+    if hasattr(instance, "trackedModelProxy_unsafe_hard_delete"):


Does this ever not exist?

I originally was aiming for this model manager to be versatile and can store other objects too but I guess at this point that ship has sailed. We won't need this check anymore. Updated.

damccorm · 2026-01-26T22:12:11Z

sdks/python/apache_beam/ml/inference/model_manager.py

+      with self._load_lock:
+        logger.info("Loading Model: %s (Unknown: %s)", tag, is_unknown)
+        isolation_baseline_snap, _, _ = self._monitor.get_stats()
+        instance = TrackedModelProxy(loader_func())


What is loader_func here - is this spawning the model in process?

Yep! This depends on what the user pass in which in the RunInference case would be spawning a model in process with the new MultiProcessShared util.

damccorm · 2026-01-26T22:17:26Z

sdks/python/apache_beam/ml/inference/model_manager.py

+    ticket_num = next(self._ticket_counter)
+    my_id = object()
+
+    with self._cv:


Is thread contention going to be an issue here/do we need this? If we are using this model_manager in a multi_process_shared setting, the calls to this (and other functions) should be serialized.

I'm a bit worried that this serialization is going to cause broader perf issues

+1 I was also worried but as a first step this is kept in this way since model inference can take some time. I originally tried splitting up the read and load lock to not have loading models block normal operations to grab stuff from the idle pool but I think there's some race condition that can happen if we do release model and model loading at the same time, the snapshot taken can be broken in some cases. We can probably figure out a smarter way to unblock this perf issue though, I will add a comment and defer it to later for now.

damccorm · 2026-01-26T22:18:00Z

sdks/python/apache_beam/ml/inference/model_manager.py

+
+    with self._cv:
+      # FAST PATH
+      if self._pending_isolation_count == 0 and not self._isolation_mode:


Is self._pending_isolation_count ever non-zero?

Good catch! This is some old code from older iterations. Removed now.

damccorm · 2026-01-26T22:18:18Z

sdks/python/apache_beam/ml/inference/model_manager.py

+    self._total_active_jobs = 0
+    self._pending_reservations = 0.0
+
+    self._isolation_mode = False


Could you add comments describing what these are?

damccorm · 2026-01-26T22:18:56Z

sdks/python/apache_beam/ml/inference/model_manager.py

+  It handles:
+  1. LRU Caching of idle models.
+  2. Resource estimation and admission control (preventing OOM).
+  3. Dynamic eviction of low-priority models when space is needed.


Could you add more info on what makes a model low-priority here?

damccorm · 2026-01-26T22:20:24Z

sdks/python/apache_beam/ml/inference/model_manager.py

+  def all_models(self, tag) -> list[Any]:
+    return self._models[tag]
+
+  def acquire_model(self, tag: str, loader_func: Callable[[], Any]) -> Any:


This function is pretty large and I find it hard to follow; could you try breaking it up into subfunctions?

Yes! Sorry about this giant change I forgot it can be really hard to read if it's for the first time. Iterating it kind of just grow the code larger and larger but I was under the false impression that it's pretty much the same size as the first draft.

I added more comments and split this function into smaller pieces, hopefully that would help. Let me know if there's anything I can help! Thanks!

…code and cleanup some code logics

codecov · 2026-01-27T03:39:55Z

Codecov Report

❌ Patch coverage is 2.09790% with 420 lines in your changes missing coverage. Please review.
✅ Project coverage is 39.97%. Comparing base (3ce2abd) to head (8a856b0).
⚠️ Report is 233 commits behind head on master.

Files with missing lines	Patch %	Lines
...s/python/apache_beam/ml/inference/model_manager.py	2.09%	420 Missing ⚠️

Additional details and impacted files

@@             Coverage Diff              @@
##             master   #37113      +/-   ##
============================================
- Coverage     40.38%   39.97%   -0.42%     
  Complexity     3476     3476              
============================================
  Files          1226     1222       -4     
  Lines        188339   187813     -526     
  Branches       3607     3607              
============================================
- Hits          76058    75074     -984     
- Misses       108883   109341     +458     
  Partials       3398     3398

Flag	Coverage Δ
python	`39.66% <2.09%> (-1.00%)`	⬇️

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…of model instance

damccorm

Didn't get to the tests yet, but overall things generally look good. Left some comments below and I'll ask gemini to review as well

damccorm · 2026-01-29T19:54:54Z

sdks/python/apache_beam/ml/inference/model_manager.py

+      return False
+
+    logger.info("Unknown model %s detected. Flushing GPU.", tag)
+    self._delete_all_models()


Do we need to delete all models, or would it be good enough to just not feed them records for inference for a bit?

Yes, because otherwise we might not be able to fit a copy in. Feel free to ignore this comment

damccorm · 2026-01-29T19:59:59Z

sdks/python/apache_beam/ml/inference/model_manager.py

+    for key, (tag, instance, release_time) in self._idle_lru.items():
+      candidate_demand = demand_map[tag]
+
+      if not am_i_starving and candidate_demand >= my_demand:


Should we weight candidate_demand more heavily than my_demand since we already have a copy in memory? (aka try to be more conservative)

Yes, this is a TODO, feel free to ignore this comment (from live review)

damccorm · 2026-01-30T16:33:54Z

sdks/python/apache_beam/ml/inference/model_manager.py

+        curr,
+        total_model_count,
+        other_idle_count)
+    self._cv.wait(timeout=10.0)


I think the control flow reads more cleanly if we keep this self._cv.wait in acquire_model. As is, it is confusing why we're waiting in should_spawn_model. It is also unexpected for a caller that this might induce a hang, and unclear that they need the cv lock. I'd recommend logging the resource usage, and then logging the wait separately.

It took me a while to figure out why we're waiting (I think basically because resource usage won't change until someone calls notify_all)

Is the timeout here basically in case GPU memory does change without notify being called somehow?

Broadly - could you add comments to functions which expect the caller to hold self._cv?

Yes the timeout is just a safety net for in case we somehow missed the notify call, so it won't be forever stuck waiting and never retry to check usages. Sg! Added some comments.

damccorm · 2026-01-30T16:36:57Z

sdks/python/apache_beam/ml/inference/model_manager.py

+              est_cost = self._estimator.get_estimate(tag)
+              break
+            else:
+              # We waited, need to re-evaluate our turn


When will acquire_model get called? Is it possible to get stuck trying to acquire a model forever, and should we have some concept of max tries before we just fail and throw?

It's possible for it to forever retry, I agree, some sort of max should be enforced and we need to let the user know something is broken. I will add some timeout or max retries count.

damccorm · 2026-01-30T16:45:26Z

sdks/python/apache_beam/ml/inference/model_manager.py

+  def all_models(self, tag) -> list[Any]:
+    return self._models[tag]
+
+  def enter_isolation_mode(self, tag: str, ticket_num: int) -> bool:


Nit: try_enter_isolation_mode might be clearer since its not guaranteed to happen

damccorm · 2026-01-30T16:53:09Z

sdks/python/apache_beam/ml/inference/model_manager.py

+              heapq.heapify(self._wait_queue)
+        self._cv.notify_all()
+
+      if should_spawn:


Can this ever be false at this point? If not, do we need this variable?

You are right, this is used to be inside the loop hence the need for checks but yeah the current implementation won't need it. Fixed now.

damccorm · 2026-01-30T16:55:07Z

sdks/python/apache_beam/ml/inference/model_manager.py

+        # Remove self from wait queue once done
+        if self._wait_queue and self._wait_queue[0][2] is my_id:
+          heapq.heappop(self._wait_queue)
+        else:


When will this condition be false?

If this is just defensive coding, that's fine, but please note that (and probably warn)

Added some comments and warning. This is mostly in case the priority is updated somehow and queue is reshuffled.

damccorm · 2026-01-30T18:18:11Z

sdks/python/apache_beam/ml/inference/model_manager.py

+    for item in self._wait_queue:
+      demand_map[item[3]] += 1
+
+    my_demand = demand_map[requesting_tag]


Should we factor in the number of models loaded when considering demand? If I'm reading this right, if I have 5 copies of model A and 3 requests in the queue, vs 3 copies of model B with 2 requests in the queue, we're more likely to evict model B. That doesn't seem right to me

Yes that would be a good addition, I will add some TODO comment here and we can probably revamp this balancing of demands altogether in another PR.

damccorm · 2026-01-30T18:21:59Z

sdks/python/apache_beam/ml/inference/model_manager.py

+      multi_process_shared.MultiProcessShared(
+          lambda: "N/A", tag=instance).unsafe_hard_delete()
+    if hasattr(instance, 'mock_model_unsafe_hard_delete'):
+      # Call the mock unsafe hard delete method for testing


Do we need this mock or can we just create the multiprocess shared objects and pass in tags in the test?

I tried creating the multiprocess shared objects earlier but mocking the resource usage is tricky. Because we need to manually call free memory usage on the fake monitor and without calling unsafe_delete the other option is editing __del__ but that doesn't seem to be reliable after testing with it.

damccorm · 2026-01-30T18:25:59Z

sdks/python/apache_beam/ml/inference/model_manager.py

+    self._monitor.refresh()
+    self._monitor.reset_peak()
+
+  def _force_reset(self):


Should this be private? How are we planning on this being used?

Yeah I mostly made this for tests, so normal clients should not try to call this.

damccorm · 2026-01-30T18:27:24Z

/gemini review

gemini-code-assist

Code Review

This pull request introduces a sophisticated ModelManager for handling ML models, with a strong focus on GPU memory management. The implementation is well-structured, separating concerns into GPUMonitor, ResourceEstimator, and the ModelManager itself. The logic for concurrency, resource contention, and model eviction is complex but appears robust. The accompanying tests are thorough and cover a wide range of scenarios. My review includes several suggestions to enhance security, code clarity, and efficiency in specific areas.

sdks/python/apache_beam/ml/inference/model_manager.py

sdks/python/apache_beam/ml/inference/model_manager_test.py

Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com>

Add model manager that automatically manage model across processes

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AMOOOMA mentioned this pull request Dec 15, 2025

Add model manager that automatically allocate models and prevents OOMs #37103

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github-actions bot added the python label Dec 15, 2025

damccorm reviewed Jan 13, 2026

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Add pydoc and move gpu detection to start

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AMOOOMA added 2 commits January 27, 2026 03:13

Add comments and helper function to make it easier to understand the …

4f82caa

…code and cleanup some code logics

Add TODO for threading

8a856b0

AMOOOMA added 2 commits January 29, 2026 21:13

Remove tracked model proxy and have model manager store tags instead …

3a200ba

…of model instance

Fix import order

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damccorm reviewed Jan 30, 2026

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AMOOOMA and others added 10 commits January 30, 2026 19:01

Clean up and logs

5c89ca7

Added timeout for waiting too long on model acquire

a5e8ff3

Throw error if timeout

c340399

Add test for timeout and adjust

82d3577

Update sdks/python/apache_beam/ml/inference/model_manager.py

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Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com>

Update sdks/python/apache_beam/ml/inference/model_manager.py

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Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com>

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Update sdks/python/apache_beam/ml/inference/model_manager_test.py

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Update GPU monitor test

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Format

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		return evicted_something

		def _perform_eviction(self, key, tag, instance, score):

Add model manager that automatically manage model across processes #37113

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AMOOOMA commented Dec 15, 2025

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