gemini-code-assist[bot] commented on PR #37703: URL: https://github.com/apache/beam/pull/37703#issuecomment-3954302215
## Summary of Changes Hello @AMOOOMA, I'm Gemini Code Assist[^1]! 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 enhances the Apache Beam Model Manager by introducing comprehensive monitoring capabilities. It exports critical operational metrics, specifically memory estimates and the count of loaded models per tag, to the Apache Beam Metrics system. These process-wide metrics provide valuable insights into the model manager's resource utilization and model lifecycle, facilitating better observability and integration with external monitoring tools. ### Highlights * **Process-Wide Gauge Metrics**: Extended the `Metrics.gauge` method to support `process_wide` metrics, allowing gauges to be calculated for the entire process rather than just the current bundle. * **Model Memory Estimate Monitoring**: Integrated `Metrics.gauge` calls into the `ModelManager` to export the memory estimate (in MB) for each model tag as a process-wide metric, updated during initial estimation and subsequent re-estimations. * **Loaded Model Count Monitoring**: Added new methods (`_update_model_count_metric`, `_clear_all_model_metrics`) and integrated `Metrics.gauge` calls to track and export the number of loaded models per tag as a process-wide metric. This count is updated when models are spawned, evicted, or all models are deleted. * **Unit Test Coverage**: Included a new unit test (`test_model_manager_metric_gauge`) to verify the correct functionality and updates of the newly added model manager metrics. 🧠**New Feature in Public Preview:** You can now enable **Memory** to help **Gemini Code Assist** learn from your team's feedback. This makes future code reviews more consistent and personalized to your project's style. **Click [here](https://codeassist.google/code-review/login) to enable Memory in your admin console.** <details> <summary><b>Changelog</b></summary> * **sdks/python/apache_beam/metrics/metric.py** * Added a `process_wide` parameter to the `Metrics.gauge` static method. * Modified the `DelegatingGauge` constructor to accept and pass the `process_wide` argument to `MetricUpdater`. * **sdks/python/apache_beam/ml/inference/model_manager.py** * Imported the `Metrics` module. * Added `Metrics.gauge` calls to `set_initial_estimate` and `_solve` to report `memory_estimate_mb` for each model tag. * Introduced `_update_model_count_metric` to report the number of loaded models per tag. * Introduced `_clear_all_model_metrics` to reset model count metrics when all models are deleted. * Integrated calls to `_update_model_count_metric` within `_perform_eviction` and `_spawn_new_model`. * Integrated a call to `_clear_all_model_metrics` within `_delete_all_models`. * **sdks/python/apache_beam/ml/inference/model_manager_test.py** * Imported `MetricsEnvironment` and `MetricName` for testing metrics. * Added a new test method `test_model_manager_metric_gauge` to validate the accuracy of model manager memory estimate and loaded model count metrics. </details> <details> <summary><b>Activity</b></summary> * No specific human activity (comments, reviews, etc.) has been recorded for this pull request yet. </details> <details> <summary><b>Using Gemini Code Assist</b></summary> <br> The full guide for Gemini Code Assist can be found on our [documentation page](https://developers.google.com/gemini-code-assist/docs/review-github-code), here are some quick tips. <b>Invoking Gemini</b> You can request assistance from Gemini at any point by creating a comment using either `/gemini <command>` or `@gemini-code-assist <command>`. 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