Add logger for anomaly detection #852
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This pull request was exported from Phabricator. Differential Revision: D58564200 |
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June 25, 2024 15:33
Summary: ### This Stack Based on [this RFC](https://docs.google.com/document/d/1K1KQ886dynMRejR0ySH1fctOjS7gxaCS8AB1L_PHxU4/edit?usp=sharing), we are adding a new logger that warns about anomalous values in metrics, and optionally executes a callback function with potential side effects. This could be useful for users to realize sooner that something has gone wrong during training. ### This Diff To provide flexibility when detecting anomalous metric values, instead of assuming and hardcoding a predefined check (like a threshold), let's create an interface that can be overriden to implement custom checks. Differential Revision: D58564201
Summary: ### This Stack Based on [this RFC](https://docs.google.com/document/d/1K1KQ886dynMRejR0ySH1fctOjS7gxaCS8AB1L_PHxU4/edit?usp=sharing), we are adding a new logger that warns about anomalous values in metrics, and optionally executes a callback function with potential side effects. This could be useful for users to realize sooner that something has gone wrong during training. ### This Diff To get started with anomaly detection, let's first define two evaluators: - Threshold is the most intuitive one, and checks that a metric value is within a predefined range. - IsNaN would be useful to catch fast cases where the loss is NaN because of bad inputs. Later on we can implement more interesting evaluators like outliers, changepoint detection, etc. if needed. Differential Revision: D58564199
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This pull request was exported from Phabricator. Differential Revision: D58564200 |
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Summary: Pull Request resolved: pytorch#852 ### This Stack Based on [this RFC](https://docs.google.com/document/d/1K1KQ886dynMRejR0ySH1fctOjS7gxaCS8AB1L_PHxU4/edit?usp=sharing), we are adding a new logger that warns about anomalous values in metrics, and optionally executes a callback function with potential side effects. This could be useful for users to realize sooner that something has gone wrong during training. ### This Diff After implementing the evaluators, let's add the `AnomalyLogger` class that receives some configuration of metrics to check for. If an anomaly is detected, then it will call an optional `on_anomaly_detected` method that can be overriden by the user. Next diffs will add this to our `AIXLogger` and `TensorboardLogger` as a base class. Reviewed By: JKSenthil Differential Revision: D58564200
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Summary: Pull Request resolved: pytorch#852 ### This Stack Based on [this RFC](https://docs.google.com/document/d/1K1KQ886dynMRejR0ySH1fctOjS7gxaCS8AB1L_PHxU4/edit?usp=sharing), we are adding a new logger that warns about anomalous values in metrics, and optionally executes a callback function with potential side effects. This could be useful for users to realize sooner that something has gone wrong during training. ### This Diff After implementing the evaluators, let's add the `AnomalyLogger` class that receives some configuration of metrics to check for. If an anomaly is detected, then it will call an optional `on_anomaly_detected` method that can be overriden by the user. Next diffs will add this to our `AIXLogger` and `TensorboardLogger` as a base class. Reviewed By: JKSenthil Differential Revision: D58564200
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Summary:
This Stack
Based on this RFC, we are adding a new logger that warns about anomalous values in metrics, and optionally executes a callback function with potential side effects. This could be useful for users to realize sooner that something has gone wrong during training.
This Diff
After implementing the evaluators, let's add the
AnomalyLoggerclass that receives some configuration of metrics to check for. If an anomaly is detected, then it will call an optionalon_anomaly_detectedmethod that can be overriden by the user.Next diffs will add this to our
AIXLoggerandTensorboardLoggeras a base class.Reviewed By: JKSenthil
Differential Revision: D58564200