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Posted to github@beam.apache.org by GitBox <gi...@apache.org> on 2022/11/08 22:45:25 UTC

[GitHub] [beam] AnandInguva opened a new issue, #24042: [Feature Request]: Support Streaming model updates for the RunInference transform

AnandInguva opened a new issue, #24042:
URL: https://github.com/apache/beam/issues/24042

   ### What would you like to happen?
   
   For streaming pipeline in Python, add support for updating the models.
   
   * Allow streaming pipelines to update the model(s) in use without requiring the use of pipeline lifecycle events ( Update / Drain ) .
   
   ### Issue Priority
   
   Priority: 2
   
   ### Issue Component
   
   Component: run-inference


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[GitHub] [beam] AnandInguva commented on issue #24042: [Feature Request]: Support Streaming model updates for the RunInference transform

Posted by "AnandInguva (via GitHub)" <gi...@apache.org>.
AnandInguva commented on issue #24042:
URL: https://github.com/apache/beam/issues/24042#issuecomment-1406794137

   Part 1: Add API that accepts Singleton side input PCollection. 
   Part 2: Use case of the API. Example: `WatchFilePattern` transform with an example. 
   Part 3: DLQ to catch errors.
   
   Also, update documentation with 
   ```
   ## Side Inputs to Update Models
   From Beam 2.45.0, the RunInference PTransform will accept a side input of `ModelMetadata`, which is a `NamedTuple` containing the `model_id` and `model_name`.
     * `model_id`: The model_id is used to load the models. It could be an URI or path to the model.
     * `model_name`: Unique identifier used to append the metrics. This should be short relative to the model_id so that it can be attached to the metrics to identify which model was used to calculate the metrics.
   
   **Note**: The side input PCollection must be compatible with `AsSingleton` view or the pipeline will result in error.
   
   **Note**: If the main PCollection emits inputs and side input has yet to receive inputs, the main PCollection will get buffered until there is
               an update to the side input. This could happen with Global windowed side inputs with data driven triggers such as `AfterCount`, `AfterProcessingTime`. So until there is an update to the side input, emit the default/initial model id that is used to pass the respective `ModelHandler` as side input..
   
   
   ``` 


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[GitHub] [beam] AnandInguva commented on issue #24042: [Feature Request]: Support Streaming model updates for the RunInference transform

Posted by GitBox <gi...@apache.org>.
AnandInguva commented on issue #24042:
URL: https://github.com/apache/beam/issues/24042#issuecomment-1307939489

   .take-issue


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[GitHub] [beam] AnandInguva commented on issue #24042: [Feature Request]: Support Streaming model updates for the RunInference transform

Posted by "AnandInguva (via GitHub)" <gi...@apache.org>.
AnandInguva commented on issue #24042:
URL: https://github.com/apache/beam/issues/24042#issuecomment-1493072574

   .close-issue


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[GitHub] [beam] AnandInguva closed issue #24042: [Feature Request]: Support Streaming model updates for the RunInference transform

Posted by "AnandInguva (via GitHub)" <gi...@apache.org>.
AnandInguva closed issue #24042: [Feature Request]: Support Streaming model updates for the RunInference transform
URL: https://github.com/apache/beam/issues/24042


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