rszper commented on code in PR #25947: URL: https://github.com/apache/beam/pull/25947#discussion_r1146891464
########## website/www/site/content/en/documentation/sdks/python-machine-learning.md: ########## @@ -243,6 +243,17 @@ For more information, see the [`PredictionResult` documentation](https://github. For detailed instructions explaining how to build and run a Python pipeline that uses ML models, see the [Example RunInference API pipelines](https://github.com/apache/beam/tree/master/sdks/python/apache_beam/examples/inference) on GitHub. +## Slowly updating side input pattern to update models used by ModelHandler Review Comment: Is slowly updating a type of side input pattern? If so, I think it should be hyphenated: Slowly-updating side input pattern ########## website/www/site/content/en/documentation/sdks/python-machine-learning.md: ########## @@ -243,6 +243,17 @@ For more information, see the [`PredictionResult` documentation](https://github. For detailed instructions explaining how to build and run a Python pipeline that uses ML models, see the [Example RunInference API pipelines](https://github.com/apache/beam/tree/master/sdks/python/apache_beam/examples/inference) on GitHub. +## Slowly updating side input pattern to update models used by ModelHandler +The RunInference PTransform will accept a side input of [ModelMetadata](https://beam.apache.org/releases/pydoc/current/apache_beam.ml.inference.base.html#apache_beam.ml.inference.base.ModelMetadata), which is a `NamedTuple` containing the `model_id` and `model_name`, +to update the models used by the ModelHandler in the RunInference PTransform without the need of stopping the pipeline for the model updates. + * `model_id`: Unique identifier for the model. This can be a file path or a URL where the model can be accessed. It is used to load the model for inference. + * `model_name`: Human-readable name for the model. This can be used to identify the model in the metrics generated by the RunInference transform. + +**Note**: The side input PCollection must follow [AsSingleton](https://beam.apache.org/releases/pydoc/current/apache_beam.pvalue.html?highlight=assingleton#apache_beam.pvalue.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 Review Comment: ```suggestion **Note**: If the main PCollection emits inputs and a side input has yet to receive inputs, the main PCollection is buffered until there is ``` ########## website/www/site/content/en/documentation/sdks/python-machine-learning.md: ########## @@ -243,6 +243,17 @@ For more information, see the [`PredictionResult` documentation](https://github. For detailed instructions explaining how to build and run a Python pipeline that uses ML models, see the [Example RunInference API pipelines](https://github.com/apache/beam/tree/master/sdks/python/apache_beam/examples/inference) on GitHub. +## Slowly updating side input pattern to update models used by ModelHandler +The RunInference PTransform will accept a side input of [ModelMetadata](https://beam.apache.org/releases/pydoc/current/apache_beam.ml.inference.base.html#apache_beam.ml.inference.base.ModelMetadata), which is a `NamedTuple` containing the `model_id` and `model_name`, +to update the models used by the ModelHandler in the RunInference PTransform without the need of stopping the pipeline for the model updates. + * `model_id`: Unique identifier for the model. This can be a file path or a URL where the model can be accessed. It is used to load the model for inference. + * `model_name`: Human-readable name for the model. This can be used to identify the model in the metrics generated by the RunInference transform. + +**Note**: The side input PCollection must follow [AsSingleton](https://beam.apache.org/releases/pydoc/current/apache_beam.pvalue.html?highlight=assingleton#apache_beam.pvalue.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 Review Comment: We generally don't want two notes in a row. Does this content need to be in a note? Can it just be text in the file? It both sections must be notes, could they be combined into one note? ########## website/www/site/content/en/documentation/sdks/python-machine-learning.md: ########## @@ -243,6 +243,17 @@ For more information, see the [`PredictionResult` documentation](https://github. For detailed instructions explaining how to build and run a Python pipeline that uses ML models, see the [Example RunInference API pipelines](https://github.com/apache/beam/tree/master/sdks/python/apache_beam/examples/inference) on GitHub. +## Slowly updating side input pattern to update models used by ModelHandler +The RunInference PTransform will accept a side input of [ModelMetadata](https://beam.apache.org/releases/pydoc/current/apache_beam.ml.inference.base.html#apache_beam.ml.inference.base.ModelMetadata), which is a `NamedTuple` containing the `model_id` and `model_name`, +to update the models used by the ModelHandler in the RunInference PTransform without the need of stopping the pipeline for the model updates. + * `model_id`: Unique identifier for the model. This can be a file path or a URL where the model can be accessed. It is used to load the model for inference. + * `model_name`: Human-readable name for the model. This can be used to identify the model in the metrics generated by the RunInference transform. + +**Note**: The side input PCollection must follow [AsSingleton](https://beam.apache.org/releases/pydoc/current/apache_beam.pvalue.html?highlight=assingleton#apache_beam.pvalue.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.. Review Comment: ```suggestion an update to the side input. This could happen with Global windowed side inputs with data driven triggers such as `AfterCount`, `AfterProcessingTime`. Until the side input is updated, emit the default or initial model ID that is used to pass the respective `ModelHandler` as a side input. ``` ########## website/www/site/content/en/documentation/sdks/python-machine-learning.md: ########## @@ -243,6 +243,17 @@ For more information, see the [`PredictionResult` documentation](https://github. For detailed instructions explaining how to build and run a Python pipeline that uses ML models, see the [Example RunInference API pipelines](https://github.com/apache/beam/tree/master/sdks/python/apache_beam/examples/inference) on GitHub. +## Slowly updating side input pattern to update models used by ModelHandler +The RunInference PTransform will accept a side input of [ModelMetadata](https://beam.apache.org/releases/pydoc/current/apache_beam.ml.inference.base.html#apache_beam.ml.inference.base.ModelMetadata), which is a `NamedTuple` containing the `model_id` and `model_name`, Review Comment: ```suggestion To update models used by the model handler in the RunInference PTransform without stopping the pipeline, use a [`ModelMetadata`](https://beam.apache.org/releases/pydoc/current/apache_beam.ml.inference.base.html#apache_beam.ml.inference.base.ModelMetadata) side input, which is a `NamedTuple` containing the `model_id` and `model_name`. ``` -- This is an automated message from the Apache Git Service. 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