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Posted to github@beam.apache.org by "rszper (via GitHub)" <gi...@apache.org> on 2023/03/23 21:43:13 UTC
[GitHub] [beam] rszper commented on a diff in pull request #25947: Add documentation for the auto model updates
rszper commented on code in PR #25947:
URL: https://github.com/apache/beam/pull/25947#discussion_r1146891464
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website/www/site/content/en/documentation/sdks/python-machine-learning.md:
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@@ -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
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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
```
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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?
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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`.
```
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