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Posted to github@beam.apache.org by GitBox <gi...@apache.org> on 2022/09/13 21:01:52 UTC
[GitHub] [beam] liferoad opened a new pull request, #23218: updated the pydoc for running a custom model on Beam
liferoad opened a new pull request, #23218:
URL: https://github.com/apache/beam/pull/23218
* Added some descriptions in pydoc to show how to run the inference using the custom models.
@rezarokni
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[GitHub] [beam] AnandInguva commented on a diff in pull request #23218: updated the pydoc for running a custom model on Beam
Posted by GitBox <gi...@apache.org>.
AnandInguva commented on code in PR #23218:
URL: https://github.com/apache/beam/pull/23218#discussion_r971406025
##########
website/www/site/content/en/documentation/sdks/python-machine-learning.md:
##########
@@ -83,6 +83,14 @@ You need to provide a path to a file that contains the pickled Scikit-learn mode
`model_uri=<path_to_pickled_file>` and `model_file_type: <ModelFileType>`, where you can specify
`ModelFileType.PICKLE` or `ModelFileType.JOBLIB`, depending on how the model was serialized.
+### Use custom models
+
+In fact, the RunInference API is designed flexibly to allow you to use any custom machine learning models. You only need to create your own `ModelHandler` or `KeyedModelHandler` to handle how the ML models are loaded from a location that the pipeline can access and how to use these models to run the inference.
+
+A simple example can be found in [this notebook](https://github.com/apache/beam/blob/master/examples/notebooks/beam-ml/run_custom_inference.ipynb).
+The `load_model` method is created to load the models using a popular `spaCy` package while `run_inference` shows how to run the inference on a batch of examples.
Review Comment:
```suggestion
The `load_model` method shows how to load the model using a popular `spaCy` package while `run_inference` shows how to run the inference on a batch of examples.
```
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[GitHub] [beam] damccorm merged pull request #23218: updated the pydoc for running a custom model on Beam
Posted by GitBox <gi...@apache.org>.
damccorm merged PR #23218:
URL: https://github.com/apache/beam/pull/23218
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[GitHub] [beam] AnandInguva commented on pull request #23218: updated the pydoc for running a custom model on Beam
Posted by GitBox <gi...@apache.org>.
AnandInguva commented on PR #23218:
URL: https://github.com/apache/beam/pull/23218#issuecomment-1249483934
cc @damccorm
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[GitHub] [beam] damccorm commented on a diff in pull request #23218: updated the pydoc for running a custom model on Beam
Posted by GitBox <gi...@apache.org>.
damccorm commented on code in PR #23218:
URL: https://github.com/apache/beam/pull/23218#discussion_r973217823
##########
website/www/site/content/en/documentation/sdks/python-machine-learning.md:
##########
@@ -83,6 +83,14 @@ You need to provide a path to a file that contains the pickled Scikit-learn mode
`model_uri=<path_to_pickled_file>` and `model_file_type: <ModelFileType>`, where you can specify
`ModelFileType.PICKLE` or `ModelFileType.JOBLIB`, depending on how the model was serialized.
+### Use custom models
+
+In fact, the RunInference API is designed flexibly to allow you to use any custom machine learning models. You only need to create your own `ModelHandler` or `KeyedModelHandler` to handle how the ML models are loaded from a location that the pipeline can access and how to use these models to run the inference.
Review Comment:
```suggestion
If you would like to use a model that isn't specified by one of the supported frameworks, the RunInference API is designed flexibly to allow you to use any custom machine learning models.
You only need to create your own `ModelHandler` or `KeyedModelHandler` with logic to load your model and use it to run the inference.
```
Mostly a wording nit, but I'd also like to emphasize that this shouldn't be the default option (the default is using one of the existing model handlers). Ideally over time we will support enough frameworks that creating a custom model handler is no longer a common need at all, but in general we want to point people to our handlers first so that they get any improvements for free.
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[GitHub] [beam] liferoad commented on a diff in pull request #23218: updated the pydoc for running a custom model on Beam
Posted by GitBox <gi...@apache.org>.
liferoad commented on code in PR #23218:
URL: https://github.com/apache/beam/pull/23218#discussion_r973510522
##########
website/www/site/content/en/documentation/sdks/python-machine-learning.md:
##########
@@ -83,6 +83,14 @@ You need to provide a path to a file that contains the pickled Scikit-learn mode
`model_uri=<path_to_pickled_file>` and `model_file_type: <ModelFileType>`, where you can specify
`ModelFileType.PICKLE` or `ModelFileType.JOBLIB`, depending on how the model was serialized.
+### Use custom models
+
+In fact, the RunInference API is designed flexibly to allow you to use any custom machine learning models. You only need to create your own `ModelHandler` or `KeyedModelHandler` to handle how the ML models are loaded from a location that the pipeline can access and how to use these models to run the inference.
Review Comment:
looks good. Thanks for rewording these.
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