[GitHub] [beam] rszper commented on a diff in pull request #22949: Adding TensorFlow support to the Machine Learning overview page

[GitBox <gi...@apache.org>]

rszper commented on code in PR #22949:
URL: https://github.com/apache/beam/pull/22949#discussion_r961862483


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website/www/site/content/en/documentation/sdks/python-machine-learning.md:
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@@ -165,7 +165,80 @@ For detailed instructions explaining how to build and run a pipeline that uses M
 
 ## Beam Java SDK support
 
-RunInference API is available to Beam Java SDK 2.41.0 and later through Apache Beam's [Multi-language Pipelines framework](https://beam.apache.org/documentation/programming-guide/#multi-language-pipelines). Please see [here](https://github.com/apache/beam/blob/master/sdks/java/extensions/python/src/main/java/org/apache/beam/sdk/extensions/python/transforms/RunInference.java) for the Java wrapper transform to use and please see [here](https://github.com/apache/beam/blob/master/sdks/java/extensions/python/src/test/java/org/apache/beam/sdk/extensions/python/transforms/RunInferenceTransformTest.java) for some example pipelines.
+The RunInference API is available with the Beam Java SDK versions 2.41.0 and later through Apache Beam's [Multi-language Pipelines framework](https://beam.apache.org/documentation/programming-guide/#multi-language-pipelines). For information about the Java wrapper transform, see [RunInference.java](https://github.com/apache/beam/blob/master/sdks/java/extensions/python/src/main/java/org/apache/beam/sdk/extensions/python/transforms/RunInference.java). For example pipelines, see [RunInferenceTransformTest.java](https://github.com/apache/beam/blob/master/sdks/java/extensions/python/src/test/java/org/apache/beam/sdk/extensions/python/transforms/RunInferenceTransformTest.java).
+
+## TensorFlow support
+
+To use TensorFlow with the RunInference API, you need to do the following:
+
+* Use `tfx_bsl` version 1.10.0 or later.
+* Create a model handler using `tfx_bsl.public.beam.run_inference.CreateModelHandler()`.
+* Use the model handler with the [`apache_beam.ml.inference.base.RunInference`](/releases/pydoc/current/apache_beam.ml.inference.base.html) transform.
+
+A sample pipeline might look like the following example:
+
+```
+import apache_beam as beam
+from apache_beam.ml.inference.base import RunInference
+from tensorflow_serving.apis import prediction_log_pb2
+from tfx_bsl.public.proto import model_spec_pb2
+from tfx_bsl.public.tfxio import TFExampleRecord
+from tfx_bsl.public.beam.run_inference import CreateModelHandler
+
+pipeline = beam.Pipeline()
+tfexample_beam_record = TFExampleRecord(file_pattern=predict_values_five_times_table)
+saved_model_spec = model_spec_pb2.SavedModelSpec(model_path=save_model_dir_multiply)
+inference_spec_type = model_spec_pb2.InferenceSpecType(saved_model_spec=saved_model_spec)
+model_handler = CreateModelHandler(inference_spec_type)
+with pipeline as p:
+    _ = (p | tfexample_beam_record.RawRecordBeamSource() 
+           | RunInference(model_handler)
+           | beam.Map(print)
+        )
+```
+
+First, within `tfx_bsl`, create a model handler. For more information, see [run_inference.py](https://github.com/tensorflow/tfx-bsl/blob/d1fca25e5eeaac9ef0111ec13e7634df836f36f6/tfx_bsl/public/beam/run_inference.py) in the TensorFlow GitHub repository.
+
+```
+tf_handler = CreateModelHandler(inference_spec_type)
+
+# unkeyed
+RunInference(tf_handler)
+
+# keyed
+RunInference(KeyedModelHandler(tf_handler))
+```
+
+The model handler that is created from within `tfx-bsl` is always unkeyed. To make a keyed model handler, wrap the unkeyed model handler in the keyed model handler, which would then take the `tfx-bsl` model handler as a parameter. For example:
+
+```
+beam.run_inference(beam.ml.inference.KeyedModelHandler(tf_handler))

Review Comment:
   I updated this example.



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