Removing tf-ranking as a dependency untill it supports tf 2.16 (#7725)

tfx/examples/ranking/features.py

@@ -17,36 +17,37 @@
 These names will be shared between the transform and the model.
 """
 
-import tensorflow as tf
-from tfx.examples.ranking import struct2tensor_parsing_utils
+# import tensorflow as tf
+# This is due to TF Ranking not supporting TensorFlow 2.16, We should re-enable it when support is added.
+# from tfx.examples.ranking import struct2tensor_parsing_utils
 
 # Labels are expected to be dense. In case of a batch of ELWCs have different
 # number of documents, the shape of the label is [N, D], where N is the batch
 # size, D is the maximum number of documents in the batch. If an ELWC in the
 # batch has D_0 < D documents, then the value of label at D0 <= d < D must be
 # negative to indicate that the document is invalid.
-LABEL_PADDING_VALUE = -1
+#LABEL_PADDING_VALUE = -1
 
 # Names of features in the ELWC.
-QUERY_TOKENS = 'query_tokens'
-DOCUMENT_TOKENS = 'document_tokens'
-LABEL = 'relevance'
+#QUERY_TOKENS = 'query_tokens'
+#DOCUMENT_TOKENS = 'document_tokens'
+#LABEL = 'relevance'
 
 # This "feature" does not exist in the data but will be created on the fly.
-LIST_SIZE_FEATURE_NAME = 'example_list_size'
+# LIST_SIZE_FEATURE_NAME = 'example_list_size'
 
 
-def get_features():
-  """Defines the context features and example features spec for parsing."""
+#def get_features():
+#  """Defines the context features and example features spec for parsing."""
 
-  context_features = [
-      struct2tensor_parsing_utils.Feature(QUERY_TOKENS, tf.string)
-  ]
+ # context_features = [
+ #     struct2tensor_parsing_utils.Feature(QUERY_TOKENS, tf.string)
+ # ]
 
-  example_features = [
-      struct2tensor_parsing_utils.Feature(DOCUMENT_TOKENS, tf.string)
-  ]
+#  example_features = [
+#      struct2tensor_parsing_utils.Feature(DOCUMENT_TOKENS, tf.string)
+#  ]
 
-  label = struct2tensor_parsing_utils.Feature(LABEL, tf.int64)
+#  label = struct2tensor_parsing_utils.Feature(LABEL, tf.int64)
 
-  return context_features, example_features, label
+#  return context_features, example_features, label

tfx/examples/ranking/ranking_pipeline_e2e_test.py

@@ -16,9 +16,12 @@
 import unittest
 
 import tensorflow as tf
-from tfx.examples.ranking import ranking_pipeline
-from tfx.orchestration import metadata
-from tfx.orchestration.beam.beam_dag_runner import BeamDagRunner
+# from tfx.orchestration import metadata
+# from tfx.orchestration.beam.beam_dag_runner import BeamDagRunner
+
+# This is due to TF Ranking not supporting TensorFlow 2.16, We should re-enable it when support is added.
+# from tfx.examples.ranking import ranking_pipeline
+
 
 try:
   import struct2tensor  # pylint: disable=g-import-not-at-top
@@ -62,23 +65,23 @@ def assertExecutedOnce(self, component) -> None:
       execution = tf.io.gfile.listdir(os.path.join(component_path, output))
       self.assertEqual(1, len(execution))
 
-  def testPipeline(self):
-    BeamDagRunner().run(
-        ranking_pipeline._create_pipeline(
-            pipeline_name=self._pipeline_name,
-            pipeline_root=self._tfx_root,
-            data_root=self._data_root,
-            module_file=self._module_file,
-            serving_model_dir=self._serving_model_dir,
-            metadata_path=self._metadata_path,
-            beam_pipeline_args=['--direct_num_workers=1']))
-    self.assertTrue(tf.io.gfile.exists(self._serving_model_dir))
-    self.assertTrue(tf.io.gfile.exists(self._metadata_path))
+  #def testPipeline(self):
+   # BeamDagRunner().run(
+   #     ranking_pipeline._create_pipeline(
+   #         pipeline_name=self._pipeline_name,
+   #         pipeline_root=self._tfx_root,
+   #         data_root=self._data_root,
+   #         module_file=self._module_file,
+   #         serving_model_dir=self._serving_model_dir,
+   #         metadata_path=self._metadata_path,
+   #         beam_pipeline_args=['--direct_num_workers=1']))
+   # self.assertTrue(tf.io.gfile.exists(self._serving_model_dir))
+   # self.assertTrue(tf.io.gfile.exists(self._metadata_path))
 
-    metadata_config = metadata.sqlite_metadata_connection_config(
-        self._metadata_path)
-    with metadata.Metadata(metadata_config) as m:
-      artifact_count = len(m.store.get_artifacts())
-      execution_count = len(m.store.get_executions())
-      self.assertGreaterEqual(artifact_count, execution_count)
-      self.assertEqual(9, execution_count)
+   # metadata_config = metadata.sqlite_metadata_connection_config(
+   #     self._metadata_path)
+   # with metadata.Metadata(metadata_config) as m:
+   #   artifact_count = len(m.store.get_artifacts())
+   #   execution_count = len(m.store.get_executions())
+   #   self.assertGreaterEqual(artifact_count, execution_count)
+   #   self.assertEqual(9, execution_count)