Fix: Vertex ML pipeline test failures

package_build/initialize.sh

@@ -27,6 +27,7 @@ do
   ln -sf $BASEDIR/setup.py $BASEDIR/package_build/$CONFIG_NAME/
   ln -sf $BASEDIR/dist $BASEDIR/package_build/$CONFIG_NAME/
   ln -sf $BASEDIR/tfx $BASEDIR/package_build/$CONFIG_NAME/
+  ln -sf $BASEDIR/MANIFEST.in $BASEDIR/package_build/$CONFIG_NAME/
   ln -sf $BASEDIR/README*.md $BASEDIR/package_build/$CONFIG_NAME/
   ln -sf $BASEDIR/LICENSE $BASEDIR/package_build/$CONFIG_NAME/
 

tfx/examples/chicago_taxi_pipeline/taxi_utils.py

@@ -246,7 +246,6 @@ def _build_keras_model(
   output = tf.keras.layers.Dense(1, activation='sigmoid')(
       tf.keras.layers.concatenate([deep, wide])
   )
-  output = tf.squeeze(output, -1)
 
   model = tf.keras.Model(input_layers, output)
   model.compile(
@@ -371,4 +370,4 @@ def run_fn(fn_args: fn_args_utils.FnArgs):
           model, tf_transform_output
       ),
   }
-  model.save(fn_args.serving_model_dir, save_format='tf', signatures=signatures)
+  tf.saved_model.save(model, fn_args.serving_model_dir, signatures=signatures)

tfx/experimental/templates/taxi/models/keras_model/model.py

@@ -106,98 +106,73 @@ def _build_keras_model(hidden_units, learning_rate):
   Returns:
     A keras Model.
   """
-  real_valued_columns = [
-      tf.feature_column.numeric_column(key, shape=())
-      for key in features.transformed_names(features.DENSE_FLOAT_FEATURE_KEYS)
-  ]
-  categorical_columns = [
-      tf.feature_column.categorical_column_with_identity(  # pylint: disable=g-complex-comprehension
-          key,
-          num_buckets=features.VOCAB_SIZE + features.OOV_SIZE,
-          default_value=0)
-      for key in features.transformed_names(features.VOCAB_FEATURE_KEYS)
-  ]
-  categorical_columns += [
-      tf.feature_column.categorical_column_with_identity(  # pylint: disable=g-complex-comprehension
-          key,
-          num_buckets=num_buckets,
-          default_value=0) for key, num_buckets in zip(
-              features.transformed_names(features.BUCKET_FEATURE_KEYS),
-              features.BUCKET_FEATURE_BUCKET_COUNT)
-  ]
-  categorical_columns += [
-      tf.feature_column.categorical_column_with_identity(  # pylint: disable=g-complex-comprehension
-          key,
-          num_buckets=num_buckets,
-          default_value=0) for key, num_buckets in zip(
-              features.transformed_names(features.CATEGORICAL_FEATURE_KEYS),
-              features.CATEGORICAL_FEATURE_MAX_VALUES)
-  ]
-  indicator_column = [
-      tf.feature_column.indicator_column(categorical_column)
-      for categorical_column in categorical_columns
-  ]
-
-  model = _wide_and_deep_classifier(
-      # TODO(b/140320729) Replace with premade wide_and_deep keras model
-      wide_columns=indicator_column,
-      deep_columns=real_valued_columns,
-      dnn_hidden_units=hidden_units,
-      learning_rate=learning_rate)
-  return model
-
-
-def _wide_and_deep_classifier(wide_columns, deep_columns, dnn_hidden_units,
-                              learning_rate):
-  """Build a simple keras wide and deep model.
-
-  Args:
-    wide_columns: Feature columns wrapped in indicator_column for wide (linear)
-      part of the model.
-    deep_columns: Feature columns for deep part of the model.
-    dnn_hidden_units: [int], the layer sizes of the hidden DNN.
-    learning_rate: [float], learning rate of the Adam optimizer.
-
-  Returns:
-    A Wide and Deep Keras model
-  """
-  # Keras needs the feature definitions at compile time.
-  # TODO(b/139081439): Automate generation of input layers from FeatureColumn.
-  input_layers = {
-      colname: tf.keras.layers.Input(name=colname, shape=(), dtype=tf.float32)
-      for colname in features.transformed_names(
-          features.DENSE_FLOAT_FEATURE_KEYS)
+  deep_input = {
+      colname: tf.keras.layers.Input(name=colname, shape=(1,), dtype=tf.float32)
+      for colname in features.transformed_names(features.DENSE_FLOAT_FEATURE_KEYS)
   }
-  input_layers.update({
-      colname: tf.keras.layers.Input(name=colname, shape=(), dtype='int32')
+  wide_vocab_input = {
+      colname: tf.keras.layers.Input(name=colname, shape=(1,), dtype='int32')
       for colname in features.transformed_names(features.VOCAB_FEATURE_KEYS)
-  })
-  input_layers.update({
-      colname: tf.keras.layers.Input(name=colname, shape=(), dtype='int32')
+  }
+  wide_bucket_input = {
+      colname: tf.keras.layers.Input(name=colname, shape=(1,), dtype='int32')
       for colname in features.transformed_names(features.BUCKET_FEATURE_KEYS)
-  })
-  input_layers.update({
-      colname: tf.keras.layers.Input(name=colname, shape=(), dtype='int32') for
-      colname in features.transformed_names(features.CATEGORICAL_FEATURE_KEYS)
-  })
-
-  # TODO(b/161952382): Replace with Keras premade models and
-  # Keras preprocessing layers.
-  deep = tf.keras.layers.DenseFeatures(deep_columns)(input_layers)
-  for numnodes in dnn_hidden_units:
+  }
+  wide_categorical_input = {
+      colname: tf.keras.layers.Input(name=colname, shape=(1,), dtype='int32')
+      for colname in features.transformed_names(features.CATEGORICAL_FEATURE_KEYS)
+  }
+  input_layers = {
+      **deep_input,
+      **wide_vocab_input,
+      **wide_bucket_input,
+      **wide_categorical_input,
+  }
+
+  deep = tf.keras.layers.concatenate(
+      [tf.keras.layers.Normalization()(layer) for layer in deep_input.values()]
+  )
+  for numnodes in (hidden_units or [100, 70, 50, 25]):
     deep = tf.keras.layers.Dense(numnodes)(deep)
-  wide = tf.keras.layers.DenseFeatures(wide_columns)(input_layers)
 
-  output = tf.keras.layers.Dense(
-      1, activation='sigmoid')(
-          tf.keras.layers.concatenate([deep, wide]))
-  output = tf.squeeze(output, -1)
+  wide_layers = []
+  for key in features.transformed_names(features.VOCAB_FEATURE_KEYS):
+    wide_layers.append(
+        tf.keras.layers.CategoryEncoding(num_tokens=features.VOCAB_SIZE + features.OOV_SIZE)(
+            input_layers[key]
+        )
+    )
+  for key, num_tokens in zip(
+      features.transformed_names(features.BUCKET_FEATURE_KEYS),
+      features.BUCKET_FEATURE_BUCKET_COUNT,
+  ):
+    wide_layers.append(
+        tf.keras.layers.CategoryEncoding(num_tokens=num_tokens)(
+                input_layers[key]
+        )
+    )
+  for key, num_tokens in zip(
+      features.transformed_names(features.CATEGORICAL_FEATURE_KEYS),
+      features.CATEGORICAL_FEATURE_MAX_VALUES,
+  ):
+    wide_layers.append(
+        tf.keras.layers.CategoryEncoding(num_tokens=num_tokens)(
+            input_layers[key]
+        )
+    )
+  wide = tf.keras.layers.concatenate(wide_layers)
+
+  output = tf.keras.layers.Dense(1, activation='sigmoid')(
+      tf.keras.layers.concatenate([deep, wide])
+  )
+  output = tf.keras.layers.Reshape((1,))(output)
 
   model = tf.keras.Model(input_layers, output)
   model.compile(
       loss='binary_crossentropy',
       optimizer=tf.keras.optimizers.Adam(learning_rate=learning_rate),
-      metrics=[tf.keras.metrics.BinaryAccuracy()])
+      metrics=[tf.keras.metrics.BinaryAccuracy()],
+  )
   model.summary(print_fn=logging.info)
   return model
 
@@ -240,4 +215,4 @@ def run_fn(fn_args):
       'transform_features':
           _get_transform_features_signature(model, tf_transform_output),
   }
-  model.save(fn_args.serving_model_dir, save_format='tf', signatures=signatures)
+  tf.saved_model.save(model, fn_args.serving_model_dir, signatures=signatures)

tfx/experimental/templates/taxi/models/keras_model/model_test.py

@@ -22,7 +22,7 @@ class ModelTest(tf.test.TestCase):
   def testBuildKerasModel(self):
     built_model = model._build_keras_model(
         hidden_units=[1, 1], learning_rate=0.1)  # pylint: disable=protected-access
-    self.assertEqual(len(built_model.layers), 10)
+    self.assertEqual(len(built_model.layers), 13)
 
     built_model = model._build_keras_model(hidden_units=[1], learning_rate=0.1)  # pylint: disable=protected-access
-    self.assertEqual(len(built_model.layers), 9)
+    self.assertEqual(len(built_model.layers), 12)

tfx/orchestration/kubeflow/v2/test_utils.py

@@ -234,25 +234,28 @@ def create_pipeline_components(
       model_blessing=tfx.dsl.Channel(
           type=tfx.types.standard_artifacts.ModelBlessing)).with_id(
               'Resolver.latest_blessed_model_resolver')
-  # Set the TFMA config for Model Evaluation and Validation.
+  # Uses TFMA to compute a evaluation statistics over features of a model and
+  # perform quality validation of a candidate model (compared to a baseline).
   eval_config = tfma.EvalConfig(
-      model_specs=[tfma.ModelSpec(signature_name='eval')],
-      metrics_specs=[
-          tfma.MetricsSpec(
-              metrics=[tfma.MetricConfig(class_name='ExampleCount')],
-              thresholds={
-                  'binary_accuracy':
-                      tfma.MetricThreshold(
-                          value_threshold=tfma.GenericValueThreshold(
-                              lower_bound={'value': 0.5}),
-                          change_threshold=tfma.GenericChangeThreshold(
-                              direction=tfma.MetricDirection.HIGHER_IS_BETTER,
-                              absolute={'value': -1e-10}))
-              })
+      model_specs=[
+          tfma.ModelSpec(
+              signature_name='serving_default', label_key='tips_xf',
+              preprocessing_function_names=['transform_features'])
       ],
-      slicing_specs=[
-          tfma.SlicingSpec(),
-          tfma.SlicingSpec(feature_keys=['trip_start_hour'])
+      slicing_specs=[tfma.SlicingSpec()],
+      metrics_specs=[
+          tfma.MetricsSpec(metrics=[
+              tfma.MetricConfig(
+                  class_name='BinaryAccuracy',
+                  threshold=tfma.MetricThreshold(
+                      value_threshold=tfma.GenericValueThreshold(
+                          lower_bound={'value': 0.6}),
+                      # Change threshold will be ignored if there is no
+                      # baseline model resolved from MLMD (first run).
+                      change_threshold=tfma.GenericChangeThreshold(
+                          direction=tfma.MetricDirection.HIGHER_IS_BETTER,
+                          absolute={'value': -1e-10})))
+          ])
       ])
   evaluator = tfx.components.Evaluator(
       examples=example_gen.outputs['examples'],

tfx/orchestration/kubeflow/v2/testdata/expected_full_taxi_pipeline_job.json

@@ -706,7 +706,7 @@
               "parameters": {
                 "eval_config": {
                   "runtimeValue": {
-                    "constant": "{\n  \"metrics_specs\": [\n    {\n      \"metrics\": [\n        {\n          \"class_name\": \"ExampleCount\"\n        }\n      ],\n      \"thresholds\": {\n        \"binary_accuracy\": {\n          \"change_threshold\": {\n            \"absolute\": -1e-10,\n            \"direction\": \"HIGHER_IS_BETTER\"\n          },\n          \"value_threshold\": {\n            \"lower_bound\": 0.5\n          }\n        }\n      }\n    }\n  ],\n  \"model_specs\": [\n    {\n      \"signature_name\": \"eval\"\n    }\n  ],\n  \"slicing_specs\": [\n    {},\n    {\n      \"feature_keys\": [\n        \"trip_start_hour\"\n      ]\n    }\n  ]\n}"
+                    "constant": "{\n  \"metrics_specs\": [\n    {\n      \"metrics\": [\n        {\n          \"class_name\": \"BinaryAccuracy\",\n          \"threshold\": {\n            \"change_threshold\": {\n              \"absolute\": -1e-10,\n              \"direction\": \"HIGHER_IS_BETTER\"\n            },\n            \"value_threshold\": {\n              \"lower_bound\": 0.6\n            }\n          }\n        }\n      ]\n    }\n  ],\n  \"model_specs\": [\n    {\n      \"label_key\": \"tips_xf\",\n      \"preprocessing_function_names\": [\n        \"transform_features\"\n      ],\n      \"signature_name\": \"serving_default\"\n    }\n  ],\n  \"slicing_specs\": [\n    {}\n  ]\n}"
                   }
                 },
                 "example_splits": {

tfx/orchestration/kubeflow/v2/testdata/legacy/expected_full_taxi_pipeline_job.json

@@ -698,7 +698,7 @@
                 "eval_config": {
                   "runtimeValue": {
                     "constantValue": {
-                      "stringValue": "{\n  \"metrics_specs\": [\n    {\n      \"metrics\": [\n        {\n          \"class_name\": \"ExampleCount\"\n        }\n      ],\n      \"thresholds\": {\n        \"binary_accuracy\": {\n          \"change_threshold\": {\n            \"absolute\": -1e-10,\n            \"direction\": \"HIGHER_IS_BETTER\"\n          },\n          \"value_threshold\": {\n            \"lower_bound\": 0.5\n          }\n        }\n      }\n    }\n  ],\n  \"model_specs\": [\n    {\n      \"signature_name\": \"eval\"\n    }\n  ],\n  \"slicing_specs\": [\n    {},\n    {\n      \"feature_keys\": [\n        \"trip_start_hour\"\n      ]\n    }\n  ]\n}"
+                      "stringValue": "{\n  \"metrics_specs\": [\n    {\n      \"metrics\": [\n        {\n          \"class_name\": \"BinaryAccuracy\",\n          \"threshold\": {\n            \"change_threshold\": {\n              \"absolute\": -1e-10,\n              \"direction\": \"HIGHER_IS_BETTER\"\n            },\n            \"value_threshold\": {\n              \"lower_bound\": 0.6\n            }\n          }\n        }\n      ]\n    }\n  ],\n  \"model_specs\": [\n    {\n      \"label_key\": \"tips_xf\",\n      \"preprocessing_function_names\": [\n        \"transform_features\"\n      ],\n      \"signature_name\": \"serving_default\"\n    }\n  ],\n  \"slicing_specs\": [\n    {}\n  ]\n}"
                     }
                   }
                 },

tfx/tools/docker/Dockerfile

@@ -27,8 +27,7 @@ WORKDIR ${TFX_DIR}
 ARG TFX_DEPENDENCY_SELECTOR
 ENV TFX_DEPENDENCY_SELECTOR=${TFX_DEPENDENCY_SELECTOR}
 
-RUN python -m pip install --upgrade pip wheel setuptools
-RUN python -m pip install tomli
+RUN python -m pip install --upgrade pip wheel setuptools tomli
 
 # TODO(b/175089240): clean up conditional checks on whether ml-pipelines-sdk is
 # built after TFX versions <= 0.25 are no longer eligible for cherry-picks.

tfx/v1/orchestration/experimental/__init__.py

@@ -14,6 +14,9 @@
 """TFX orchestration.experimental module."""
 
 try:
+    from tfx.orchestration.kubeflow.decorators import exit_handler  # pylint: disable=g-import-not-at-top
+    from tfx.orchestration.kubeflow.decorators import FinalStatusStr  # pylint: disable=g-import-not-at-top
+
     from tfx.orchestration.kubeflow.v2.kubeflow_v2_dag_runner import (
         KubeflowV2DagRunner,
         KubeflowV2DagRunnerConfig,
@@ -24,11 +27,8 @@
 
 __all__ = [
     "FinalStatusStr",
-    "KubeflowDagRunner",
-    "KubeflowDagRunnerConfig",
     "KubeflowV2DagRunner",
     "KubeflowV2DagRunnerConfig",
-    "LABEL_KFP_SDK_ENV",
     "exit_handler",
     "get_default_kubeflow_metadata_config",
 ]