feat(components): Release Forecasting training pipelines to V1 namespace

PiperOrigin-RevId: 615914679
kubeflow · Mar 14, 2024 · ab549ef · ab549ef
1 parent 2abe91e
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diff --git a/components/google-cloud/RELEASE.md b/components/google-cloud/RELEASE.md
@@ -1,5 +1,4 @@
 ## Upcoming release
-* Add `v1.automl.forecasting.learn_to_learn_forecasting_pipeline`, `v1.automl.forecasting.sequence_to_sequence_forecasting_pipeline`, `v1.automl.forecasting.temporal_fusion_transformer_forecasting_pipeline`, `v1.automl.forecasting.time_series_dense_encoder_forecasting_pipeline` as Forecasting on Pipelines moves to GA.
 * Fix bug in `preview.llm.rlhf_pipeline` that caused wrong output artifact to be used for inference after training.
 * Fix issue where AutoSxS was not propagating location to all sub-components.
 * Add CMEK support to `preview.llm.infer_pipeline`.

diff --git a/...ents/google-cloud/google_cloud_pipeline_components/preview/automl/forecasting/__init__.py b/...ents/google-cloud/google_cloud_pipeline_components/preview/automl/forecasting/__init__.py
@@ -12,24 +12,18 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-"""Preview AutoML forecasting components."""
-
+"""Experimental AutoML forecasting components."""
 import os
 
 from google_cloud_pipeline_components.preview.automl.forecasting.forecasting_ensemble import automl_forecasting_ensemble as ForecastingEnsembleOp
 from google_cloud_pipeline_components.preview.automl.forecasting.forecasting_stage_1_tuner import automl_forecasting_stage_1_tuner as ForecastingStage1TunerOp
 from google_cloud_pipeline_components.preview.automl.forecasting.forecasting_stage_2_tuner import automl_forecasting_stage_2_tuner as ForecastingStage2TunerOp
-from google_cloud_pipeline_components.v1.automl.forecasting import learn_to_learn_forecasting_pipeline
-from google_cloud_pipeline_components.v1.automl.forecasting import sequence_to_sequence_forecasting_pipeline
-from google_cloud_pipeline_components.v1.automl.forecasting import temporal_fusion_transformer_forecasting_pipeline
-from google_cloud_pipeline_components.v1.automl.forecasting import time_series_dense_encoder_forecasting_pipeline
-from google_cloud_pipeline_components.v1.automl.forecasting.utils import get_learn_to_learn_forecasting_pipeline_and_parameters
-from google_cloud_pipeline_components.v1.automl.forecasting.utils import get_sequence_to_sequence_forecasting_pipeline_and_parameters
-from google_cloud_pipeline_components.v1.automl.forecasting.utils import get_temporal_fusion_transformer_forecasting_pipeline_and_parameters
-from google_cloud_pipeline_components.v1.automl.forecasting.utils import get_time_series_dense_encoder_forecasting_pipeline_and_parameters
+from google_cloud_pipeline_components.preview.automl.forecasting.utils import get_learn_to_learn_forecasting_pipeline_and_parameters
+from google_cloud_pipeline_components.preview.automl.forecasting.utils import get_sequence_to_sequence_forecasting_pipeline_and_parameters
+from google_cloud_pipeline_components.preview.automl.forecasting.utils import get_temporal_fusion_transformer_forecasting_pipeline_and_parameters
+from google_cloud_pipeline_components.preview.automl.forecasting.utils import get_time_series_dense_encoder_forecasting_pipeline_and_parameters
 from kfp import components
 
-
 __all__ = [
     'ForecastingEnsembleOp',
     'ForecastingStage1TunerOp',
@@ -43,3 +37,38 @@
     'temporal_fusion_transformer_forecasting_pipeline',
     'time_series_dense_encoder_forecasting_pipeline',
 ]
+
+learn_to_learn_forecasting_pipeline = components.load_component_from_file(
+    # Note, please don't name it as `component.yaml` which will conflict with
+    # the generated file.
+    os.path.join(
+        os.path.dirname(__file__), 'learn_to_learn_forecasting_pipeline.yaml'
+    )
+)
+
+sequence_to_sequence_forecasting_pipeline = components.load_component_from_file(
+    # Note, please don't name it as `component.yaml` which will conflict with
+    # the generated file.
+    os.path.join(
+        os.path.dirname(__file__),
+        'sequence_to_sequence_forecasting_pipeline.yaml',
+    )
+)
+
+temporal_fusion_transformer_forecasting_pipeline = components.load_component_from_file(
+    # Note, please don't name it as `component.yaml` which will conflict with
+    # the generated file.
+    os.path.join(
+        os.path.dirname(__file__),
+        'temporal_fusion_transformer_forecasting_pipeline.yaml',
+    )
+)
+
+time_series_dense_encoder_forecasting_pipeline = components.load_component_from_file(
+    # Note, please don't name it as `component.yaml` which will conflict with
+    # the generated file.
+    os.path.join(
+        os.path.dirname(__file__),
+        'time_series_dense_encoder_forecasting_pipeline.yaml',
+    )
+)
diff --git a/...e_components/preview/automl/tabular/wide_and_deep_hyperparameter_tuning_job_pipeline.yaml b/...e_components/preview/automl/tabular/wide_and_deep_hyperparameter_tuning_job_pipeline.yaml
@@ -49,7 +49,7 @@
 #    test_fraction: float [Default: -1.0]
 #    tf_auto_transform_features: dict
 #    tf_custom_transformation_definitions: list
-#    tf_transform_execution_engine: str [Default: 'bigquery']
+#    tf_transform_execution_engine: str [Default: '']
 #    tf_transformations_path: str [Default: '']
 #    training_fraction: float [Default: -1.0]
 #    transform_dataflow_disk_size_gb: int [Default: 40.0]
@@ -3819,7 +3819,7 @@ root:
         isOptional: true
         parameterType: LIST
       tf_transform_execution_engine:
-        defaultValue: bigquery
+        defaultValue: ''
         description: 'Execution engine to run TF-based
 
           transformations. Currently supports "dataflow" or "bigquery"'

diff --git a/...ogle_cloud_pipeline_components/preview/automl/tabular/wide_and_deep_trainer_pipeline.yaml b/...ogle_cloud_pipeline_components/preview/automl/tabular/wide_and_deep_trainer_pipeline.yaml
@@ -65,7 +65,7 @@
 #    test_fraction: float [Default: -1.0]
 #    tf_auto_transform_features: dict
 #    tf_custom_transformation_definitions: list
-#    tf_transform_execution_engine: str [Default: 'bigquery']
+#    tf_transform_execution_engine: str [Default: '']
 #    tf_transformations_path: str [Default: '']
 #    training_fraction: float [Default: -1.0]
 #    transform_dataflow_disk_size_gb: int [Default: 40.0]
@@ -3839,7 +3839,7 @@ root:
         isOptional: true
         parameterType: LIST
       tf_transform_execution_engine:
-        defaultValue: bigquery
+        defaultValue: ''
         description: 'Execution engine to run TF-based
 
           transformations. Currently supports "dataflow" or "bigquery"'

diff --git a/components/google-cloud/google_cloud_pipeline_components/v1/automl/forecasting/__init__.py b/components/google-cloud/google_cloud_pipeline_components/v1/automl/forecasting/__init__.py
@@ -13,66 +13,17 @@
 # limitations under the License.
 
 """GA AutoML forecasting components."""
-import os
 
 from google_cloud_pipeline_components.v1.automl.forecasting.prophet_trainer import prophet_trainer as ProphetTrainerOp
 from google_cloud_pipeline_components.v1.automl.forecasting.utils import get_bqml_arima_predict_pipeline_and_parameters
 from google_cloud_pipeline_components.v1.automl.forecasting.utils import get_bqml_arima_train_pipeline_and_parameters
-from google_cloud_pipeline_components.v1.automl.forecasting.utils import get_learn_to_learn_forecasting_pipeline_and_parameters
 from google_cloud_pipeline_components.v1.automl.forecasting.utils import get_prophet_prediction_pipeline_and_parameters
 from google_cloud_pipeline_components.v1.automl.forecasting.utils import get_prophet_train_pipeline_and_parameters
-from google_cloud_pipeline_components.v1.automl.forecasting.utils import get_sequence_to_sequence_forecasting_pipeline_and_parameters
-from google_cloud_pipeline_components.v1.automl.forecasting.utils import get_temporal_fusion_transformer_forecasting_pipeline_and_parameters
-from google_cloud_pipeline_components.v1.automl.forecasting.utils import get_time_series_dense_encoder_forecasting_pipeline_and_parameters
-from kfp import components
 
 __all__ = [
     'ProphetTrainerOp',
     'get_bqml_arima_predict_pipeline_and_parameters',
     'get_bqml_arima_train_pipeline_and_parameters',
     'get_prophet_prediction_pipeline_and_parameters',
     'get_prophet_train_pipeline_and_parameters',
-    'get_learn_to_learn_forecasting_pipeline_and_parameters',
-    'get_sequence_to_sequence_forecasting_pipeline_and_parameters',
-    'get_temporal_fusion_transformer_forecasting_pipeline_and_parameters',
-    'get_time_series_dense_encoder_forecasting_pipeline_and_parameters',
-    'learn_to_learn_forecasting_pipeline',
-    'sequence_to_sequence_forecasting_pipeline',
-    'temporal_fusion_transformer_forecasting_pipeline',
-    'time_series_dense_encoder_forecasting_pipeline',
 ]
-
-learn_to_learn_forecasting_pipeline = components.load_component_from_file(
-    # Note, please don't name it as `component.yaml` which will conflict with
-    # the generated file.
-    os.path.join(
-        os.path.dirname(__file__), 'learn_to_learn_forecasting_pipeline.yaml'
-    )
-)
-
-sequence_to_sequence_forecasting_pipeline = components.load_component_from_file(
-    # Note, please don't name it as `component.yaml` which will conflict with
-    # the generated file.
-    os.path.join(
-        os.path.dirname(__file__),
-        'sequence_to_sequence_forecasting_pipeline.yaml',
-    )
-)
-
-temporal_fusion_transformer_forecasting_pipeline = components.load_component_from_file(
-    # Note, please don't name it as `component.yaml` which will conflict with
-    # the generated file.
-    os.path.join(
-        os.path.dirname(__file__),
-        'temporal_fusion_transformer_forecasting_pipeline.yaml',
-    )
-)
-
-time_series_dense_encoder_forecasting_pipeline = components.load_component_from_file(
-    # Note, please don't name it as `component.yaml` which will conflict with
-    # the generated file.
-    os.path.join(
-        os.path.dirname(__file__),
-        'time_series_dense_encoder_forecasting_pipeline.yaml',
-    )
-)