feat(components): Added the component to convert from KerasModelHdf5 …

…to TensorflowSavedModel (#4524)

* Components -  Added the component to convert from KerasModelHdf5 to TensorflowSavedModel

* Adding the component to a sample

components/_converters/KerasModelHdf5/to_TensorflowSavedModel/component.py

@@ -0,0 +1,29 @@
+from kfp.components import create_component_from_func, InputPath, OutputPath
+
+def keras_convert_hdf5_model_to_tf_saved_model(
+    model_path: InputPath('KerasModelHdf5'),
+    converted_model_path: OutputPath('TensorflowSavedModel'),
+):
+    '''Converts Keras HDF5 model to Tensorflow SavedModel format.
+
+    Args:
+        model_path: Keras model in HDF5 format.
+        converted_model_path: Keras model in Tensorflow SavedModel format.
+
+    Annotations:
+        author: Alexey Volkov <alexey.volkov@ark-kun.com>
+    '''
+    from pathlib import Path
+    from tensorflow import keras
+
+    model = keras.models.load_model(filepath=model_path)
+    keras.models.save_model(model=model, filepath=converted_model_path, save_format='tf')
+
+
+if __name__ == '__main__':
+    keras_convert_hdf5_model_to_tf_saved_model_op = create_component_from_func(
+        keras_convert_hdf5_model_to_tf_saved_model,
+        base_image='tensorflow/tensorflow:2.3.0',
+        packages_to_install=['h5py==2.10.0'],
+        output_component_file='component.yaml',
+    )

components/_converters/KerasModelHdf5/to_TensorflowSavedModel/component.yaml

@@ -0,0 +1,55 @@
+name: Keras convert hdf5 model to tf saved model
+description: Converts Keras HDF5 model to Tensorflow SavedModel format.
+inputs:
+- {name: model, type: KerasModelHdf5, description: Keras model in HDF5 format.}
+outputs:
+- {name: converted_model, type: TensorflowSavedModel, description: Keras model in Tensorflow SavedModel format.}
+implementation:
+  container:
+    image: tensorflow/tensorflow:2.3.0
+    command:
+    - sh
+    - -c
+    - (PIP_DISABLE_PIP_VERSION_CHECK=1 python3 -m pip install --quiet --no-warn-script-location
+      'h5py==2.10.0' || PIP_DISABLE_PIP_VERSION_CHECK=1 python3 -m pip install --quiet
+      --no-warn-script-location 'h5py==2.10.0' --user) && "$0" "$@"
+    - python3
+    - -u
+    - -c
+    - |
+      def _make_parent_dirs_and_return_path(file_path: str):
+          import os
+          os.makedirs(os.path.dirname(file_path), exist_ok=True)
+          return file_path
+
+      def keras_convert_hdf5_model_to_tf_saved_model(
+          model_path,
+          converted_model_path,
+      ):
+          '''Converts Keras HDF5 model to Tensorflow SavedModel format.
+
+          Args:
+              model_path: Keras model in HDF5 format.
+              converted_model_path: Keras model in Tensorflow SavedModel format.
+
+          Annotations:
+              author: Alexey Volkov <alexey.volkov@ark-kun.com>
+          '''
+          from pathlib import Path
+          from tensorflow import keras
+
+          model = keras.models.load_model(filepath=model_path)
+          keras.models.save_model(model=model, filepath=converted_model_path, save_format='tf')
+
+      import argparse
+      _parser = argparse.ArgumentParser(prog='Keras convert hdf5 model to tf saved model', description='Converts Keras HDF5 model to Tensorflow SavedModel format.')
+      _parser.add_argument("--model", dest="model_path", type=str, required=True, default=argparse.SUPPRESS)
+      _parser.add_argument("--converted-model", dest="converted_model_path", type=_make_parent_dirs_and_return_path, required=True, default=argparse.SUPPRESS)
+      _parsed_args = vars(_parser.parse_args())
+
+      _outputs = keras_convert_hdf5_model_to_tf_saved_model(**_parsed_args)
+    args:
+    - --model
+    - {inputPath: model}
+    - --converted-model
+    - {outputPath: converted_model}

components/keras/Train_classifier/_samples/sample_pipeline.py

@@ -6,6 +6,8 @@
 chicago_taxi_dataset_op = components.load_component_from_url('https://raw.githubusercontent.com/kubeflow/pipelines/e3337b8bdcd63636934954e592d4b32c95b49129/components/datasets/Chicago%20Taxi/component.yaml')
 pandas_transform_csv_op = components.load_component_from_url('https://raw.githubusercontent.com/kubeflow/pipelines/6162d55998b176b50267d351241100bb0ee715bc/components/pandas/Transform_DataFrame/in_CSV_format/component.yaml')
 keras_train_classifier_from_csv_op = components.load_component_from_url('https://raw.githubusercontent.com/kubeflow/pipelines/f6aabf7f10b1f545f1fd5079aa8071845224f8e7/components/keras/Train_classifier/from_CSV/component.yaml')
+keras_convert_hdf5_model_to_tf_saved_model_op = components.load_component_from_url('https://raw.githubusercontent.com/kubeflow/pipelines/51e49282d9511e4b72736c12dc66e37486849c6e/components/_converters/KerasModelHdf5/to_TensorflowSavedModel/component.yaml')
+
 
 number_of_classes = 2
 
@@ -38,14 +40,17 @@ def keras_classifier_pipeline():
         transform_code='''df = df["was_tipped"] * 1''',
     ).output
 
-    keras_train_classifier_from_csv_op(
+    keras_model_in_hdf5 = keras_train_classifier_from_csv_op(
         training_features=features_in_csv,
         training_labels=labels_in_csv,
         network_json=dense_network_with_sigmoid.to_json(),
         learning_rate=0.1,
         num_epochs=100,
-    )
+    ).outputs['model']
 
+    keras_model_in_tf_format = keras_convert_hdf5_model_to_tf_saved_model_op(
+        model=keras_model_in_hdf5,
+    ).output
 
 if __name__ == '__main__':
     kfp_endpoint = None