def setUp(self):
self._source_data_dir = os.path.join(
os.path.dirname(os.path.dirname(__file__)), 'testdata')
self._output_data_dir = os.path.join(
os.environ.get('TEST_UNDECLARED_OUTPUTS_DIR', self.get_temp_dir()),
self._testMethodName)
tf.gfile.MakeDirs(self._output_data_dir)
self._model_export = standard_artifacts.Model()
self._model_export.uri = os.path.join(self._source_data_dir,
'trainer/current/')
self._model_blessing = standard_artifacts.ModelBlessing()
self._input_dict = {
'model_export': [self._model_export],
'model_blessing': [self._model_blessing],
}
self._model_push = standard_artifacts.PushedModel()
self._model_push.uri = os.path.join(self._output_data_dir,
'model_push')
tf.gfile.MakeDirs(self._model_push.uri)
self._output_dict = {
'model_push': [self._model_push],
}
self._serving_model_dir = os.path.join(self._output_data_dir,
'serving_model_dir')
tf.gfile.MakeDirs(self._serving_model_dir)
self._exec_properties = {
'push_destination':
json_format.MessageToJson(
pusher_pb2.PushDestination(
filesystem=pusher_pb2.PushDestination.Filesystem(
base_directory=self._serving_model_dir))),
}
self._executor = executor.Executor()
def setUp(self):
super(ExecutorTest, self).setUp()
self._source_data_dir = os.path.join(
os.path.dirname(os.path.dirname(__file__)), 'testdata')
self._output_data_dir = os.path.join(
os.environ.get('TEST_UNDECLARED_OUTPUTS_DIR', self.get_temp_dir()),
self._testMethodName)
tf.io.gfile.makedirs(self._output_data_dir)
self._model_export = standard_artifacts.Model()
self._model_export.uri = os.path.join(self._source_data_dir,
'trainer/current')
self._model_blessing = standard_artifacts.ModelBlessing()
self._input_dict = {
executor.MODEL_KEY: [self._model_export],
executor.MODEL_BLESSING_KEY: [self._model_blessing],
}
self._model_push = standard_artifacts.PushedModel()
self._model_push.uri = os.path.join(self._output_data_dir, 'model_push')
tf.io.gfile.makedirs(self._model_push.uri)
self._output_dict = {
executor.PUSHED_MODEL_KEY: [self._model_push],
}
self._serving_model_dir = os.path.join(self._output_data_dir,
'serving_model_dir')
tf.io.gfile.makedirs(self._serving_model_dir)
self._exec_properties = {
'push_destination':
json_format.MessageToJson(
pusher_pb2.PushDestination(
filesystem=pusher_pb2.PushDestination.Filesystem(
base_directory=self._serving_model_dir)),
preserving_proto_field_name=True),
}
self._executor = executor.Executor()
def setUp(self):
super(ExecutorTest, self).setUp()
self._source_data_dir = os.path.join(
os.path.dirname(os.path.dirname(__file__)), 'testdata')
self._output_data_dir = os.path.join(
os.environ.get('TEST_UNDECLARED_OUTPUTS_DIR', self.get_temp_dir()),
self._testMethodName)
fileio.makedirs(self._output_data_dir)
self._model_export = standard_artifacts.Model()
self._model_export.uri = os.path.join(self._source_data_dir,
'trainer/current')
self._model_blessing = standard_artifacts.ModelBlessing()
self._input_dict = {
MODEL_KEY: [self._model_export],
MODEL_BLESSING_KEY: [self._model_blessing],
}
self._model_push = standard_artifacts.PushedModel()
self._model_push.uri = os.path.join(self._output_data_dir,
'model_push')
fileio.makedirs(self._model_push.uri)
self._output_dict = {
PUSHED_MODEL_KEY: [self._model_push],
}
self._serving_model_dir = os.path.join(self._output_data_dir,
'serving_model_dir')
fileio.makedirs(self._serving_model_dir)
self._exec_properties = self._MakeExecProperties()
self._executor = executor.Executor()
def setUp(self):
self._source_data_dir = os.path.join(
os.path.dirname(
os.path.dirname(os.path.dirname(os.path.dirname(__file__)))),
'components', 'testdata')
self._output_data_dir = os.path.join(
os.environ.get('TEST_UNDECLARED_OUTPUTS_DIR', self.get_temp_dir()),
self._testMethodName)
tf.gfile.MakeDirs(self._output_data_dir)
self._model_export = standard_artifacts.Model()
self._model_export.uri = os.path.join(self._source_data_dir,
'trainer/current/')
self._model_blessing = standard_artifacts.ModelBlessing()
self._input_dict = {
'model_export': [self._model_export],
'model_blessing': [self._model_blessing],
}
self._model_push = standard_artifacts.PushedModel()
self._model_push.uri = os.path.join(self._output_data_dir,
'model_push')
tf.gfile.MakeDirs(self._model_push.uri)
self._output_dict = {
'model_push': [self._model_push],
}
self._exec_properties = {
'custom_config': {
'ai_platform_serving_args': {
'model_name': 'model_name',
'project_id': 'project_id'
},
},
}
self._executor = Executor()
def setUp(self):
super().setUp()
self._source_data_dir = os.path.join(
os.path.dirname(
os.path.dirname(os.path.dirname(os.path.dirname(__file__)))),
'components', 'testdata')
self._output_data_dir = os.path.join(
os.environ.get('TEST_UNDECLARED_OUTPUTS_DIR', self.get_temp_dir()),
self._testMethodName)
fileio.makedirs(self._output_data_dir)
self._model_export = standard_artifacts.Model()
self._model_export.uri = os.path.join(self._source_data_dir,
'trainer/current')
self._model_blessing = standard_artifacts.ModelBlessing()
self._input_dict = {
standard_component_specs.MODEL_KEY: [self._model_export],
standard_component_specs.MODEL_BLESSING_KEY:
[self._model_blessing],
}
self._model_push = standard_artifacts.PushedModel()
self._model_push.uri = os.path.join(self._output_data_dir,
'model_push')
fileio.makedirs(self._model_push.uri)
self._output_dict = {
standard_component_specs.PUSHED_MODEL_KEY: [self._model_push],
}
# Dict format of exec_properties. custom_config needs to be serialized
# before being passed into Do function.
self._exec_properties = {
'custom_config': {
constants.SERVING_ARGS_KEY: {
'model_name': 'model_name',
'project_id': 'project_id'
},
},
'push_destination': None,
}
self._container_image_uri_vertex = 'gcr.io/path/to/container'
# Dict format of exec_properties for Vertex. custom_config needs to be
# serialized before being passed into Do function.
self._exec_properties_vertex = {
'custom_config': {
constants.SERVING_ARGS_KEY: {
'endpoint_name': 'endpoint_name',
'project_id': 'project_id',
},
constants.VERTEX_CONTAINER_IMAGE_URI_KEY:
self._container_image_uri_vertex,
constants.VERTEX_REGION_KEY: 'us-central1',
constants.ENABLE_VERTEX_KEY: True,
},
'push_destination': None,
}
self._executor = executor.Executor()
def testConstruct(self):
examples = standard_artifacts.Examples()
model = standard_artifacts.Model()
model_blessing = standard_artifacts.ModelBlessing()
pushed_model = standard_artifacts.PushedModel()
bulk_inferrer = component.BulkInferrer(
examples=channel_utils.as_channel([examples]),
model=channel_utils.as_channel([model]),
model_blessing=channel_utils.as_channel([model_blessing])
pushed_model=channel_utils.as_channel([pushed_model]))
self.assertEqual('InferenceResult',
bulk_inferrer.outputs['inference_result'].type_name)
def __init__(
self,
model: types.Channel = None,
push_destination: Optional[Union[pusher_pb2.PushDestination,
Dict[Text, Any]]] = None,
custom_config: Optional[Dict[Text, Any]] = None,
custom_executor_spec: Optional[executor_spec.ExecutorSpec] = None,
output: Optional[types.Channel] = None,
instance_name: Optional[Text] = None,
enable_cache: Optional[bool] = None
):
"""Construct a Pusher component.
Args:
model: A Channel of type `standard_artifacts.Model`, usually produced by
a Trainer component.
push_destination: A pusher_pb2.PushDestination instance, providing info
for tensorflow serving to load models. Optional if executor_class
doesn't require push_destination. If any field is provided as a
RuntimeParameter, push_destination should be constructed as a dict with
the same field names as PushDestination proto message.
custom_config: A dict which contains the deployment job parameters to be
passed to cloud-based training platforms. The [Kubeflow
example](https://github.com/tensorflow/tfx/blob/master/tfx/examples/chicago_taxi_pipeline/taxi_pipeline_kubeflow.py#L211)
contains an example how this can be used by custom executors.
custom_executor_spec: Optional custom executor spec.
output: Optional output `standard_artifacts.PushedModel` channel with
result of push.
instance_name: Optional unique instance name. Necessary if multiple Pusher
components are declared in the same pipeline.
enable_cache: Optional boolean to indicate if cache is enabled for the
Pusher component. If not specified, defaults to the value
specified for pipeline's enable_cache parameter.
"""
output = output or types.Channel(
type=standard_artifacts.PushedModel,
artifacts=[standard_artifacts.PushedModel()])
if push_destination is None and not custom_executor_spec:
raise ValueError('push_destination is required unless a '
'custom_executor_spec is supplied that does not require '
'it.')
spec = AlwaysPusherSpec(
model=model,
push_destination=push_destination,
custom_config=custom_config,
pushed_model=output)
super(AlwaysPusher, self).__init__(
spec=spec,
custom_executor_spec=custom_executor_spec,
instance_name=instance_name,
enable_cache=enable_cache)
def __init__(
self,
model: types.Channel = None,
model_blessing: types.Channel = None,
push_destination: Optional[Union[pusher_pb2.PushDestination,
Dict[Text, Any]]] = None,
custom_config: Optional[Dict[Text, Any]] = None,
custom_executor_spec: Optional[executor_spec.ExecutorSpec] = None,
model_push: Optional[types.Channel] = None,
model_export: Optional[types.Channel] = None,
instance_name: Optional[Text] = None):
"""Construct a Pusher component.
Args:
model: A Channel of type `standard_artifacts.Model`, usually produced by
a Trainer component.
model_blessing: A Channel of type `standard_artifacts.ModelBlessing`,
usually produced by a ModelValidator component. _required_
push_destination: A pusher_pb2.PushDestination instance, providing info
for tensorflow serving to load models. Optional if executor_class
doesn't require push_destination. If any field is provided as a
RuntimeParameter, push_destination should be constructed as a dict with
the same field names as PushDestination proto message.
custom_config: A dict which contains the deployment job parameters to be
passed to cloud-based training platforms. The [Kubeflow
example](https://github.com/tensorflow/tfx/blob/master/tfx/examples/chicago_taxi_pipeline/taxi_pipeline_kubeflow.py#L211)
contains an example how this can be used by custom executors.
custom_executor_spec: Optional custom executor spec.
model_push: Optional output 'ModelPushPath' channel with result of push.
model_export: Backwards compatibility alias for the 'model' argument.
instance_name: Optional unique instance name. Necessary if multiple Pusher
components are declared in the same pipeline.
"""
model = model or model_export
model_push = model_push or types.Channel(
type=standard_artifacts.PushedModel,
artifacts=[standard_artifacts.PushedModel()])
if push_destination is None and not custom_executor_spec:
raise ValueError(
'push_destination is required unless a '
'custom_executor_spec is supplied that does not require '
'it.')
spec = PusherSpec(model_export=model,
model_blessing=model_blessing,
push_destination=push_destination,
custom_config=custom_config,
model_push=model_push)
super(Pusher, self).__init__(spec=spec,
custom_executor_spec=custom_executor_spec,
instance_name=instance_name)
def setUp(self):
super(ExecutorTest, self).setUp()
self._source_data_dir = os.path.join(
os.path.dirname(
os.path.dirname(os.path.dirname(os.path.dirname(__file__)))),
'components', 'testdata')
self._output_data_dir = os.path.join(
os.environ.get('TEST_UNDECLARED_OUTPUTS_DIR', self.get_temp_dir()),
self._testMethodName)
tf.io.gfile.makedirs(self._output_data_dir)
self._model_export = standard_artifacts.Model()
self._model_export.uri = os.path.join(self._source_data_dir,
'trainer/current/')
self._model_blessing = standard_artifacts.ModelBlessing()
self._input_dict = {
'model_export': [self._model_export],
'model_blessing': [self._model_blessing],
}
self._model_push = standard_artifacts.PushedModel()
self._model_push.uri = os.path.join(self._output_data_dir,
'model_push')
tf.io.gfile.makedirs(self._model_push.uri)
self._output_dict = {
'model_push': [self._model_push],
}
self._exec_properties = {
'custom_config': {
'bigquery_serving_args': {
'model_name': 'model_name',
'project_id': 'project_id',
'bq_dataset_id': 'bq_dataset_id',
},
},
'push_destination': None,
}
self._executor = Executor()
# Setting up Mock for external services
self.addCleanup(mock.patch.stopall)
self.mock_bq = mock.patch.object(bigquery, 'Client',
autospec=True).start()
self.mock_check_blessing = mock.patch.object(Executor,
'CheckBlessing',
autospec=True).start()
self.mock_path_utils = mock.patch.object(
path_utils,
'serving_model_path',
return_value='gs://test_model_path',
autospec=True).start()
def __init__(
self,
model_export: types.Channel = None,
model_blessing: types.Channel = None,
push_destination: Optional[pusher_pb2.PushDestination] = None,
custom_config: Optional[Dict[Text, Any]] = None,
executor_class: Optional[Type[base_executor.BaseExecutor]] = None,
model_push: Optional[types.Channel] = None,
model: Optional[types.Channel] = None,
name: Optional[Text] = None):
"""Construct a Pusher component.
Args:
model_export: A Channel of 'ModelExportPath' type, usually produced by
Trainer component (required).
model_blessing: A Channel of 'ModelBlessingPath' type, usually produced by
ModelValidator component (required).
push_destination: A pusher_pb2.PushDestination instance, providing
info for tensorflow serving to load models. Optional if executor_class
doesn't require push_destination.
custom_config: A dict which contains the deployment job parameters to be
passed to Google Cloud ML Engine. For the full set of parameters
supported by Google Cloud ML Engine, refer to
https://cloud.google.com/ml-engine/reference/rest/v1/projects.models
executor_class: Optional custom python executor class.
model_push: Optional output 'ModelPushPath' channel with result of push.
model: Forwards compatibility alias for the 'model_exports' argument.
name: Optional unique name. Necessary if multiple Pusher components are
declared in the same pipeline.
"""
model_export = model_export or model
model_push = model_push or types.Channel(
type=standard_artifacts.PushedModel,
artifacts=[standard_artifacts.PushedModel()])
if push_destination is None and not executor_class:
raise ValueError(
'push_destination is required unless a custom '
'executor_class is supplied that does not require '
'it.')
spec = PusherSpec(model_export=model_export,
model_blessing=model_blessing,
push_destination=push_destination,
custom_config=custom_config,
model_push=model_push)
super(Pusher, self).__init__(spec=spec,
custom_executor_class=executor_class,
name=name)
def setUp(self):
super(ExecutorTest, self).setUp()
self._source_data_dir = os.path.join(
os.path.dirname(
os.path.dirname(os.path.dirname(os.path.dirname(__file__)))),
'components', 'testdata')
self._output_data_dir = os.path.join(
os.environ.get('TEST_UNDECLARED_OUTPUTS_DIR', self.get_temp_dir()),
self._testMethodName)
fileio.makedirs(self._output_data_dir)
self._model_export = standard_artifacts.Model()
self._model_export.uri = os.path.join(self._source_data_dir,
'trainer/current')
self._model_blessing = standard_artifacts.ModelBlessing()
self._input_dict = {
standard_component_specs.MODEL_KEY: [self._model_export],
standard_component_specs.MODEL_BLESSING_KEY:
[self._model_blessing],
}
self._model_push = standard_artifacts.PushedModel()
self._model_push.uri = os.path.join(self._output_data_dir,
'model_push')
fileio.makedirs(self._model_push.uri)
self._output_dict = {
standard_component_specs.PUSHED_MODEL_KEY: [self._model_push],
}
# Dict format of exec_properties. custom_config needs to be serialized
# before being passed into Do function.
self._exec_properties = {
'custom_config': {
executor.SERVING_ARGS_KEY: {
'model_name': 'model_name',
'project_id': 'project_id'
},
},
'push_destination': None,
}
self._executor = executor.Executor()
def setUp(self):
super(ExecutorTest, self).setUp()
self._source_data_dir = os.path.join(
os.path.dirname(os.path.dirname(__file__)), 'testdata')
self._output_data_dir = os.path.join(
os.environ.get('TEST_UNDECLARED_OUTPUTS_DIR', self.get_temp_dir()),
self._testMethodName)
self.component_id = 'test_component'
# Create input dict.
self._examples = standard_artifacts.Examples()
self._examples.uri = os.path.join(self._source_data_dir,
'csv_example_gen')
self._examples.split_names = artifact_utils.encode_split_names(
['unlabelled'])
self._model = standard_artifacts.Model()
self._model.uri = os.path.join(self._source_data_dir,
'trainer/current')
self._model_blessing = standard_artifacts.ModelBlessing()
self._model_blessing.uri = os.path.join(self._source_data_dir,
'model_validator/blessed')
self._model_blessing.set_int_custom_property('blessed', 1)
self._pushed_model = standard_artifacts.PushedModel()
self._pushed_model.uri = os.path.join(self._source_data_dir,
'pusher/pushed')
self._pushed_model.set_int_custom_property('pushed', 1)
self._inference_result = standard_artifacts.InferenceResult()
self._prediction_log_dir = os.path.join(self._output_data_dir,
'prediction_logs')
self._inference_result.uri = self._prediction_log_dir
# Create context
self._tmp_dir = os.path.join(self._output_data_dir, '.temp')
self._context = executor.Executor.Context(tmp_dir=self._tmp_dir,
unique_id='2')
def setUp(self):
super(ExecutorTest, self).setUp()
self._source_data_dir = os.path.join(
os.path.dirname(
os.path.dirname(os.path.dirname(os.path.dirname(__file__)))),
'components', 'testdata')
self._output_data_dir = os.path.join(
os.environ.get('TEST_UNDECLARED_OUTPUTS_DIR', self.get_temp_dir()),
self._testMethodName)
tf.io.gfile.makedirs(self._output_data_dir)
self._model_export = standard_artifacts.Model()
self._model_export.uri = os.path.join(self._source_data_dir,
'trainer/current')
self._model_blessing = standard_artifacts.ModelBlessing()
self._input_dict = {
'model_export': [self._model_export],
'model_blessing': [self._model_blessing],
}
self._model_push = standard_artifacts.PushedModel()
self._model_push.uri = os.path.join(self._output_data_dir,
'model_push')
tf.io.gfile.makedirs(self._model_push.uri)
self._output_dict = {
'model_push': [self._model_push],
}
self._exec_properties = {
'custom_config': {
executor.SERVING_ARGS_KEY: {
'model_name': 'model_name',
'project_id': 'project_id'
},
},
'push_destination': None,
}
self._executor = executor.Executor()
def __init__(
self,
model: types.Channel = None,
model_blessing: types.Channel = None,
infra_blessing: Optional[types.Channel] = None,
push_destination: Optional[Union[pusher_pb2.PushDestination,
Dict[Text, Any]]] = None,
custom_config: Optional[Dict[Text, Any]] = None,
custom_executor_spec: Optional[executor_spec.ExecutorSpec] = None,
output: Optional[types.Channel] = None,
model_export: Optional[types.Channel] = None,
instance_name: Optional[Text] = None,
enable_cache: Optional[bool] = None):
"""Construct a Pusher component.
Args:
model: A Channel of type `standard_artifacts.Model`, usually produced by
a Trainer component.
model_blessing: A Channel of type `standard_artifacts.ModelBlessing`,
usually produced by a ModelValidator component. _required_
infra_blessing: An optional Channel of type
`standard_artifacts.InfraBlessing`, usually produced from an
InfraValidator component.
push_destination: A pusher_pb2.PushDestination instance, providing info
for tensorflow serving to load models. Optional if executor_class
doesn't require push_destination. If any field is provided as a
RuntimeParameter, push_destination should be constructed as a dict with
the same field names as PushDestination proto message.
custom_config: A dict which contains the deployment job parameters to be
passed to cloud-based training platforms. The [Kubeflow example](
https://github.com/tensorflow/tfx/blob/6ff57e36a7b65818d4598d41e584a42584d361e6/tfx/examples/chicago_taxi_pipeline/taxi_pipeline_kubeflow_gcp.py#L278-L285)
contains an example how this can be used by custom executors.
custom_executor_spec: Optional custom executor spec.
output: Optional output `standard_artifacts.PushedModel` channel with
result of push.
model_export: Backwards compatibility alias for the 'model' argument.
instance_name: Optional unique instance name. Necessary if multiple Pusher
components are declared in the same pipeline.
enable_cache: Optional boolean to indicate if cache is enabled for the
Pusher component. If not specified, defaults to the value
specified for pipeline's enable_cache parameter.
"""
if model_export:
absl.logging.warning(
'The "model_export" argument to the Pusher component has '
'been renamed to "model" and is deprecated. Please update your '
'usage as support for this argument will be removed soon.')
model = model_export
output = output or types.Channel(
type=standard_artifacts.PushedModel,
artifacts=[standard_artifacts.PushedModel()])
if push_destination is None and not custom_executor_spec:
raise ValueError(
'push_destination is required unless a '
'custom_executor_spec is supplied that does not require '
'it.')
spec = PusherSpec(model=model,
model_blessing=model_blessing,
infra_blessing=infra_blessing,
push_destination=push_destination,
custom_config=custom_config,
pushed_model=output)
super(Pusher, self).__init__(spec=spec,
custom_executor_spec=custom_executor_spec,
instance_name=instance_name,
enable_cache=enable_cache)
def test_scaffold_assets_with_source(self, output_file_format: str,
artifacts: bool):
if artifacts:
connection_config = metadata_store_pb2.ConnectionConfig()
connection_config.fake_database.SetInParent()
mlmd_store = mlmd.MetadataStore(connection_config)
else:
mlmd_store = None
train_dataset_name = 'Dataset-Split-train'
train_features = ['feature_name1']
eval_dataset_name = 'Dataset-Split-eval'
eval_features = ['feature_name2']
tfma_path = os.path.join(self.tmpdir, 'tfma')
tfdv_path = os.path.join(self.tmpdir, 'tfdv')
pushed_model_path = os.path.join(self.tmpdir, 'pushed_model')
self._write_tfma(tfma_path, output_file_format, mlmd_store)
self._write_tfdv(tfdv_path, train_dataset_name, train_features,
eval_dataset_name, eval_features, mlmd_store)
if artifacts:
model_evaluation_artifacts = mlmd_store.get_artifacts_by_type(
standard_artifacts.ModelEvaluation.TYPE_NAME)
example_statistics_artifacts = mlmd_store.get_artifacts_by_type(
standard_artifacts.ExampleStatistics.TYPE_NAME)
pushed_model_artifact = standard_artifacts.PushedModel()
pushed_model_artifact.uri = pushed_model_path
tfma_src = src.TfmaSource(
model_evaluation_artifacts=model_evaluation_artifacts,
metrics_exclude=['average_loss'])
tfdv_src = src.TfdvSource(
example_statistics_artifacts=example_statistics_artifacts,
features_include=['feature_name1'])
model_src = src.ModelSource(
pushed_model_artifact=pushed_model_artifact)
else:
tfma_src = src.TfmaSource(eval_result_paths=[tfma_path],
metrics_exclude=['average_loss'])
tfdv_src = src.TfdvSource(dataset_statistics_paths=[tfdv_path],
features_include=['feature_name1'])
model_src = src.ModelSource(pushed_model_path=pushed_model_path)
mc = model_card_toolkit.ModelCardToolkit(source=src.Source(
tfma=tfma_src, tfdv=tfdv_src, model=model_src)).scaffold_assets()
with self.subTest(name='quantitative_analysis'):
list_to_proto = lambda lst: [x.to_proto() for x in lst]
expected_performance_metrics = [
model_card.PerformanceMetric(
type='post_export_metrics/example_count', value='2.0')
]
self.assertCountEqual(
list_to_proto(mc.quantitative_analysis.performance_metrics),
list_to_proto(expected_performance_metrics))
self.assertLen(mc.quantitative_analysis.graphics.collection, 1)
with self.subTest(name='model_parameters.data'):
self.assertLen(mc.model_parameters.data, 2) # train and eval
for dataset in mc.model_parameters.data:
for graphic in dataset.graphics.collection:
self.assertIsNotNone(
graphic.image,
msg=
f'No image found for graphic: {dataset.name} {graphic.name}'
)
graphic.image = None # ignore graphic.image for below assertions
self.assertIn(
model_card.Dataset(
name=train_dataset_name,
graphics=model_card.GraphicsCollection(collection=[
model_card.Graphic(name='counts | feature_name1')
])), mc.model_parameters.data)
self.assertNotIn(
model_card.Dataset(
name=eval_dataset_name,
graphics=model_card.GraphicsCollection(collection=[
model_card.Graphic(name='counts | feature_name2')
])), mc.model_parameters.data)
with self.subTest(name='model_details.path'):
self.assertEqual(mc.model_details.path, pushed_model_path)
