Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

feat: Add support for start_execution in MLMD SDK. #1465

Merged
merged 7 commits into from
Jul 11, 2022
Merged
Show file tree
Hide file tree
Changes from 2 commits
Commits
File filter

Filter by extension

Filter by extension

Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
65 changes: 65 additions & 0 deletions google/cloud/aiplatform/metadata/schema/base_execution.py
Original file line number Diff line number Diff line change
Expand Up @@ -24,6 +24,7 @@
from google.cloud.aiplatform.compat.types import execution as gca_execution
from google.cloud.aiplatform.metadata import constants
from google.cloud.aiplatform.metadata import execution
from google.cloud.aiplatform.metadata import metadata


class BaseExecutionSchema(metaclass=abc.ABCMeta):
Expand Down Expand Up @@ -112,3 +113,67 @@ def create(
credentials=credentials,
)
return self.execution

def start_execution(
self,
*,
metadata_store_id: Optional[str] = "default",
resume: bool = False,
project: Optional[str] = None,
location: Optional[str] = None,
credentials: Optional[auth_credentials.Credentials] = None,
) -> "execution.Execution":
"""Create and starts a new Metadata Execution or resumes a previously created Execution.
To start a new execution:
```
with execution_schema.ContainerExecution(display_name='preprocess').start_execution() as exc:
exc.assign_input_artifacts([my_artifact])
model = aiplatform.Artifact.create(uri='gs://my-uri', schema_title='system.Model')
exc.assign_output_artifacts([model])
```
To continue a previously created execution:
```
with execution_schema.ContainerExecution(resource_id='my-exc', resume=True) as exc:
...
```
Args:
metadata_store_id (str):
Optional. The <metadata_store_id> portion of the resource name with
the format:
projects/123/locations/us-central1/metadataStores/<metadata_store_id>/executions/<resource_id>
If not provided, the MetadataStore's ID will be set to "default". Currently only the 'default'
MetadataStore ID is supported.
resume (bool):
Resume an existing execution. If resume set to `True`, resource_id must be provided.
SinaChavoshi marked this conversation as resolved.
Show resolved Hide resolved
project (str):
Optional. Project used to create this Execution. Overrides project set in
aiplatform.init.
location (str):
Optional. Location used to create this Execution. Overrides location set in
aiplatform.init.
credentials (auth_credentials.Credentials):
Optional. Custom credentials used to create this Execution. Overrides
credentials set in aiplatform.init.
Returns:
Execution: Instantiated representation of the managed Metadata Execution.
Raises:
ValueError: If metadata_store_id other than 'default' is provided.
"""
if metadata_store_id != "default":
raise ValueError(
f"metadata_store_id {metadata_store_id} is not supported. Only the default MetadataStore ID is supported."
)

return metadata._ExperimentTracker().start_execution(
schema_title=self.schema_title,
display_name=self.display_name,
resource_id=self.execution_id,
metadata=self.metadata,
schema_version=self.schema_version,
description=self.description,
# TODO: Add support for metadata_store_id once it is supported in experiment.
resume=resume,
project=project,
location=location,
credentials=credentials,
)
27 changes: 27 additions & 0 deletions tests/unit/aiplatform/test_metadata_schema.py
Original file line number Diff line number Diff line change
Expand Up @@ -561,3 +561,30 @@ def test_container_spec_to_dict_method_returns_correct_schema(self):
}

assert json.dumps(container_spec.to_dict()) == json.dumps(expected_results)

@pytest.mark.usefixtures("create_execution_mock")
def test_start_execution_method_calls_gapic_library_with_correct_parameters(
self, create_execution_mock
):
aiplatform.init(project=_TEST_PROJECT)

class TestExecution(base_execution.BaseExecutionSchema):
schema_title = _TEST_SCHEMA_TITLE

execution = TestExecution(
state=_TEST_EXECUTION_STATE,
display_name=_TEST_DISPLAY_NAME,
description=_TEST_DESCRIPTION,
metadata=_TEST_UPDATED_METADATA,
)
execution.start_execution()
create_execution_mock.assert_called_once_with(
parent=f"{_TEST_PARENT}/metadataStores/default",
execution=mock.ANY,
execution_id=None,
)
_, _, kwargs = create_execution_mock.mock_calls[0]
assert kwargs["execution"].schema_title == _TEST_SCHEMA_TITLE
assert kwargs["execution"].display_name == _TEST_DISPLAY_NAME
assert kwargs["execution"].description == _TEST_DESCRIPTION
assert kwargs["execution"].metadata == _TEST_UPDATED_METADATA