tessl/pypi-google-cloud-aiplatform
Comprehensive Python client library for Google Cloud Vertex AI, offering machine learning tools, generative AI models, and MLOps capabilities
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Pending
pipelines.mddocs/
ML Pipelines
Workflow orchestration, scheduling, and complex ML pipeline management with Kubeflow Pipelines integration.
Capabilities
Pipeline Jobs
Execute and manage ML workflows with comprehensive monitoring and scheduling.
class PipelineJob:
@classmethod
def create(
cls,
display_name: str,
template_path: str,
job_id: Optional[str] = None,
pipeline_root: Optional[str] = None,
parameter_values: Optional[Dict[str, Any]] = None,
input_artifacts: Optional[Dict[str, str]] = None,
enable_caching: Optional[bool] = None,
encryption_spec_key_name: Optional[str] = None,
labels: Optional[Dict[str, str]] = None,
credentials: Optional[auth_credentials.Credentials] = None,
project: Optional[str] = None,
location: Optional[str] = None,
**kwargs
) -> 'PipelineJob': ...
def run(
self,
service_account: Optional[str] = None,
network: Optional[str] = None,
sync: bool = True,
create_request_timeout: Optional[float] = None,
**kwargs
) -> None: ...
@property
def state(self) -> PipelineState: ...
@property
def task_details(self) -> List[PipelineTaskDetail]: ...Pipeline Scheduling
Schedule pipeline executions with cron expressions and comprehensive scheduling options.
class PipelineJobSchedule:
@classmethod
def create(
cls,
pipeline_job: PipelineJob,
display_name: str,
cron: str,
max_concurrent_run_count: int = 1,
max_run_count: Optional[int] = None,
service_account: Optional[str] = None,
network: Optional[str] = None,
labels: Optional[Dict[str, str]] = None,
**kwargs
) -> 'PipelineJobSchedule': ...
def pause(self) -> None: ...
def resume(self) -> None: ...
def update(self, **kwargs) -> 'PipelineJobSchedule': ...Install with Tessl CLI
npx tessl i tessl/pypi-google-cloud-aiplatform