brk-code

pipelines の API リファレンス

pipelines (kubeflow/pipelines) の公開 API 383 件 —— クラス 81、関数 204、メソッド 98。実際のソースを静的解析して抽出した正確なシグネチャを掲載しています。

リポジトリ: kubeflow/pipelines

種別件数
クラス81
関数204
メソッド98

API 一覧

class.github.actions.junit-summary.junit_to_summary.TestCase
Represents a single test case.
class.github.actions.junit-summary.junit_to_summary.TestReport
Represents the complete test report.
class.github.actions.junit-summary.junit_to_summary.TestSuite
Represents a test suite.
func.github.actions.junit-summary.junit_to_summary.expand_file_patterns(patterns:List[str]) -> List[Path]
Expand glob patterns and collect all XML files.
func.github.actions.junit-summary.junit_to_summary.format_duration(seconds:float) -> str
Format duration in seconds to a human-readable string.
func.github.actions.junit-summary.junit_to_summary.parse_junit_xml(xml_file:Path) -> TestSuite
Parse a JUnit XML file and extract test results.
func.github.actions.junit-summary.junit_to_summary.parse_test_case(testcase_elem:ET.Element) -> TestCase
Parse a single testcase element.
func.github.actions.junit-summary.junit_to_summary.parse_test_suite(suite_elem:ET.Element) -> TestSuite
Parse a single testsuite element.
func.github.actions.junit-summary.junit_to_summary.set_github_output(key:str, value:str)
Set a GitHub Action output.
func.github.actions.junit-summary.junit_to_summary.write_to_step_summary(markdown:str)
Write markdown to GitHub step summary.
methodbackend.api.v1beta1.python_http_client.kfp_server_api.configuration.Configuration.logger_file()
The logger file.
methodbackend.api.v1beta1.python_http_client.kfp_server_api.configuration.Configuration.logger_format()
The logger format.
methodbackend.api.v2beta1.python_http_client.kfp_server_api.configuration.Configuration.logger_file()
The logger file.
methodbackend.api.v2beta1.python_http_client.kfp_server_api.configuration.Configuration.logger_format()
The logger format.
methodbackend.src.apiserver.visualization.exporter.Exporter.generate_html_from_notebook(nb:NotebookNode) -> Text
Converts a provided NotebookNode to HTML.
funccomponents.google-cloud.google_cloud_pipeline_components._implementation.llm.preference_data_formatter.format_preference_data(input_uri:str) -> str
Format the input for preference data.
funccomponents.google-cloud.google_cloud_pipeline_components._implementation.llm.preprocess_chat_dataset.get_gcs_path(input_path:str, allow_local_files:bool) -> str
Gets the /gcs/ path for a given URI.
funccomponents.google-cloud.google_cloud_pipeline_components._implementation.llm.preprocess_chat_dataset.get_gs_path(input_path:str, allow_local_files:bool) -> str
Gets the gs:// path for a given URI.
funccomponents.google-cloud.google_cloud_pipeline_components.container._implementation.llm.templated_custom_job.launcher.main(argv:List[str]) -> None
Main entry.
funccomponents.google-cloud.google_cloud_pipeline_components.container._implementation.model.get_model.get_model.main(argv)
Main entry.
funccomponents.google-cloud.google_cloud_pipeline_components.container.preview.custom_job.launcher.main(argv)
Main entry.
funccomponents.google-cloud.google_cloud_pipeline_components.container.preview.dataflow.flex_template.launcher.main(argv)
Main entry.
funccomponents.google-cloud.google_cloud_pipeline_components.container.v1.aiplatform.remote_runner.cast(value:str, annotation_type:Type[T]) -> T
Casts a value to the annotation type.
funccomponents.google-cloud.google_cloud_pipeline_components.container.v1.aiplatform.utils.is_serializable_to_json(annotation:Any) -> bool
Checks if the type is serializable.
funccomponents.google-cloud.google_cloud_pipeline_components.container.v1.automl_training_job.image.launcher.main(argv:List[str])
Main entry.
funccomponents.google-cloud.google_cloud_pipeline_components.container.v1.batch_prediction_job.launcher.main(argv)
Main entry.
funccomponents.google-cloud.google_cloud_pipeline_components.container.v1.bigquery.create_model.launcher.main(argv)
Main entry.
funccomponents.google-cloud.google_cloud_pipeline_components.container.v1.bigquery.drop_model.launcher.main(argv)
Main entry.
funccomponents.google-cloud.google_cloud_pipeline_components.container.v1.bigquery.evaluate_model.launcher.main(argv)
Main entry.
funccomponents.google-cloud.google_cloud_pipeline_components.container.v1.bigquery.export_model.launcher.main(argv)
Main entry.
funccomponents.google-cloud.google_cloud_pipeline_components.container.v1.bigquery.feature_importance.launcher.main(argv)
Main entry.
funccomponents.google-cloud.google_cloud_pipeline_components.container.v1.bigquery.forecast_model.launcher.main(argv)
Main entry.
funccomponents.google-cloud.google_cloud_pipeline_components.container.v1.bigquery.global_explain.launcher.main(argv)
Main entry.
funccomponents.google-cloud.google_cloud_pipeline_components.container.v1.bigquery.ml_advanced_weights.launcher.main(argv)
Main entry.
funccomponents.google-cloud.google_cloud_pipeline_components.container.v1.bigquery.ml_arima_evaluate.launcher.main(argv)
Main entry.
funccomponents.google-cloud.google_cloud_pipeline_components.container.v1.bigquery.ml_centroids.launcher.main(argv)
Main entry.
funccomponents.google-cloud.google_cloud_pipeline_components.container.v1.bigquery.ml_confusion_matrix.launcher.main(argv)
Main entry.
funccomponents.google-cloud.google_cloud_pipeline_components.container.v1.bigquery.ml_feature_info.launcher.main(argv)
Main entry.
funccomponents.google-cloud.google_cloud_pipeline_components.container.v1.bigquery.ml_recommend.launcher.main(argv)
Main entry.
funccomponents.google-cloud.google_cloud_pipeline_components.container.v1.bigquery.ml_roc_curve.launcher.main(argv)
Main entry.
funccomponents.google-cloud.google_cloud_pipeline_components.container.v1.bigquery.ml_training_info.launcher.main(argv)
Main entry.
funccomponents.google-cloud.google_cloud_pipeline_components.container.v1.bigquery.ml_trial_info.launcher.main(argv)
Main entry.
funccomponents.google-cloud.google_cloud_pipeline_components.container.v1.bigquery.ml_weights.launcher.main(argv)
Main entry.
funccomponents.google-cloud.google_cloud_pipeline_components.container.v1.bigquery.predict_model.launcher.main(argv)
Main entry.
funccomponents.google-cloud.google_cloud_pipeline_components.container.v1.bigquery.query_job.launcher.main(argv)
Main entry.
funccomponents.google-cloud.google_cloud_pipeline_components.container.v1.custom_job.launcher.main(argv)
Main entry.
funccomponents.google-cloud.google_cloud_pipeline_components.container.v1.dataproc.create_pyspark_batch.launcher.main(argv)
Main entry.
funccomponents.google-cloud.google_cloud_pipeline_components.container.v1.dataproc.create_spark_batch.launcher.main(argv)
Main entry.
funccomponents.google-cloud.google_cloud_pipeline_components.container.v1.dataproc.create_spark_r_batch.launcher.main(argv)
Main entry.
funccomponents.google-cloud.google_cloud_pipeline_components.container.v1.endpoint.create_endpoint.launcher.main(argv)
Main entry.
funccomponents.google-cloud.google_cloud_pipeline_components.container.v1.endpoint.delete_endpoint.launcher.main(argv)
Main entry.
funccomponents.google-cloud.google_cloud_pipeline_components.container.v1.endpoint.deploy_model.launcher.main(argv)
Main entry.
funccomponents.google-cloud.google_cloud_pipeline_components.container.v1.endpoint.undeploy_model.launcher.main(argv)
Main entry.
funccomponents.google-cloud.google_cloud_pipeline_components.container.v1.hyperparameter_tuning_job.launcher.main(argv)
Main entry.
funccomponents.google-cloud.google_cloud_pipeline_components.container.v1.infra_validation_job.launcher.main(argv)
Main entry.
funccomponents.google-cloud.google_cloud_pipeline_components.container.v1.model.delete_model.launcher.main(argv)
Main entry.
funccomponents.google-cloud.google_cloud_pipeline_components.container.v1.model.export_model.launcher.main(argv)
Main entry.
funccomponents.google-cloud.google_cloud_pipeline_components.container.v1.model.get_model.launcher.main(argv)
Main entry.
funccomponents.google-cloud.google_cloud_pipeline_components.container.v1.model.get_model.remote_runner.get_model(executor_input, model_name:str, project:str, location:str) -> None
Get model.
funccomponents.google-cloud.google_cloud_pipeline_components.container.v1.model.upload_model.launcher.main(argv)
Main entry.
funccomponents.google-cloud.google_cloud_pipeline_components.container.v1.wait_gcp_resources.launcher.main(argv)
Main entry.
funccomponents.google-cloud.google_cloud_pipeline_components.preview.custom_job.utils.create_custom_training_job_op_from_component(*args, **kwargs) -> Callable
Deprecated.
methodcomponents.google-cloud.google_cloud_pipeline_components.types.artifact_types.VertexModel.create(name:str, uri:str, model_resource_name:str) -> 'VertexModel'
Create a VertexModel artifact instance.
funccomponents.google-cloud.google_cloud_pipeline_components.v1.custom_job.utils.create_custom_training_job_op_from_component(*args, **kwargs) -> Callable
Deprecated.
funckubernetes_platform.python.generate_proto.generate_proto(source:str) -> None
Generate a _pb2.py from a .proto file.
funckubernetes_platform.python.kfp.kubernetes.image.set_image_pull_policy(task:PipelineTask, policy:str) -> PipelineTask
Set image pull policy for the container.
funckubernetes_platform.python.kfp.kubernetes.volume.DeletePVC(pvc_name:str)
Delete a PersistentVolumeClaim.
funcrelease.kfpr.cli.clear(state_file:Path=typer.Option(Path(STATE_FILE), help='Path to checkpoint state file.')) -> None
Delete the checkpoint state file.
funcrelease.kfpr.cli.done_command(step_id:str, state_file:Path=typer.Option(Path(STATE_FILE), help='Path to checkpoint state file.')) -> None
Mark one release step complete in the checkpoint.
funcrelease.kfpr.cli.validate_state_command(state_file:Path=typer.Option(Path(STATE_FILE), help='Path to checkpoint state file.')) -> None
Validate a checkpoint file before resuming.
classrelease.kfpr.core.CommandRunner
Runs commands with optional dry-run mode.
classrelease.kfpr.core.ReleaseContext
Context for release step execution.
classrelease.kfpr.core.ReleaseState
Checkpoint for release process state.
methodrelease.kfpr.core.ReleaseState.is_done(step_id:str) -> bool
Check if a step has been marked as done.
methodrelease.kfpr.core.ReleaseState.load(path:Path) -> 'ReleaseState'
Load state from JSON file.
methodrelease.kfpr.core.ReleaseState.mark_done(step_id:str) -> None
Mark a step as done.
methodrelease.kfpr.core.ReleaseState.reset_step(step_id:str) -> None
Remove a step from the completed checkpoint list.
methodrelease.kfpr.core.ReleaseState.save() -> None
Save state to JSON file atomically.
funcrelease.kfpr.core.collect_context(args:argparse.Namespace, state:ReleaseState) -> ReleaseContext
Collect release context from state and prompts.
funcrelease.kfpr.core.confirm(question:str) -> None
Prompt user for confirmation.
funcrelease.kfpr.core.doctor_errors(answers:dict[str, object], root:Path) -> list[str]
Return release preflight diagnostics without mutating state.
funcrelease.kfpr.core.emphasize_prompt(text:str) -> str
Bold a prompt that needs user input.
funcrelease.kfpr.core.image_workflow_command(metadata:ReleaseMetadata) -> list[str]
Build command to trigger image-builds-release.yml workflow.
funcrelease.kfpr.core.normalize_fork_remote(fork_remote:str) -> str
Normalize a fork owner or remote URL to a GitHub remote URL.
funcrelease.kfpr.core.parse_github_owner(fork_remote:str) -> str
Parse GitHub owner from fork remote URL.
funcrelease.kfpr.core.prompt_choice(question:str, choices:list[str], default:str | None=None) -> str
Prompt user to choose from a list of options.
funcrelease.kfpr.core.prompt_numbered_choice(question:str, choices:list[str]) -> str
Prompt user to choose from a numbered list.
funcrelease.kfpr.core.prompt_required(question:str) -> str
Prompt user for required input.
funcrelease.kfpr.core.prompt_validated(question:str, validator) -> str
Prompt until validator accepts the answer.
funcrelease.kfpr.core.sdk_workflow_command(metadata:ReleaseMetadata, packages:str='all') -> list[str]
Build command to trigger publish-packages.yml workflow.
funcrelease.kfpr.core.underline_links(text:str) -> str
Underline URLs in terminal output.
funcrelease.kfpr.core.validate_state(state:ReleaseState) -> list[str]
Validate a checkpoint state file for resume safety.
funcrelease.kfpr.core.wait_for_pr_merge(runner:CommandRunner, pr_url:str) -> None
Poll PR status until merged.
funcrelease.kfpr.core.watch_pr_ci(runner:CommandRunner, pr_url:str) -> None
Poll PR checks until one fails or all reported checks complete.
classrelease.kfpr.rtd.ReadTheDocsClient
Tiny Read the Docs API v3 client.
classrelease.kfpr.rtd.ReadTheDocsError
Raised when Read the Docs automation cannot complete.
classrelease.kfpr.steps.Step
Represents a release step with ID, description, and handler.
funcrelease.kfpr.steps.build_steps(release_type:str, include_backend:bool, include_sdk:bool) -> list[Step]
Build the list of release steps based on configuration.
funcrelease.kfpr.steps.manual_checklist(step_id:str, metadata) -> str
Return manual checkpoint text for status output.
funcrelease.kfpr.steps.pull_if_upstream(context:ReleaseContext) -> None
Fast-forward pull only when the current branch tracks an upstream.
funcrelease.kfpr.steps.run_steps(context:ReleaseContext) -> None
Execute release steps in order, skipping completed ones.
funcrelease.kfpr.steps.step_cherry_pick_prs(context:ReleaseContext) -> None
Cherry-pick requested PRs.
funcrelease.kfpr.steps.step_confirm_rtd(context:ReleaseContext) -> None
Confirm ReadTheDocs version updates.
funcrelease.kfpr.steps.step_confirm_website_and_slack(context:ReleaseContext) -> None
Confirm website PR and Slack announcement.
funcrelease.kfpr.steps.step_create_backend_release(context:ReleaseContext) -> None
Create backend GitHub release.
funcrelease.kfpr.steps.step_create_kfp_kubernetes_docs_branch(context:ReleaseContext) -> None
Create kfp-kubernetes docs branch for ReadTheDocs.
funcrelease.kfpr.steps.step_create_sdk_release(context:ReleaseContext) -> None
Create the SDK GitHub release after packages are published.
funcrelease.kfpr.steps.step_create_sdk_tag(context:ReleaseContext) -> None
Create the SDK tag without creating a GitHub release.
funcrelease.kfpr.steps.step_merge_cherry_pick_pr(context:ReleaseContext) -> None
Push patch branch and create PR for cherry-picks.
funcrelease.kfpr.steps.step_merge_version_pr(context:ReleaseContext) -> None
Push release branch and tag, create version PR.
funcrelease.kfpr.steps.step_preflight(context:ReleaseContext) -> None
Verify required tools and GitHub authentication.
funcrelease.kfpr.steps.step_prepare_patch_branch(context:ReleaseContext) -> None
Create patch branch from release branch.
funcrelease.kfpr.steps.step_prepare_release_branch(context:ReleaseContext) -> None
Create release branch from the selected source branch.
funcrelease.kfpr.steps.step_publish_images(context:ReleaseContext) -> None
Trigger and watch image publication workflow.
funcrelease.kfpr.steps.step_publish_sdks(context:ReleaseContext) -> None
Trigger and watch SDK publication workflow.
funcrelease.kfpr.steps.step_sync_master(context:ReleaseContext) -> None
Sync release version to master branch.
funcrelease.kfpr.steps.step_update_version_tags(context:ReleaseContext) -> None
Update version tags in repository files.
funcsdk.python.kfp.cli.compile_.is_component_func(func:Callable) -> bool
Checks if a function is a component function.
funcsdk.python.kfp.cli.compile_.is_pipeline_func(func:Callable) -> bool
Checks if a function is a pipeline function.
funcsdk.python.kfp.cli.component.component(ctx:click.Context)
Builds shareable, containerized components.
classsdk.python.kfp.cli.diagnose_me.dev_env.Commands
Enum for gcloud and gsutil commands.
classsdk.python.kfp.cli.diagnose_me.gcp.Commands
Enum for gcloud and gsutil commands.
funcsdk.python.kfp.cli.diagnose_me.gcp.execute_gsutil_command(gsutil_command_list:List[Text], project_id:Optional[Text]=None) -> utility.ExecutorResponse
Function for invoking gsutil command.
classsdk.python.kfp.cli.diagnose_me.kubernetes_cluster.Commands
Enum for kubernetes commands.
funcsdk.python.kfp.cli.diagnose_me.kubernetes_cluster.execute_kubectl_command(kubectl_command_list:List[Text], human_readable:bool=False) -> utility.ExecutorResponse
Invokes the kubectl command.
classsdk.python.kfp.cli.diagnose_me.utility.ExecutorResponse
Class for keeping track of output of _executor methods.
methodsdk.python.kfp.cli.diagnose_me.utility.ExecutorResponse.execute_command(command_list:List[Text])
Executes the command in command_list.
methodsdk.python.kfp.cli.diagnose_me.utility.ExecutorResponse.has_error() -> bool
Returns true if execution error code was not 0.
methodsdk.python.kfp.cli.diagnose_me.utility.ExecutorResponse.json_output() -> Text
Run results in stdout in json format.
funcsdk.python.kfp.cli.diagnose_me_cli.diagnose_me(ctx:click.Context, json:bool, project_id:str, namespace:str)
Runs KFP environment diagnostic.
funcsdk.python.kfp.cli.experiment.archive(ctx:click.Context, experiment_id:str, experiment_name:str)
Archive an experiment.
funcsdk.python.kfp.cli.experiment.create(ctx:click.Context, description:str, name:str)
Create an experiment.
funcsdk.python.kfp.cli.experiment.delete(ctx:click.Context, experiment_id:str)
Delete an experiment.
funcsdk.python.kfp.cli.experiment.experiment()
Manage experiment resources.
funcsdk.python.kfp.cli.experiment.get(ctx:click.Context, experiment_id:str)
Get information about an experiment.
funcsdk.python.kfp.cli.experiment.list(ctx:click.Context, page_token:str, max_size:int, sort_by:str, filter:str)
List experiments.
funcsdk.python.kfp.cli.experiment.unarchive(ctx:click.Context, experiment_id:str, experiment_name:str)
Unarchive an experiment.
classsdk.python.kfp.cli.output.DatetimeEncoder
JSON encoder for serializing datetime objects.
classsdk.python.kfp.cli.output.ModelType
Enumerated class with the allowed output format constants.
classsdk.python.kfp.cli.output.OutputFormat
Enumerated class with the allowed output format constants.
funcsdk.python.kfp.cli.output.print_output(resources:list, model_type:ModelType, output_format:str) -> None
Prints output in tabular or JSON format, using click.echo.
funcsdk.python.kfp.cli.pipeline.create(ctx:click.Context, pipeline_name:str, package_file:str, description:str=None)
Upload a pipeline.
funcsdk.python.kfp.cli.pipeline.delete(ctx:click.Context, pipeline_id:str)
Delete a pipeline.
funcsdk.python.kfp.cli.pipeline.delete_version(ctx:click.Context, pipeline_id:str, version_id:str)
Delete a version of a pipeline.
funcsdk.python.kfp.cli.pipeline.get(ctx:click.Context, pipeline_id:str)
Get information about a pipeline.
funcsdk.python.kfp.cli.pipeline.list(ctx:click.Context, page_token:str, max_size:int, sort_by:str, filter:str)
List pipelines.
funcsdk.python.kfp.cli.pipeline.list_versions(ctx:click.Context, pipeline_id:str, page_token:str, max_size:int, sort_by:str, filter:str)
List versions of a pipeline.
funcsdk.python.kfp.cli.pipeline.pipeline()
Manage pipeline resources.
funcsdk.python.kfp.cli.recurring_run.delete(ctx:click.Context, recurring_run_id:str)
Delete a recurring run.
funcsdk.python.kfp.cli.recurring_run.disable(ctx:click.Context, recurring_run_id:str)
Disable a recurring run.
funcsdk.python.kfp.cli.recurring_run.enable(ctx:click.Context, recurring_run_id:str)
Enable a recurring run.
funcsdk.python.kfp.cli.recurring_run.get(ctx:click.Context, recurring_run_id:str)
Get information about a recurring run.
funcsdk.python.kfp.cli.recurring_run.list(ctx:click.Context, experiment_id:str, page_token:str, max_size:int, sort_by:str, filter:str)
List recurring runs.
funcsdk.python.kfp.cli.recurring_run.recurring_run()
Manage recurring run resources.
funcsdk.python.kfp.cli.run.archive(ctx:click.Context, run_id:str)
Archive a pipeline run.
funcsdk.python.kfp.cli.run.delete(ctx:click.Context, run_id:str)
Delete a pipeline run.
funcsdk.python.kfp.cli.run.get(ctx:click.Context, watch:bool, detail:bool, run_id:str)
Get information about a pipeline run.
funcsdk.python.kfp.cli.run.list(ctx:click.Context, experiment_id:str, page_token:str, max_size:int, sort_by:str, filter:str)
List pipeline runs.
funcsdk.python.kfp.cli.run.run()
Manage run resources.
funcsdk.python.kfp.cli.run.unarchive(ctx:click.Context, run_id:str)
Unarchive a pipeline run.
funcsdk.python.kfp.cli.utils.parsing.parse_parameter_value(value:str) -> Any
Parse a CLI string value into the appropriate Python type.
classsdk.python.kfp.client.auth.RedirectWSGIApp
WSGI app to handle the authorization redirect.
funcsdk.python.kfp.client.auth.fetch_auth_token_from_response(url:str) -> str
Fetches authorization code for OAuth2.0 Loopback flow.
funcsdk.python.kfp.client.auth.get_auth_code(client_id:str) -> Tuple[str, str]
Retrieves authorization token using Loopback flow.
funcsdk.python.kfp.client.auth.get_auth_token_from_sa(client_id:str) -> Optional[str]
Gets auth token from default service account.
funcsdk.python.kfp.client.auth.id_token_from_refresh_token(client_id:str, client_secret:str, refresh_token:str, audience:str) -> str
Returns ID token from refresh token.
funcsdk.python.kfp.client.auth.is_ipython() -> bool
Returns whether we are running in notebook.
classsdk.python.kfp.client.client.Client
The KFP SDK client for the Kubeflow Pipelines backend API.
methodsdk.python.kfp.client.client.Client.archive_experiment(experiment_id:str) -> dict
Archives an experiment.
methodsdk.python.kfp.client.client.Client.archive_run(run_id:str) -> dict
Archives a run.
methodsdk.python.kfp.client.client.Client.create_experiment(name:str, description:str=None, namespace:str=None) -> kfp_server_api.V2beta1Experiment
Creates a new experiment.
methodsdk.python.kfp.client.client.Client.delete_experiment(experiment_id:str) -> dict
Delete experiment.
methodsdk.python.kfp.client.client.Client.delete_job(job_id:str) -> dict
Deletes a job (recurring run).
methodsdk.python.kfp.client.client.Client.delete_pipeline(pipeline_id:str) -> dict
Deletes a pipeline.
methodsdk.python.kfp.client.client.Client.delete_pipeline_version(pipeline_id:str, pipeline_version_id:str) -> dict
Deletes a pipeline version.p.
methodsdk.python.kfp.client.client.Client.delete_recurring_run(recurring_run_id:str) -> dict
Deletes a recurring run.
methodsdk.python.kfp.client.client.Client.delete_run(run_id:str) -> dict
Deletes a run.
methodsdk.python.kfp.client.client.Client.disable_job(job_id:str) -> dict
Disables a job (recurring run).
methodsdk.python.kfp.client.client.Client.disable_recurring_run(recurring_run_id:str) -> dict
Disables a recurring run.
methodsdk.python.kfp.client.client.Client.enable_job(job_id:str) -> dict
Enables a job (recurring run).
methodsdk.python.kfp.client.client.Client.enable_recurring_run(recurring_run_id:str) -> dict
Enables a recurring run.
methodsdk.python.kfp.client.client.Client.get_experiment(experiment_id:Optional[str]=None, experiment_name:Optional[str]=None, namespace:Optional[str]=None) -> kfp_server_api.V2beta1Experiment
Gets details of an experiment.
methodsdk.python.kfp.client.client.Client.get_kfp_healthz(sleep_duration:int=5) -> kfp_server_api.V2beta1GetHealthzResponse
Gets healthz info for KFP deployment.
methodsdk.python.kfp.client.client.Client.get_pipeline(pipeline_id:str) -> kfp_server_api.V2beta1Pipeline
Gets pipeline details.
methodsdk.python.kfp.client.client.Client.get_pipeline_id(name:str) -> Optional[str]
Gets the ID of a pipeline by its name.
methodsdk.python.kfp.client.client.Client.get_pipeline_version(pipeline_id:str, pipeline_version_id:str) -> kfp_server_api.V2beta1PipelineVersion
Gets a pipeline version.
methodsdk.python.kfp.client.client.Client.get_recurring_run(recurring_run_id:str, job_id:Optional[str]=None) -> kfp_server_api.V2beta1RecurringRun
Gets recurring run details.
methodsdk.python.kfp.client.client.Client.get_run(run_id:str) -> kfp_server_api.V2beta1Run
Gets run details.
methodsdk.python.kfp.client.client.Client.get_user_namespace() -> str
Gets user namespace in context config.
methodsdk.python.kfp.client.client.Client.list_experiments(page_token:str='', page_size:int=10, sort_by:str='', namespace:Optional[str]=None, filter:Optional[str]=None) -> kfp_server_api.V2beta1ListExperimentsResponse
Lists experiments.
methodsdk.python.kfp.client.client.Client.list_pipeline_versions(pipeline_id:str, page_token:str='', page_size:int=10, sort_by:str='', filter:Optional[str]=None) -> kfp_server_api.V2beta1ListPipelineVersionsResponse
Lists pipeline versions.
methodsdk.python.kfp.client.client.Client.list_pipelines(page_token:str='', page_size:int=10, sort_by:str='', filter:Optional[str]=None, namespace:Optional[str]=None) -> kfp_server_api.V2beta1ListPipelinesResponse
Lists pipelines.
methodsdk.python.kfp.client.client.Client.list_runs(page_token:str='', page_size:int=10, sort_by:str='', experiment_id:Optional[str]=None, namespace:Optional[str]=None, filter:Optional[str]=None) -> kfp_server_api.V2beta1ListRunsResponse
List runs.
methodsdk.python.kfp.client.client.Client.set_user_namespace(namespace:str) -> None
Sets the namespace in the Kuberenetes cluster to use.
methodsdk.python.kfp.client.client.Client.terminate_run(run_id:str) -> dict
Terminates a run.
methodsdk.python.kfp.client.client.Client.unarchive_experiment(experiment_id:str) -> dict
Unarchives an experiment.
methodsdk.python.kfp.client.client.Client.unarchive_run(run_id:str) -> dict
Restores an archived run.
methodsdk.python.kfp.client.client.Client.upload_pipeline(pipeline_package_path:str, pipeline_name:Optional[str]=None, description:Optional[str]=None, namespace:Optional[str]=None) -> kfp_server_api.V2beta1Pipeline
Uploads a pipeline.
methodsdk.python.kfp.client.client.Client.wait_for_run_completion(run_id:str, timeout:int, sleep_duration:int=5) -> kfp_server_api.V2beta1Run
Waits for a run to complete.
funcsdk.python.kfp.client.token_credentials_base.read_token_from_file(path:Optional[str]=None) -> str
Reads a token found in some file.
funcsdk.python.kfp.compiler.compiler_utils.recursive_replace_placeholders(data:Any, old_value:str, new_value:str) -> Union[Dict, List, str]
Replaces the given data.
funcsdk.python.kfp.compiler.pipeline_spec_builder.build_component_spec_for_exit_task(task:pipeline_task.PipelineTask) -> pipeline_spec_pb2.ComponentSpec
Builds ComponentSpec for an exit task.
funcsdk.python.kfp.components.load_yaml_utilities.load_component_from_file(file_path:str) -> yaml_component.YamlComponent
Loads a component from a file.
funcsdk.python.kfp.components.load_yaml_utilities.load_component_from_text(text:str) -> yaml_component.YamlComponent
Loads a component from text.
funcsdk.python.kfp.components.load_yaml_utilities.load_component_from_url(url:str, auth:Optional[Tuple[str, str]]=None) -> yaml_component.YamlComponent
Loads a component from a URL.
classsdk.python.kfp.dsl.base_component.BaseComponent
Base class for a component.
classsdk.python.kfp.dsl.component_factory.ComponentInfo
A dataclass capturing registered components.
funcsdk.python.kfp.dsl.component_factory.get_name_to_specs(signature:inspect.Signature, containerized:bool=False) -> Tuple[Dict[str, Any], Dict[str, Any]]
Returns two dictionaries.
classsdk.python.kfp.dsl.container_component_class.ContainerComponent
Component defined via pre-built container.
classsdk.python.kfp.dsl.executor.Executor
Executor executes Python function components.
classsdk.python.kfp.dsl.for_loop.LoopArgumentVariable
Represents a subvariable for a loop argument.
classsdk.python.kfp.dsl.graph_component.GraphComponent
A component defined via @dsl.pipeline decorator.
classsdk.python.kfp.dsl.importer_component.ImporterComponent
Component defined via dsl.importer.
classsdk.python.kfp.dsl.kfp_config.KFPConfig
Class for managing KFP component configuration.
methodsdk.python.kfp.dsl.kfp_config.KFPConfig.add_component(function_name:str, path:pathlib.Path)
Adds a KFP component.
methodsdk.python.kfp.dsl.kfp_config.KFPConfig.get_components() -> Dict[str, pathlib.Path]
Returns a list of known KFP components.
methodsdk.python.kfp.dsl.kfp_config.KFPConfig.save()
Writes out a KFP config file.
classsdk.python.kfp.dsl.pipeline_channel.PipelineArtifactChannel
Represents a pipeline artifact channel.
classsdk.python.kfp.dsl.pipeline_channel.PipelineParameterChannel
Represents a pipeline parameter channel.
classsdk.python.kfp.dsl.pipeline_config.PipelineConfig
PipelineConfig contains pipeline-level config options.
classsdk.python.kfp.dsl.pipeline_context.Pipeline
A pipeline contains a list of tasks.
methodsdk.python.kfp.dsl.pipeline_context.Pipeline.add_task(task:pipeline_task.PipelineTask, add_to_group:bool) -> str
Adds a new task.
methodsdk.python.kfp.dsl.pipeline_context.Pipeline.get_default_pipeline()
Gets the default pipeline.
methodsdk.python.kfp.dsl.pipeline_context.Pipeline.get_next_group_id() -> str
Gets the next id for a new group.
methodsdk.python.kfp.dsl.pipeline_context.Pipeline.push_tasks_group(group:'tasks_group.TasksGroup')
Pushes a TasksGroup into the stack.
methodsdk.python.kfp.dsl.pipeline_context.Pipeline.remove_task_from_groups(task:pipeline_task.PipelineTask)
Removes a task from the pipeline.
classsdk.python.kfp.dsl.pipeline_task.PipelineTask
Represents a pipeline task (instantiated component).
methodsdk.python.kfp.dsl.pipeline_task.PipelineTask.add_node_selector_constraint(accelerator:str) -> 'PipelineTask'
Deprecated.
methodsdk.python.kfp.dsl.pipeline_task.PipelineTask.dependent_tasks() -> List[str]
A list of the dependent task names.
methodsdk.python.kfp.dsl.pipeline_task.PipelineTask.inputs() -> Dict[str, Union[type_utils.PARAMETER_TYPES, pipeline_channel.PipelineChannel]]
The inputs passed to the task.
methodsdk.python.kfp.dsl.pipeline_task.PipelineTask.name() -> str
The name of the task.
methodsdk.python.kfp.dsl.pipeline_task.PipelineTask.output() -> pipeline_channel.PipelineChannel
The single output of the task.
methodsdk.python.kfp.dsl.pipeline_task.PipelineTask.outputs() -> Mapping[str, pipeline_channel.PipelineChannel]
The dictionary of outputs of the task.
methodsdk.python.kfp.dsl.pipeline_task.PipelineTask.set_accelerator_limit(limit:Union[int, str, pipeline_channel.PipelineChannel]) -> 'PipelineTask'
Sets accelerator limit (maximum) for the task.
methodsdk.python.kfp.dsl.pipeline_task.PipelineTask.set_caching_options(enable_caching:bool, cache_key:Optional[str]=None) -> 'PipelineTask'
Sets caching options for the task.
methodsdk.python.kfp.dsl.pipeline_task.PipelineTask.set_cpu_limit(cpu:Union[str, pipeline_channel.PipelineChannel]) -> 'PipelineTask'
Sets CPU limit (maximum) for the task.
methodsdk.python.kfp.dsl.pipeline_task.PipelineTask.set_cpu_request(cpu:Union[str, pipeline_channel.PipelineChannel]) -> 'PipelineTask'
Sets CPU request (minimum) for the task.
methodsdk.python.kfp.dsl.pipeline_task.PipelineTask.set_display_name(name:str) -> 'PipelineTask'
Sets display name for the task.
methodsdk.python.kfp.dsl.pipeline_task.PipelineTask.set_env_variable(name:str, value:str) -> 'PipelineTask'
Sets environment variable for the task.
methodsdk.python.kfp.dsl.pipeline_task.PipelineTask.set_gpu_limit(gpu:str) -> 'PipelineTask'
Sets GPU limit (maximum) for the task.
methodsdk.python.kfp.dsl.pipeline_task.PipelineTask.set_memory_limit(memory:Union[str, pipeline_channel.PipelineChannel]) -> 'PipelineTask'
Sets memory limit (maximum) for the task.
methodsdk.python.kfp.dsl.pipeline_task.PipelineTask.set_memory_request(memory:Union[str, pipeline_channel.PipelineChannel]) -> 'PipelineTask'
Sets memory request (minimum) for the task.
methodsdk.python.kfp.dsl.pipeline_task.PipelineTask.set_retry(num_retries:int, backoff_duration:Optional[str]=None, backoff_factor:Optional[float]=None, backoff_max_duration:Optional[str]=None) -> 'PipelineTask'
Sets task retry parameters.
classsdk.python.kfp.dsl.placeholders.ConcatPlaceholder
Placeholder for concatenating multiple strings.
classsdk.python.kfp.dsl.python_component.PythonComponent
A component defined via Python function.
classsdk.python.kfp.dsl.structures.ComponentSpec
The definition of a component.
methodsdk.python.kfp.dsl.structures.ComponentSpec.from_v1_component_spec(v1_component_spec:v1_structures.ComponentSpec) -> 'ComponentSpec'
Converts V1 ComponentSpec to V2 ComponentSpec.
methodsdk.python.kfp.dsl.structures.ComponentSpec.from_yaml_documents(component_yaml:str) -> 'ComponentSpec'
Loads V1 or V2 component YAML into a ComponentSpec.
methodsdk.python.kfp.dsl.structures.ComponentSpec.save_to_component_yaml(output_file:str) -> None
Saves ComponentSpec into IR YAML file.
classsdk.python.kfp.dsl.structures.ContainerSpec
Container definition.
classsdk.python.kfp.dsl.structures.ContainerSpecImplementation
Container implementation definition.
classsdk.python.kfp.dsl.structures.Implementation
Implementation definition.
classsdk.python.kfp.dsl.structures.ImporterSpec
ImporterSpec definition.
classsdk.python.kfp.dsl.structures.InputSpec
Component input definitions.
classsdk.python.kfp.dsl.structures.OutputSpec
Component output definitions.
classsdk.python.kfp.dsl.structures.ResourceSpec
The resource requirements of a container execution.
classsdk.python.kfp.dsl.structures.RetryPolicy
The retry policy of a container execution.
classsdk.python.kfp.dsl.structures.TaskSpec
The spec of a pipeline task.
funcsdk.python.kfp.dsl.structures.convert_duration_to_seconds(duration:str) -> int
Converts a duration string to seconds.
funcsdk.python.kfp.dsl.structures.load_documents_from_yaml(component_yaml:str) -> Tuple[dict, dict]
Loads up to two YAML documents from a YAML string.
funcsdk.python.kfp.dsl.structures.normalize_time_string(duration:str) -> str
Normalizes a time string.
classsdk.python.kfp.dsl.task_config.TaskConfig
Configurations for a task.
classsdk.python.kfp.dsl.task_final_status.PipelineTaskFinalStatus
A final status of a pipeline task.
classsdk.python.kfp.dsl.tasks_group.Condition
Deprecated.
classsdk.python.kfp.dsl.tasks_group.TasksGroupType
Types of TasksGroup.
classsdk.python.kfp.dsl.types.artifact_types.Artifact
Represents a generic machine learning artifact.
classsdk.python.kfp.dsl.types.artifact_types.ClassificationMetrics
An artifact for storing classification metrics.
methodsdk.python.kfp.dsl.types.artifact_types.ClassificationMetrics.log_confusion_matrix(categories:List[str], matrix:List[List[int]]) -> None
Logs a confusion matrix to metadata.
methodsdk.python.kfp.dsl.types.artifact_types.ClassificationMetrics.log_confusion_matrix_row(row_category:str, row:List[float]) -> None
Logs a confusion matrix row to metadata.
methodsdk.python.kfp.dsl.types.artifact_types.ClassificationMetrics.log_roc_curve(fpr:List[float], tpr:List[float], threshold:List[float]) -> None
Logs an ROC curve to metadata.
methodsdk.python.kfp.dsl.types.artifact_types.ClassificationMetrics.set_confusion_matrix_categories(categories:List[str]) -> None
Stores confusion matrix categories to metadata.
classsdk.python.kfp.dsl.types.artifact_types.Dataset
An artifact representing a machine learning dataset.
classsdk.python.kfp.dsl.types.artifact_types.HTML
An artifact representing an HTML file.
classsdk.python.kfp.dsl.types.artifact_types.Markdown
An artifact representing a markdown file.
classsdk.python.kfp.dsl.types.artifact_types.Metrics
An artifact for storing key-value scalar metrics.
methodsdk.python.kfp.dsl.types.artifact_types.Metrics.log_metric(metric:str, value:float) -> None
Sets a custom scalar metric in the artifact's metadata.
classsdk.python.kfp.dsl.types.artifact_types.Model
An artifact representing a machine learning model.
funcsdk.python.kfp.dsl.types.custom_artifact_types.get_full_qualname_for_artifact(obj:type) -> str
Gets the fully qualified name for an object.
classsdk.python.kfp.dsl.types.type_annotations.EmbeddedAnnotation
Marker type for embedded runtime-only inputs.
classsdk.python.kfp.dsl.types.type_annotations.InputAnnotation
Marker type for input artifacts.
classsdk.python.kfp.dsl.types.type_annotations.OutputAnnotation
Marker type for output artifacts.
funcsdk.python.kfp.dsl.types.type_annotations.get_inner_type(annotation:Any) -> Optional[Any]
Returns the inner type of a generic annotation.
funcsdk.python.kfp.dsl.types.type_annotations.get_short_type_name(type_name:str) -> str
Extracts the short form type name.
funcsdk.python.kfp.dsl.types.type_annotations.is_artifact_wrapped_in_Input(typ:Any) -> bool
Returns True if typ is of type Input[T].
funcsdk.python.kfp.dsl.types.type_annotations.is_artifact_wrapped_in_Output(typ:Any) -> bool
Returns True if typ is of type Output[T].
funcsdk.python.kfp.dsl.types.type_annotations.maybe_strip_optional_from_annotation(annotation:T) -> T
Strips 'Optional' from 'Optional[<type>]' if applicable.
funcsdk.python.kfp.dsl.types.type_utils.get_canonical_name_for_outer_generic(type_name:Any) -> str
Maps a complex/nested type name back to a canonical type.
funcsdk.python.kfp.dsl.types.type_utils.get_canonical_type_name_for_type(typ:Type) -> Optional[str]
Find the canonical type name for a given type.
funcsdk.python.kfp.dsl.types.type_utils.get_parameter_type_name(param_type:Optional[Union[Type, str, dict]]) -> str
Gets the parameter type name.
funcsdk.python.kfp.dsl.types.type_utils.is_task_config_type(type_name:Optional[Union[str, dict]]) -> bool
Check if a ComponentSpec I/O type is TaskConfig.
funcsdk.python.kfp.dsl.utils.make_name_unique_by_adding_index(name:str, collection:List[str], delimiter:str) -> str
Makes a unique name by adding index.
funcsdk.python.kfp.dsl.utils.maybe_rename_for_k8s(name:str) -> str
Cleans and converts a name to be k8s compatible.
funcsdk.python.kfp.dsl.utils.sanitize_component_name(name:str) -> str
Sanitizes component name.
funcsdk.python.kfp.dsl.utils.sanitize_executor_label(label:str) -> str
Sanitizes executor label.
funcsdk.python.kfp.dsl.utils.sanitize_input_name(name:str) -> str
Sanitizes input name.
funcsdk.python.kfp.dsl.utils.sanitize_task_name(name:str) -> str
Sanitizes task name.
funcsdk.python.kfp.dsl.utils.validate_pipeline_name(name:str) -> None
Validate pipeline name.
classsdk.python.kfp.dsl.v1_structures.AndPredicate
Represents the "and" logical operation.
classsdk.python.kfp.dsl.v1_structures.ComponentReference
Component reference.
classsdk.python.kfp.dsl.v1_structures.ComponentSpec
Component specification.
classsdk.python.kfp.dsl.v1_structures.ContainerImplementation
Represents the container component implementation.
classsdk.python.kfp.dsl.v1_structures.ContainerSpec
Describes the container component implementation.
classsdk.python.kfp.dsl.v1_structures.EqualsPredicate
Represents the "equals" comparison predicate.
classsdk.python.kfp.dsl.v1_structures.GraphImplementation
Represents the graph component implementation.
classsdk.python.kfp.dsl.v1_structures.GraphSpec
Describes the graph component implementation.
classsdk.python.kfp.dsl.v1_structures.GreaterThanPredicate
Represents the "greater than" comparison predicate.
classsdk.python.kfp.dsl.v1_structures.InputSpec
Describes the component input specification.
classsdk.python.kfp.dsl.v1_structures.LessThenPredicate
Represents the "less than" comparison predicate.
classsdk.python.kfp.dsl.v1_structures.NotEqualsPredicate
Represents the "not equals" comparison predicate.
classsdk.python.kfp.dsl.v1_structures.NotPredicate
Represents the "not" logical operation.
classsdk.python.kfp.dsl.v1_structures.OrPredicate
Represents the "or" logical operation.
classsdk.python.kfp.dsl.v1_structures.OutputSpec
Describes the component output specification.
classsdk.python.kfp.dsl.v1_structures.TaskSpec
Task specification.
classsdk.python.kfp.dsl.yaml_component.YamlComponent
A component loaded from a YAML file.
methodsdk.python.kfp.dsl.yaml_component.YamlComponent.execute(*args, **kwargs)
Not implemented.
funcsdk.python.kfp.kubeflow_client.backends.kubernetes.utils.discover_host(namespace:str) -> str
Auto-discover the KFP API server endpoint.
classsdk.python.kfp.local.cache.LocalCache
Thread-safe, file-backed cache for local task outputs.
methodsdk.python.kfp.local.cache.LocalCache.get(key:str) -> Optional[Dict[str, Any]]
Retrieves cached outputs for `key`, or None on cache miss.
methodsdk.python.kfp.local.cache.LocalCache.put(key:str, outputs:Dict[str, Any]) -> None
Persists `outputs` under `key`, atomically.
funcsdk.python.kfp.local.cache.reset_local_cache_singleton() -> None
Test hook: clears the module-level cache singleton.
classsdk.python.kfp.local.config.LocalRunnerType
The ABC for user-facing Runner configurations.
classsdk.python.kfp.local.docker_task_handler.DockerTaskHandler
The task handler corresponding to DockerRunner.
funcsdk.python.kfp.local.executor_input_utils.dict_to_protobuf_struct(d:Dict[str, Any]) -> struct_pb2.Struct
Converts a Python dictionary to a prototobuf Struct.
funcsdk.python.kfp.local.executor_output_utils.load_executor_output(executor_output_path:str) -> pipeline_spec_pb2.ExecutorOutput
Loads the ExecutorOutput message from a path.
funcsdk.python.kfp.local.executor_output_utils.pb2_struct_to_python(struct:struct_pb2.Struct) -> Dict[str, Any]
Converts protobuf Struct to a dict.
funcsdk.python.kfp.local.executor_output_utils.pb2_value_to_python(value:struct_pb2.Value) -> Any
Converts protobuf Value to the corresponding Python type.
classsdk.python.kfp.local.io.IOStore
In-memory store of a DAG's parameter/artifact state.
methodsdk.python.kfp.local.io.IOStore.get_task_output(task_name:str, key:str) -> Any
Get the value of an upstream task output.
methodsdk.python.kfp.local.io.IOStore.get_task_status(task_name:str) -> str
Get the final status of a task.
methodsdk.python.kfp.local.io.IOStore.put_task_output(task_name:str, key:str, value:Any) -> None
Persist the value of an upstream task output.
methodsdk.python.kfp.local.io.IOStore.put_task_status(task_name:str, task_status:str) -> None
Persist the final status of a task.
funcsdk.python.kfp.local.placeholder_utils.make_random_id() -> str
Makes a random 8 digit integer as a string.
classsdk.python.kfp.local.task_handler_interface.ITaskHandler
Interface for a TaskHandler.
methodsdk.python.kfp.local.task_handler_interface.ITaskHandler.run() -> status.Status
Runs the task and returns the status.
funcsdk.python.kfp.local.testing_utilities.write_proto_to_json_file(proto_message:message.Message, file_path:str) -> None
Writes proto_message to file_path as JSON.
classsdk.python.kfp.registry.registry_client.ApiAuth
Class for registry authentication using an API token.
classsdk.python.kfp.registry.registry_client.RegistryClient
Class for communicating with registry hosts.
methodsdk.python.kfp.registry.registry_client.RegistryClient.create_tag(package_name:str, version:str, tag:str) -> Dict[str, Any]
Creates a tag on a package version.
methodsdk.python.kfp.registry.registry_client.RegistryClient.delete_package(package_name:str) -> bool
Deletes a package.
methodsdk.python.kfp.registry.registry_client.RegistryClient.delete_tag(package_name:str, tag:str) -> Dict[str, Any]
Deletes package tag.
methodsdk.python.kfp.registry.registry_client.RegistryClient.delete_version(package_name:str, version:str) -> bool
Deletes package version.
methodsdk.python.kfp.registry.registry_client.RegistryClient.download_pipeline(package_name:str, version:Optional[str]=None, tag:Optional[str]=None, file_name:Optional[str]=None) -> str
Downloads a pipeline.
methodsdk.python.kfp.registry.registry_client.RegistryClient.get_package(package_name:str) -> Dict[str, Any]
Gets package metadata.
methodsdk.python.kfp.registry.registry_client.RegistryClient.get_tag(package_name:str, tag:str) -> Dict[str, Any]
Gets tag metadata.
methodsdk.python.kfp.registry.registry_client.RegistryClient.get_version(package_name:str, version:str) -> Dict[str, Any]
Gets package version metadata.
methodsdk.python.kfp.registry.registry_client.RegistryClient.list_packages() -> List[dict]
Lists packages.
methodsdk.python.kfp.registry.registry_client.RegistryClient.list_tags(package_name:str) -> List[dict]
Lists package tags.
methodsdk.python.kfp.registry.registry_client.RegistryClient.list_versions(package_name:str) -> List[dict]
Lists package versions.
methodsdk.python.kfp.registry.registry_client.RegistryClient.update_tag(package_name:str, version:str, tag:str) -> Dict[str, Any]
Updates a tag to another package version.
methodsdk.python.kfp.registry.registry_client.RegistryClient.upload_pipeline(file_name:str, tags:Optional[Union[str, List[str]]]=None, extra_headers:Optional[dict]=None) -> Tuple[str, str]
Uploads the pipeline.
functest_data.sdk_compiled_pipelines.valid.component_with_metadata_fields.dataset_joiner(dataset_a:Input[Dataset], dataset_b:Input[Dataset], out_dataset:Output[Dataset]) -> str
Concatenate dataset_a and dataset_b.
functest_data.sdk_compiled_pipelines.valid.critical.flip_coin.flip_coin() -> str
Flip a coin and output heads or tails randomly.
functest_data.sdk_compiled_pipelines.valid.critical.flip_coin.print_msg(msg:str)
Print a message.
functest_data.sdk_compiled_pipelines.valid.critical.flip_coin.random_num(low:int, high:int) -> int
Generate a random number between low and high.
functest_data.sdk_compiled_pipelines.valid.critical.pipeline_with_importer_workspace.train(dataset:dsl.Input[dsl.Dataset]) -> NamedTuple('Outputs', [('scalar', str), ('message', str)])
Dummy Training step.
functest_data.sdk_compiled_pipelines.valid.critical.pipeline_with_workspace.write_to_workspace(workspace_path:str) -> str
Write a file to the workspace.
functest_data.sdk_compiled_pipelines.valid.essential.pipeline_with_condition.print_op(msg:str)
Print a message.
functest_data.sdk_compiled_pipelines.valid.essential.pipeline_with_nested_conditions.print_op(msg:str)
Print a message.
functest_data.sdk_compiled_pipelines.valid.failing.fail_v2.fail()
Fails
functest_data.sdk_compiled_pipelines.valid.failing.pipeline_with_exit_handler.fail_op(message:str)
Fails.
functest_data.sdk_compiled_pipelines.valid.failing.pipeline_with_exit_handler.print_op(message:str)
Prints a message.
functest_data.sdk_compiled_pipelines.valid.failing.pipeline_with_multiple_exit_handlers.fail_op(message:str)
Fails.
functest_data.sdk_compiled_pipelines.valid.failing.pipeline_with_multiple_exit_handlers.print_op(message:str)
Prints a message.
functest_data.sdk_compiled_pipelines.valid.parallel_and_nested.nested_parallel_for_secret.emit_secret_name() -> str
Emits the secret name dynamically.
functest_data.sdk_compiled_pipelines.valid.pipeline_as_exit_task.exit_op(status:PipelineTaskFinalStatus)
Checks pipeline run status.
functest_data.sdk_compiled_pipelines.valid.pipeline_as_exit_task.fail_op(message:str)
Fails.
functest_data.sdk_compiled_pipelines.valid.pipeline_as_exit_task.print_op(message:str)
Prints a message.
functest_data.sdk_compiled_pipelines.valid.pipeline_with_importer.train(dataset:Input[Dataset]) -> NamedTuple('Outputs', [('scalar', str), ('model', Model)])
Dummy Training step.
functest_data.sdk_compiled_pipelines.valid.pipeline_with_metadata_fields.dataset_joiner(dataset_a:Input[Dataset], dataset_b:Input[Dataset], out_dataset:Output[Dataset]) -> str
Concatenate dataset_a and dataset_b.
functest_data.sdk_compiled_pipelines.valid.pipeline_with_metadata_fields.str_to_dataset(string:str, dataset:Output[Dataset])
Convert string to dataset.
functest_data.sdk_compiled_pipelines.valid.pipeline_with_task_final_status.exit_op(user_input:str, status:PipelineTaskFinalStatus)
Checks pipeline run status.
functest_data.sdk_compiled_pipelines.valid.pipeline_with_task_final_status.fail_op(message:str)
Fails.
functest_data.sdk_compiled_pipelines.valid.pipeline_with_task_final_status.print_op(message:str)
Prints a message.
functest_data.sdk_compiled_pipelines.valid.pipeline_with_task_using_ignore_upstream_failure.fail_op(message:str) -> str
Fails.
functest_data.sdk_compiled_pipelines.valid.pipeline_with_task_using_ignore_upstream_failure.print_op(message:str='default')
Prints a message.
functest_data.sdk_compiled_pipelines.valid.pvc_mount_subpath.read_from_logs() -> None
Reads data from the logs subdirectory.
functest_data.sdk_compiled_pipelines.valid.pvc_mount_subpath.read_from_models() -> None
Reads data from the models subdirectory.
functest_data.sdk_compiled_pipelines.valid.pvc_mount_subpath.write_to_logs() -> None
Writes data to the logs subdirectory.
functest_data.sdk_compiled_pipelines.valid.pvc_mount_subpath.write_to_models() -> None
Writes data to the models subdirectory.
functest_data.sdk_compiled_pipelines.valid.take_nap.take_nap(naptime_secs:int) -> str
Sleeps for secs
functest_data.sdk_compiled_pipelines.valid.take_nap.wake_up(message:str)
Wakes up from nap printing a message
functest_data.sdk_compiled_pipelines.valid.take_nap_pipeline_root.take_nap(naptime_secs:int) -> str
Sleeps for secs
functest_data.sdk_compiled_pipelines.valid.take_nap_pipeline_root.wake_up(message:str)
Wakes up from nap printing a message

この情報について

掲載しているシグネチャは kubeflow/pipelines の公開ソースコードを Python の ast モジュールで静的解析し、引数名・デフォルト値・ 型注釈・戻り値型をそのまま抽出したものです。実装コードは保存していません。 詳しくは仕組みの解説をご覧ください。

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