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PipelineJob(
display_name: str,
template_path: str,
job_id: typing.Optional[str] = None,
pipeline_root: typing.Optional[str] = None,
parameter_values: typing.Optional[typing.Dict[str, typing.Any]] = None,
input_artifacts: typing.Optional[typing.Dict[str, str]] = None,
enable_caching: typing.Optional[bool] = None,
encryption_spec_key_name: typing.Optional[str] = None,
labels: typing.Optional[typing.Dict[str, str]] = None,
credentials: typing.Optional[google.auth.credentials.Credentials] = None,
project: typing.Optional[str] = None,
location: typing.Optional[str] = None,
failure_policy: typing.Optional[str] = None,
)
Retrieves a PipelineJob resource and instantiates its representation.
Parameters |
|
---|---|
Name | Description |
display_name |
str
Required. The user-defined name of this Pipeline. |
template_path |
str
Required. The path of PipelineJob or PipelineSpec JSON or YAML file. It can be a local path, a Google Cloud Storage URI (e.g. "gs://project.name"), an Artifact Registry URI (e.g. "https://us-central1-kfp.pkg.dev/proj/repo/pack/latest"), or an HTTPS URI. |
job_id |
str
Optional. The unique ID of the job run. If not specified, pipeline name + timestamp will be used. |
pipeline_root |
str
Optional. The root of the pipeline outputs. If not set, the staging bucket set in aiplatform.init will be used. If that's not set a pipeline-specific artifacts bucket will be used. |
parameter_values |
Dict[str, Any]
Optional. The mapping from runtime parameter names to its values that control the pipeline run. |
input_artifacts |
Dict[str, str]
Optional. The mapping from the runtime parameter name for this artifact to its resource id. For example: "vertex_model":"456". Note: full resource name ("projects/123/locations/us-central1/metadataStores/default/artifacts/456") cannot be used. |
enable_caching |
bool
Optional. Whether to turn on caching for the run. If this is not set, defaults to the compile time settings, which are True for all tasks by default, while users may specify different caching options for individual tasks. If this is set, the setting applies to all tasks in the pipeline. Overrides the compile time settings. |
encryption_spec_key_name |
str
Optional. The Cloud KMS resource identifier of the customer managed encryption key used to protect the job. Has the form: |
labels |
Dict[str, str]
Optional. The user defined metadata to organize PipelineJob. |
credentials |
auth_credentials.Credentials
Optional. Custom credentials to use to create this PipelineJob. Overrides credentials set in aiplatform.init. |
project |
str
Optional. The project that you want to run this PipelineJob in. If not set, the project set in aiplatform.init will be used. |
location |
str
Optional. Location to create PipelineJob. If not set, location set in aiplatform.init will be used. |
failure_policy |
str
Optional. The failure policy - "slow" or "fast". Currently, the default of a pipeline is that the pipeline will continue to run until no more tasks can be executed, also known as PIPELINE_FAILURE_POLICY_FAIL_SLOW (corresponds to "slow"). However, if a pipeline is set to PIPELINE_FAILURE_POLICY_FAIL_FAST (corresponds to "fast"), it will stop scheduling any new tasks when a task has failed. Any scheduled tasks will continue to completion. |
Properties
create_time
Time this resource was created.
display_name
Display name of this resource.
encryption_spec
Customer-managed encryption key options for this Vertex AI resource.
If this is set, then all resources created by this Vertex AI resource will be encrypted with the provided encryption key.
gca_resource
The underlying resource proto representation.
has_failed
Returns True if pipeline has failed.
False otherwise.
labels
User-defined labels containing metadata about this resource.
Read more about labels at https://goo.gl/xmQnxf
name
Name of this resource.
resource_name
Full qualified resource name.
state
Current pipeline state.
update_time
Time this resource was last updated.
Methods
__init_subclass__
__init_subclass__(
*,
experiment_loggable_schemas: typing.Tuple[
google.cloud.aiplatform.metadata.experiment_resources._ExperimentLoggableSchema
],
**kwargs
)
Register the metadata_schema for the subclass so Experiment can use it to retrieve the associated types.
usage:
class PipelineJob(..., experiment_loggable_schemas= (_ExperimentLoggableSchema(title='system.PipelineRun'), )
batch_cancel
batch_cancel(
project: str, location: str, names: typing.List[str]
) -> google.api_core.operation.Operation
Example Usage: pipeline_job = aiplatform.PipelineJob( display_name='job_display_name', template_path='your_pipeline.yaml', ) pipeline_job.batch_cancel( project='your_project_id', location='your_location', names=['pipeline_job_name', 'pipeline_job_name2'] )
Parameters | |
---|---|
Name | Description |
project |
str
Required. The project id of the PipelineJobs to batch delete. |
location |
str
Required. The location of the PipelineJobs to batch delete. |
names |
typing.List[str]
Required. The names of the PipelineJobs to cancel. A maximum of 32 PipelineJobs can be cancelled in a batch. |
Returns | |
---|---|
Type | Description |
operation (Operation) |
An object representing a long-running operation. |
batch_delete
batch_delete(
project: str, location: str, names: typing.List[str]
) -> google.cloud.aiplatform_v1.types.pipeline_service.BatchDeletePipelineJobsResponse
Example Usage: pipeline_job = aiplatform.PipelineJob( display_name='job_display_name', template_path='your_pipeline.yaml', ) pipeline_job.batch_delete( project='your_project_id', location='your_location', names=['pipeline_job_name', 'pipeline_job_name2'] )
Parameters | |
---|---|
Name | Description |
project |
str
Required. The project id of the PipelineJobs to batch delete. |
location |
str
Required. The location of the PipelineJobs to batch delete. |
names |
typing.List[str]
Required. The names of the PipelineJobs to delete. A maximum of 32 PipelineJobs can be deleted in a batch. |
cancel
cancel() -> None
Starts asynchronous cancellation on the PipelineJob. The server
makes a best effort to cancel the job, but success is not guaranteed.
On successful cancellation, the PipelineJob is not deleted; instead it
becomes a job with state set to CANCELLED
.
clone
clone(
display_name: typing.Optional[str] = None,
job_id: typing.Optional[str] = None,
pipeline_root: typing.Optional[str] = None,
parameter_values: typing.Optional[typing.Dict[str, typing.Any]] = None,
input_artifacts: typing.Optional[typing.Dict[str, str]] = None,
enable_caching: typing.Optional[bool] = None,
encryption_spec_key_name: typing.Optional[str] = None,
labels: typing.Optional[typing.Dict[str, str]] = None,
credentials: typing.Optional[google.auth.credentials.Credentials] = None,
project: typing.Optional[str] = None,
location: typing.Optional[str] = None,
) -> google.cloud.aiplatform.pipeline_jobs.PipelineJob
Returns a new PipelineJob object with the same settings as the original one.
Parameters | |
---|---|
Name | Description |
display_name |
str
Optional. The user-defined name of this cloned Pipeline. If not specified, original pipeline display name will be used. |
job_id |
str
Optional. The unique ID of the job run. If not specified, "cloned" + pipeline name + timestamp will be used. |
pipeline_root |
str
Optional. The root of the pipeline outputs. Default to be the same staging bucket as original pipeline. |
parameter_values |
Dict[str, Any]
Optional. The mapping from runtime parameter names to its values that control the pipeline run. Defaults to be the same values as original PipelineJob. |
input_artifacts |
Dict[str, str]
Optional. The mapping from the runtime parameter name for this artifact to its resource id. Defaults to be the same values as original PipelineJob. For example: "vertex_model":"456". Note: full resource name ("projects/123/locations/us-central1/metadataStores/default/artifacts/456") cannot be used. |
enable_caching |
bool
Optional. Whether to turn on caching for the run. If this is not set, defaults to be the same as original pipeline. If this is set, the setting applies to all tasks in the pipeline. |
encryption_spec_key_name |
str
Optional. The Cloud KMS resource identifier of the customer managed encryption key used to protect the job. Has the form: |
labels |
Dict[str, str]
Optional. The user defined metadata to organize PipelineJob. |
credentials |
auth_credentials.Credentials
Optional. Custom credentials to use to create this PipelineJob. Overrides credentials set in aiplatform.init. |
project |
str
Optional. The project that you want to run this PipelineJob in. If not set, the project set in original PipelineJob will be used. |
location |
str
Optional. Location to create PipelineJob. If not set, location set in original PipelineJob will be used. |
Exceptions | |
---|---|
Type | Description |
ValueError |
If job_id or labels have incorrect format. |
create_schedule
create_schedule(
cron: str,
display_name: str,
start_time: typing.Optional[str] = None,
end_time: typing.Optional[str] = None,
allow_queueing: bool = False,
max_run_count: typing.Optional[int] = None,
max_concurrent_run_count: int = 1,
service_account: typing.Optional[str] = None,
network: typing.Optional[str] = None,
create_request_timeout: typing.Optional[float] = None,
) -> google.cloud.aiplatform.pipeline_job_schedules.PipelineJobSchedule
Creates a PipelineJobSchedule directly from a PipelineJob.
Example Usage:
pipeline_job = aiplatform.PipelineJob( display_name='job_display_name', template_path='your_pipeline.yaml', ) pipeline_job.run() pipeline_job_schedule = pipeline_job.create_schedule( cron='* * * * *', display_name='schedule_display_name', )
Parameters | |
---|---|
Name | Description |
cron |
str
Required. Time specification (cron schedule expression) to launch scheduled runs. To explicitly set a timezone to the cron tab, apply a prefix: "CRON_TZ=${IANA_TIME_ZONE}" or "TZ=${IANA_TIME_ZONE}". The ${IANA_TIME_ZONE} may only be a valid string from IANA time zone database. For example, "CRON_TZ=America/New_York 1 * * * *", or "TZ=America/New_York 1 * * * *". |
display_name |
str
Required. The user-defined name of this PipelineJobSchedule. |
start_time |
str
Optional. Timestamp after which the first run can be scheduled. If unspecified, it defaults to the schedule creation timestamp. |
end_time |
str
Optional. Timestamp after which no more runs will be scheduled. If unspecified, then runs will be scheduled indefinitely. |
allow_queueing |
bool
Optional. Whether new scheduled runs can be queued when max_concurrent_runs limit is reached. |
max_run_count |
int
Optional. Maximum run count of the schedule. If specified, The schedule will be completed when either started_run_count >= max_run_count or when end_time is reached. Must be positive and <= 2^63-1. |
max_concurrent_run_count |
int
Optional. Maximum number of runs that can be started concurrently for this PipelineJobSchedule. |
service_account |
str
Optional. Specifies the service account for workload run-as account. Users submitting jobs must have act-as permission on this run-as account. |
network |
str
Optional. The full name of the Compute Engine network to which the job should be peered. For example, projects/12345/global/networks/myVPC. Private services access must already be configured for the network. If left unspecified, the network set in aiplatform.init will be used. Otherwise, the job is not peered with any network. |
create_request_timeout |
float
Optional. The timeout for the create request in seconds. |
delete
delete(sync: bool = True) -> None
Deletes this Vertex AI resource. WARNING: This deletion is permanent.
Parameter | |
---|---|
Name | Description |
sync |
bool
Whether to execute this deletion synchronously. If False, this method will be executed in concurrent Future and any downstream object will be immediately returned and synced when the Future has completed. |
done
done() -> bool
Helper method that return True is PipelineJob is done. False otherwise.
from_pipeline_func
from_pipeline_func(
pipeline_func: typing.Callable,
parameter_values: typing.Optional[typing.Dict[str, typing.Any]] = None,
input_artifacts: typing.Optional[typing.Dict[str, str]] = None,
output_artifacts_gcs_dir: typing.Optional[str] = None,
enable_caching: typing.Optional[bool] = None,
context_name: typing.Optional[str] = "pipeline",
display_name: typing.Optional[str] = None,
labels: typing.Optional[typing.Dict[str, str]] = None,
job_id: typing.Optional[str] = None,
project: typing.Optional[str] = None,
location: typing.Optional[str] = None,
credentials: typing.Optional[google.auth.credentials.Credentials] = None,
encryption_spec_key_name: typing.Optional[str] = None,
) -> google.cloud.aiplatform.pipeline_jobs.PipelineJob
Creates PipelineJob by compiling a pipeline function.
Parameters | |
---|---|
Name | Description |
pipeline_func |
Callable
Required. A pipeline function to compile. A pipeline function creates instances of components and connects component inputs to outputs. |
parameter_values |
Dict[str, Any]
Optional. The mapping from runtime parameter names to its values that control the pipeline run. |
input_artifacts |
Dict[str, str]
Optional. The mapping from the runtime parameter name for this artifact to its resource id. For example: "vertex_model":"456". Note: full resource name ("projects/123/locations/us-central1/metadataStores/default/artifacts/456") cannot be used. |
output_artifacts_gcs_dir |
str
Optional. The GCS location of the pipeline outputs. A GCS bucket for artifacts will be created if not specified. |
enable_caching |
bool
Optional. Whether to turn on caching for the run. If this is not set, defaults to the compile time settings, which are True for all tasks by default, while users may specify different caching options for individual tasks. If this is set, the setting applies to all tasks in the pipeline. Overrides the compile time settings. |
context_name |
str
Optional. The name of metadata context. Used for cached execution reuse. |
display_name |
str
Optional. The user-defined name of this Pipeline. |
labels |
Dict[str, str]
Optional. The user defined metadata to organize PipelineJob. |
job_id |
str
Optional. The unique ID of the job run. If not specified, pipeline name + timestamp will be used. |
project |
str
Optional. The project that you want to run this PipelineJob in. If not set, the project set in aiplatform.init will be used. |
location |
str
Optional. Location to create PipelineJob. If not set, location set in aiplatform.init will be used. |
credentials |
auth_credentials.Credentials
Optional. Custom credentials to use to create this PipelineJob. Overrides credentials set in aiplatform.init. |
encryption_spec_key_name |
str
Optional. The Cloud KMS resource identifier of the customer managed encryption key used to protect the job. Has the form: |
Exceptions | |
---|---|
Type | Description |
ValueError |
If job_id or labels have incorrect format. |
get
get(
resource_name: str,
project: typing.Optional[str] = None,
location: typing.Optional[str] = None,
credentials: typing.Optional[google.auth.credentials.Credentials] = None,
) -> google.cloud.aiplatform.pipeline_jobs.PipelineJob
Get a Vertex AI Pipeline Job for the given resource_name.
Parameters | |
---|---|
Name | Description |
resource_name |
str
Required. A fully-qualified resource name or ID. |
project |
str
Optional. Project to retrieve dataset from. If not set, project set in aiplatform.init will be used. |
location |
str
Optional. Location to retrieve dataset from. If not set, location set in aiplatform.init will be used. |
credentials |
auth_credentials.Credentials
Optional. Custom credentials to use to upload this model. Overrides credentials set in aiplatform.init. |
get_associated_experiment
get_associated_experiment() -> (
typing.Optional[google.cloud.aiplatform.metadata.experiment_resources.Experiment]
)
Gets the aiplatform.Experiment associated with this PipelineJob, or None if this PipelineJob is not associated with an experiment.
list
list(
filter: typing.Optional[str] = None,
order_by: typing.Optional[str] = None,
enable_simple_view: bool = False,
project: typing.Optional[str] = None,
location: typing.Optional[str] = None,
credentials: typing.Optional[google.auth.credentials.Credentials] = None,
) -> typing.List[google.cloud.aiplatform.pipeline_jobs.PipelineJob]
List all instances of this PipelineJob resource.
Example Usage:
aiplatform.PipelineJob.list( filter='display_name="experiment_a27"', order_by='create_time desc' )
Parameters | |
---|---|
Name | Description |
filter |
str
Optional. An expression for filtering the results of the request. For field names both snake_case and camelCase are supported. |
order_by |
str
Optional. A comma-separated list of fields to order by, sorted in ascending order. Use "desc" after a field name for descending. Supported fields: |
enable_simple_view |
bool
Optional. Whether to pass the |
project |
str
Optional. Project to retrieve list from. If not set, project set in aiplatform.init will be used. |
location |
str
Optional. Location to retrieve list from. If not set, location set in aiplatform.init will be used. |
credentials |
auth_credentials.Credentials
Optional. Custom credentials to use to retrieve list. Overrides credentials set in aiplatform.init. |
run
run(
service_account: typing.Optional[str] = None,
network: typing.Optional[str] = None,
reserved_ip_ranges: typing.Optional[typing.List[str]] = None,
sync: typing.Optional[bool] = True,
create_request_timeout: typing.Optional[float] = None,
) -> None
Run this configured PipelineJob and monitor the job until completion.
Parameters | |
---|---|
Name | Description |
service_account |
str
Optional. Specifies the service account for workload run-as account. Users submitting jobs must have act-as permission on this run-as account. |
network |
str
Optional. The full name of the Compute Engine network to which the job should be peered. For example, projects/12345/global/networks/myVPC. Private services access must already be configured for the network. If left unspecified, the network set in aiplatform.init will be used. Otherwise, the job is not peered with any network. |
reserved_ip_ranges |
List[str]
Optional. A list of names for the reserved IP ranges under the VPC network that can be used for this PipelineJob's workload. For example: ['vertex-ai-ip-range']. |
sync |
bool
Optional. Whether to execute this method synchronously. If False, this method will unblock and it will be executed in a concurrent Future. |
create_request_timeout |
float
Optional. The timeout for the create request in seconds. |
submit
submit(
service_account: typing.Optional[str] = None,
network: typing.Optional[str] = None,
reserved_ip_ranges: typing.Optional[typing.List[str]] = None,
create_request_timeout: typing.Optional[float] = None,
*,
experiment: typing.Optional[
typing.Union[
google.cloud.aiplatform.metadata.experiment_resources.Experiment, str
]
] = None
) -> None
Run this configured PipelineJob.
Parameters | |
---|---|
Name | Description |
service_account |
str
Optional. Specifies the service account for workload run-as account. Users submitting jobs must have act-as permission on this run-as account. |
network |
str
Optional. The full name of the Compute Engine network to which the job should be peered. For example, projects/12345/global/networks/myVPC. Private services access must already be configured for the network. If left unspecified, the network set in aiplatform.init will be used. Otherwise, the job is not peered with any network. |
reserved_ip_ranges |
List[str]
Optional. A list of names for the reserved IP ranges under the VPC network that can be used for this PipelineJob's workload. For example: ['vertex-ai-ip-range']. If left unspecified, the job will be deployed to any IP ranges under the provided VPC network. |
create_request_timeout |
float
Optional. The timeout for the create request in seconds. |
experiment |
Union[str, experiments_resource.Experiment]
Optional. The Vertex AI experiment name or instance to associate to this PipelineJob. Metrics produced by the PipelineJob as system.Metric Artifacts will be associated as metrics to the current Experiment Run. Pipeline parameters will be associated as parameters to the current Experiment Run. |
to_dict
to_dict() -> typing.Dict[str, typing.Any]
Returns the resource proto as a dictionary.
wait
wait()
Wait for this PipelineJob to complete.
wait_for_resource_creation
wait_for_resource_creation() -> None
Waits until resource has been created.