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feat(components): Introduce max_wait_duration to custom job to v1 GCPC custom job components/utils #11452

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feat(components): Introduce max_wait_duration to custom job to v1 GCPC custom job components/utils #11452

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3 changes: 3 additions & 0 deletions components/google-cloud/RELEASE.md
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Original file line number Diff line number Diff line change
@@ -1,5 +1,8 @@
## Upcoming release

## Release 2.19.0
* Add max_wait_duration to v1 GCPC custom job components/utils

## Release 2.18.0
* Remove default prediction column names in `v1.model_evaluation.regression_component` component to fix pipeline errors when using bigquery data source.
* Add reservation_affinition support in `v1.create_custom_training_job_from_component`.
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3 changes: 3 additions & 0 deletions components/google-cloud/google_cloud_pipeline_components/v1/custom_job/component.py
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Expand Up @@ -39,6 +39,7 @@ def custom_training_job(
persistent_resource_id: str = _placeholders.PERSISTENT_RESOURCE_ID_PLACEHOLDER,
project: str = _placeholders.PROJECT_ID_PLACEHOLDER,
strategy: str = 'STANDARD',
max_wait_duration: str = '86400s',
):
# fmt: off
"""Launch a Vertex AI [custom training job](https://cloud.google.com/vertex-ai/docs/training/create-custom-job) using the [CustomJob](https://cloud.google.com/vertex-ai/docs/reference/rest/v1/projects.locations.customJobs) API. See [Create custom training jobs ](https://cloud.google.com/vertex-ai/docs/training/create-custom-job) for more information.
Expand All @@ -60,6 +61,7 @@ def custom_training_job(
persistent_resource_id: The ID of the PersistentResource in the same Project and Location which to run. The default value is a placeholder that will be resolved to the PipelineJob [RuntimeConfig](https://cloud.google.com/vertex-ai/docs/reference/rest/v1/projects.locations.pipelineJobs#PipelineJob.RuntimeConfig)'s persistent resource id at runtime. However, if the PipelineJob doesn't set Persistent Resource as the job level runtime, the placedholder will be resolved to an empty string and the custom job will be run on demand. If the value is set explicitly, the custom job will runs in the specified persistent resource, in this case, please note the network and CMEK configs on the job should be consistent with those on the PersistentResource, otherwise, the job will be rejected.
project: Project to create the custom training job in. Defaults to the project in which the PipelineJob is run.
strategy: The strategy to use for the custom training job. The default is 'STANDARD'. See [more information](https://cloud.google.com/vertex-ai/docs/reference/rest/v1/CustomJobSpec#Strategy).
max_wait_duration: The maximum time to wait for the custom training job to be scheduled only if the scheduling strategy is set to FLEX_START. If set to 0, the job will wait indefinitely. The default is 24 hours. See [more information](https://cloud.google.com/vertex-ai/docs/reference/rest/v1/CustomJobSpec#Strategy).

Returns:
gcp_resources: Serialized JSON of `gcp_resources` [proto](https://github.com/kubeflow/pipelines/tree/master/components/google-cloud/google_cloud_pipeline_components/proto) which tracks the CustomJob.
Expand All @@ -78,6 +80,7 @@ def custom_training_job(
restart_job_on_worker_restart
),
'strategy': strategy,
'max_wait_duration': max_wait_duration,
},
'service_account': service_account,
'tensorboard': tensorboard,
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3 changes: 3 additions & 0 deletions components/google-cloud/google_cloud_pipeline_components/v1/custom_job/utils.py
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Expand Up @@ -72,6 +72,7 @@ def create_custom_training_job_from_component(
persistent_resource_id: str = _placeholders.PERSISTENT_RESOURCE_ID_PLACEHOLDER,
env: Optional[List[Dict[str, str]]] = None,
strategy: str = 'STANDARD',
max_wait_duration: str = '86400s',
reservation_affinity_type: Optional[str] = None,
reservation_affinity_key: Optional[str] = None,
reservation_affinity_values: Optional[List[str]] = None,
Expand Down Expand Up @@ -104,6 +105,7 @@ def create_custom_training_job_from_component(
persistent_resource_id: The ID of the PersistentResource in the same Project and Location which to run. The default value is a placeholder that will be resolved to the PipelineJob [RuntimeConfig](https://cloud.google.com/vertex-ai/docs/reference/rest/v1/projects.locations.pipelineJobs#PipelineJob.RuntimeConfig)'s persistent resource id at runtime. However, if the PipelineJob doesn't set Persistent Resource as the job level runtime, the placedholder will be resolved to an empty string and the custom job will be run on demand. If the value is set explicitly, the custom job will runs in the specified persistent resource, in this case, please note the network and CMEK configs on the job should be consistent with those on the PersistentResource, otherwise, the job will be rejected.
env: Environment variables to be passed to the container. Takes the form `[{'name': '...', 'value': '...'}]`. Maximum limit is 100.
startegy: The strategy to use for the custom training job. The default is 'STANDARD'. See [more information](https://cloud.google.com/vertex-ai/docs/reference/rest/v1/CustomJobSpec#Strategy).
max_wait_duration: The maximum time to wait for the custom training job to be scheduled only if the scheduling strategy is set to FLEX_START. If set to 0, the job will wait indefinitely. The default is 24 hours. See [more information](https://cloud.google.com/vertex-ai/docs/reference/rest/v1/CustomJobSpec#Strategy).
reservation_affinity_type: The type of [reservation affinity](https://cloud.google.com/vertex-ai/docs/reference/rest/v1/MachineSpec#reservationaffinity). Valid values are "NO_RESERVATION", "ANY_RESERVATION", "SPECIFIC_RESERVATION".
reservation_affinity_key: Corresponds to the label key of a reservation resource. To target a SPECIFIC_RESERVATION by name, use compute.googleapis.com/reservation-name as the key and specify the name of your reservation as its value.
reservation_affinity_values: Corresponds to the label values of a reservation resource. This must be the full resource name of the reservation.
Expand Down Expand Up @@ -217,6 +219,7 @@ def create_custom_training_job_from_component(
'timeout': timeout,
'restart_job_on_worker_restart': restart_job_on_worker_restart,
'strategy': strategy,
'max_wait_duration': max_wait_duration,
'service_account': service_account,
'tensorboard': tensorboard,
'enable_web_access': enable_web_access,
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