Schedule a pipeline run with scheduler API

You can schedule one-time or recurring pipeline runs in Vertex AI using the scheduler API. This lets you implement continuous training in your project.

After you create a schedule, it can have one of the following states:

  • ACTIVE: An active schedule continuously creates pipeline runs according to the frequency configured using the cron schedule expression. A schedule becomes active on its start time and remains in that state until the specified end time, or until you pause it.

  • PAUSED: A paused schedule doesn't create pipeline runs. You can resume a paused schedule to make it active again. When you resume a paused schedule, you can use the catch_up parameter to specify whether skipped runs (runs that would have been scheduled if the schedule had been active) need to be rescheduled and submitted at the earliest possible schedule.

  • COMPLETED: A completed schedule no longer creates new pipeline runs. A schedule is completed according to its specified end time.

You can use the scheduler API to do the following:

Before you begin

Before you schedule a pipeline run using the scheduler API, use the following instructions to set up your Google Cloud project and development environment in the Google Cloud console.

  1. Grant the at least one of the following IAM permissions to the user or service account for using the scheduler API:

    • roles/aiplatform.admin
    • roles/aiplatform.user
  2. Build and compile a pipeline. For more information, see Build a Pipeline.

Create a schedule

You can create a one-time or recurring schedule.

Console

Use the following instructions to create a schedule using the Google Cloud console. If a schedule already exists for the project and region, use the instructions in Create a pipeline run.

Use the following instructions to create a pipeline schedule:

  1. In the Google Cloud console, in the Vertex AI section, go to the Schedules tab on the Pipelines page.

    Go to Schedules

  2. Click Create scheduled run to open the Create pipeline run pane.

  3. Specify the following Run details by selecting one of the following options:

    • To create a pipeline run based on an existing pipeline template, click Select from existing pipelines and enter the following details:

      1. Select the Repository containing the pipeline or component definition file.

      2. Select the Pipeline or component and Version.

    • To upload a compiled pipeline definition, click Upload file and enter the following details:

      1. Click Browse to open the file selector. Navigate to the compiled pipeline YAML file that you want to run, select the pipeline, and click Open.

      2. The Pipeline or component name shows the name specified in the pipeline definition, by default. Optionally, specify a different Pipeline name.

    • To import a pipeline definition file from Cloud Storage, click Import from Cloud Storage and enter the following details:

      1. Click Browse to navigate to the Cloud Storage bucket containing the pipeline definition object, select the file, and then click Select.

      2. Specify the Pipeline or component name.

  4. Specify a Run name to uniquely identify the pipeline run.

  5. Specify the Run schedule, as follows:

    1. Select Recurring.

    2. Under Start time, specify the when the schedule becomes active.

      • To schedule the first run to occur immediately upon schedule creation, select Immediately.

      • To schedule the first run to occur at a specific time and date, select On.

    3. In the Frequency field, specify the frequency to schedule and execute the pipeline runs, using a cron schedule expression based on unix-cron.

    4. Under Ends, specify when the schedule ends.

      • To indicate that the schedule creates pipeline runs indefinitely, select Never.

      • To indicate that the schedule ends on a specific date and time, select On, and specify the end date and time for the schedule.

  6. Optional: To specify a custom service account, a customer-managed encryption key (CMEK), or a peered VPC network, click Advanced options and specify a service account, CMEK, or peered VPC network name.

  7. Click Continue and specify the Runtime configuration for the pipeline.

  8. Click Submit to create your pipeline run schedule.

REST

To create a pipeline run schedule, send a POST request by using the projects.locations.schedules.create method.

Before using any of the request data, make the following replacements:

  • LOCATION: The region where you want to run the pipeline. For more information about the regions where Vertex AI Pipelines is available, see the Vertex AI locations guide.
  • PROJECT_ID: The Google Cloud project where you want to run the pipeline.
  • DISPLAY_NAME: The name of the pipeline schedule. You can specify a name having a maximum length of 128 UTF-8 characters.
  • START_TIME: Timestamp after which the first run can be scheduled, for example, 2045-07-26T00:00:00Z. If you don't specify this parameter, the timestamp corresponding to the date and time when you create the schedule is used as the default value.
  • END_TIME: Timestamp after which pipeline runs are no longer scheduled scheduled. After the END_TIME is reached, the state of the schedule changes to COMPLETED. If you don't specify this parameter, then the schedule continues to run new pipeline jobs indefinitely until you pause or delete the schedule.
  • CRON_EXPRESSION: Cron schedule expression representing the frequency to schedule and execute pipeline runs. For more information, see cron.
  • MAX_CONCURRENT_RUN_COUNT: The maximum number of concurrent runs for the schedule.
  • API_REQUEST_TEMPLATE: PipelineService.CreatePipelineJob API request template used to execute the scheduled pipeline runs. For more information about the parameters in the API request template, see the documentation for pipelineJobs.create. Note that you can't specify the pipelineJobId parameter in this template, as the scheduler API doesn't support this parameter.

HTTP method and URL:

POST https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/schedules

Request JSON body:

{
  "display_name":"DISPLAY_NAME",
  "start_time": "START_TIME",
  "end_time": "END_TIME",
  "cron": "CRON_EXPRESSION",
  "max_concurrent_run_count": "MAX_CONCURRENT_RUN_COUNT",
  "create_pipeline_job_request": API_REQUEST_TEMPLATE
}

To send your request, choose one of these options:

curl

Save the request body in a file named request.json, and execute the following command:

curl -X POST \
-H "Authorization: Bearer $(gcloud auth print-access-token)" \
-H "Content-Type: application/json; charset=utf-8" \
-d @request.json \
"https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/schedules"

PowerShell

Save the request body in a file named request.json, and execute the following command:

$cred = gcloud auth print-access-token
$headers = @{ "Authorization" = "Bearer $cred" }

Invoke-WebRequest `
-Method POST `
-Headers $headers `
-ContentType: "application/json; charset=utf-8" `
-InFile request.json `
-Uri "https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/schedules" | Select-Object -Expand Content

You should see output similar to the following. You can use the SCHEDULE_ID from the response to retrieve, pause, resume, or delete the schedule. PIPELINE_JOB_CREATION_REQUEST represents the API request to create the pipeline job.

{
  "name": "projects/PROJECT_ID/locations/LOCATION/schedules/SCHEDULE_ID",
  "displayName": "DISPLAY_NAME",
  "startTime": "START_TIME",
  "state": "ACTIVE",
  "createTime": "2025-01-01T00:00:00.000000Z",
  "nextRunTime": "2045-08-01T00:00:00Z",
  "cron": "CRON_EXPRESSION",
  "maxConcurrentRunCount": "MAX_CONCURRENT_RUN_COUNT",
  "createPipelineJobRequest": PIPELINE_JOB_CREATION_REQUEST
}

Vertex AI SDK for Python

You can create a pipeline run schedule in the following ways:

  • Create a schedule based on a PipelineJob using the PipelineJob.create_schedule method.

  • Creating a schedule using the PipelineJobSchedule.create method.

While creating a pipeline run schedule, you can also pass the following placeholders supported by the KFP SDK as inputs:

  • {{$.pipeline_job_name_placeholder}}

  • {{$.pipeline_job_resource_name_placeholder}}

  • {{$.pipeline_job_id_placeholder}}

  • {{$.pipeline_task_name_placeholder}}

  • {{$.pipeline_task_id_placeholder}}

  • {{$.pipeline_job_create_time_utc_placeholder}}

  • {{$.pipeline_job_schedule_time_utc_placeholder}}

  • {{$.pipeline_root_placeholder}}

For more information, see Special input types in the Kubeflow Pipelines v2 documentation.

Create a schedule from a PipelineJob

Use the following sample to schedule pipeline runs using the PipelineJob.create_schedule method:

from google.cloud import aiplatform

pipeline_job = aiplatform.PipelineJob(
  template_path="COMPILED_PIPELINE_PATH",
  pipeline_root="PIPELINE_ROOT_PATH",
  display_name="DISPLAY_NAME",
)

pipeline_job_schedule = pipeline_job.create_schedule(
  display_name="SCHEDULE_NAME",
  cron="TZ=CRON",
  max_concurrent_run_count=MAX_CONCURRENT_RUN_COUNT,
  max_run_count=MAX_RUN_COUNT,
)

  • COMPILED_PIPELINE_PATH: The path to your compiled pipeline YAML file. It can be a local path or a Cloud Storage URI.

    Optional: To specify a particular version of a template, include the version tag along with the path in any one of the following formats:

    • COMPILED_PIPELINE_PATH:TAG, where TAG is the version tag.

    • COMPILED_PIPELINE_PATH@SHA256_TAG, where SHA256_TAG is the sha256 hash value of the pipeline version.

  • PIPELINE_ROOT_PATH: (optional) To override the pipeline root path specified in the pipeline definition, specify a path that your pipeline job can access, such as a Cloud Storage bucket URI.

  • DISPLAY_NAME: The name of the pipeline. This will show up in the Google Cloud console.

  • SCHEDULE_NAME: The name of the pipeline schedule. You can specify a name having a maximum length of 128 UTF-8 characters.

  • CRON: Cron schedule expression representing the frequency to schedule and execute pipeline runs. For more information, see Cron.

  • MAX_CONCURRENT_RUN_COUNT: The maximum number of concurrent runs for the schedule.

  • MAX_RUN_COUNT: The maximum number of pipeline runs that the schedule creates after which it's completed.

Create a schedule using PipelineJobSchedule.create

Use the following sample to schedule pipeline runs using the PipelineJobSchedule.create method:

from google.cloud import aiplatform

pipeline_job = aiplatform.PipelineJob(
  template_path="COMPILED_PIPELINE_PATH",
  pipeline_root="PIPELINE_ROOT_PATH",
  display_name="DISPLAY_NAME",
)

pipeline_job_schedule = aiplatform.PipelineJobSchedule(
  pipeline_job=pipeline_job,
  display_name="SCHEDULE_NAME"
)

pipeline_job_schedule.create(
  cron="TZ=CRON",
  max_concurrent_run_count=MAX_CONCURRENT_RUN_COUNT,
  max_run_count=MAX_RUN_COUNT,
)

  • COMPILED_PIPELINE_PATH: The path to your compiled pipeline YAML file. It can be a local path or a Cloud Storage URI.

    Optional: To specify a particular version of a template, include the version tag along with the path in any one of the following formats:

    • COMPILED_PIPELINE_PATH:TAG, where TAG is the version tag.

    • COMPILED_PIPELINE_PATH@SHA256_TAG, where SHA256_TAG is the sha256 hash value of the pipeline version.

  • PIPELINE_ROOT_PATH: (optional) To override the pipeline root path specified in the pipeline definition, specify a path that your pipeline job can access, such as a Cloud Storage bucket URI.

  • DISPLAY_NAME: The name of the pipeline. This will show up in the Google Cloud console.

  • SCHEDULE_NAME: The name of the pipeline schedule. You can specify a name having a maximum length of 128 UTF-8 characters.

  • CRON: Cron schedule expression representing the frequency to schedule and execute pipeline runs. For more information, see Cron.

  • MAX_CONCURRENT_RUN_COUNT: The maximum number of concurrent runs for the schedule.

  • MAX_RUN_COUNT: The maximum number of pipeline runs that the schedule creates after which it's completed.

List schedules

You can view the list of pipeline schedules created for your Google Cloud project.

Console

You can view the list of pipeline schedules on the Schedules tab of the Google Cloud console for the selected region.

To view the list of pipeline schedules, in the Google Cloud console, in the Vertex AI section, go to the Schedules tab on the Pipelines page.

Go to Schedules

REST

To list pipeline run schedules in your project, send a GET request by using the projects.locations.schedules.list method.

Before using any of the request data, make the following replacements:

  • LOCATION: The region where you want to run the pipeline. For more information about the regions where Vertex AI Pipelines is available, see the Vertex AI locations guide.
  • PROJECT_ID: The Google Cloud project where you want to run the pipeline.
  • FILTER: (optional) Expression to filter the list of schedules. For more information, see ...
  • PAGE_SIZE: (optional) The number of schedules to be listed per page.
  • PAGE_TOKEN: (optional) The standard list page token, typically obtained via ListSchedulesResponse.next_page_token[] from a previous ScheduleService.ListSchedules[] call.
  • ORDER_BY: (optional) Comma-separated list of fields, indicating the sort order of the schedules in the response.

HTTP method and URL:

GET https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/schedules?FILTER&PAGE_SIZE&PAGE_TOKEN&ORDER_BY

To send your request, choose one of these options:

curl

Execute the following command:

curl -X GET \
-H "Authorization: Bearer $(gcloud auth print-access-token)" \
"https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/schedules?FILTER&PAGE_SIZE&PAGE_TOKEN&ORDER_BY"

PowerShell

Execute the following command:

$cred = gcloud auth print-access-token
$headers = @{ "Authorization" = "Bearer $cred" }

Invoke-WebRequest `
-Method GET `
-Headers $headers `
-Uri "https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/schedules?FILTER&PAGE_SIZE&PAGE_TOKEN&ORDER_BY" | Select-Object -Expand Content

You should see output similar to the following:

{
  "schedules": [
    SCHEDULE_ENTITY_OBJECT_1,
    SCHEDULE_ENTITY_OBJECT_2,
    ...
  ],
}

Vertex AI SDK for Python

Use the following sample to list all the schedules in your project in the descending order of their creation times:

from google.cloud import aiplatform

aiplatform.PipelineJobSchedule.list(
  filter='display_name="DISPLAY_NAME"',
  order_by='create_time desc'
)

DISPLAY_NAME: The name of the pipeline schedule. You can specify a name having a maximum length of 128 UTF-8 characters.

Retrieve a schedule

You can retrieve a pipeline run schedule using the schedule ID.

REST

To retrieve a pipeline run schedule, send a GET request by using the projects.locations.schedules.get method and the schedule ID.

Before using any of the request data, make the following replacements:

  • LOCATION: The region where you want to run the pipeline. For more information about the regions where Vertex AI Pipelines is available, see the Vertex AI locations guide.
  • PROJECT_ID: The Google Cloud project where you want to run the pipeline.
  • SCHEDULE_ID: Unique schedule ID generated while creating the schedule.

HTTP method and URL:

GET https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/schedules/SCHEDULE_ID

To send your request, choose one of these options:

curl

Execute the following command:

curl -X GET \
-H "Authorization: Bearer $(gcloud auth print-access-token)" \
"https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/schedules/SCHEDULE_ID"

PowerShell

Execute the following command:

$cred = gcloud auth print-access-token
$headers = @{ "Authorization" = "Bearer $cred" }

Invoke-WebRequest `
-Method GET `
-Headers $headers `
-Uri "https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/schedules/SCHEDULE_ID" | Select-Object -Expand Content

You should see output similar to the following. PIPELINE_JOB_CREATION_REQUEST represents the API request to create the pipeline job.

{
  "name": "projects/PROJECT_ID/locations/LOCATION/schedules/SCHEDULE_ID",
  "displayName": "schedule_display_name",
  "startTime": "2045-07-26T06:59:59Z",
  "state": "ACTIVE",
  "createTime": "20xx-01-01T00:00:00.000000Z",
  "nextRunTime": "2045-08-01T00:00:00Z",
  "cron": "TZ=America/New_York 0 0 1 * *",
  "maxConcurrentRunCount": "10",
  "createPipelineJobRequest": PIPELINE_JOB_CREATION_REQUEST
}

Vertex AI SDK for Python

Use the following sample to retrieve a pipeline run schedule using the schedule ID:

from google.cloud import aiplatform

pipeline_job_schedule = aiplatform.PipelineJobSchedule.get(schedule_id=SCHEDULE_ID)

SCHEDULE_ID: Unique schedule ID generated while creating the schedule.

Pause a schedule

You can pause an active pipeline schedule by specifying the schedule ID. When you pause a schedule, its state changes from ACTIVE to PAUSED.

Console

You can pause a pipeline run schedule that's currently active.

Use the following instructions to pause a schedule:

  1. In the Google Cloud console, in the Vertex AI section, go to the Schedules tab on the Pipelines page.

    Go to Schedules

  2. Go to the options menu that's in the same row as the schedule you want to pause, and then click Pause. You can pause any schedule where the Status column shows Active.

REST

To pause a pipeline run schedule in your project, send a POST request by using the projects.locations.schedules.pause method.

Before using any of the request data, make the following replacements:

  • LOCATION: The region where the pipeline run schedule is currently active. For more information about the regions where Vertex AI Pipelines is available, see the Vertex AI locations guide.
  • PROJECT_ID: The Google Cloud project where the pipeline run schedule is currently active.
  • SCHEDULE_ID: Unique schedule ID generated while creating the schedule.

HTTP method and URL:

POST https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/schedules/SCHEDULE_ID:pause

To send your request, choose one of these options:

curl

Execute the following command:

curl -X POST \
-H "Authorization: Bearer $(gcloud auth print-access-token)" \
-H "Content-Type: application/json; charset=utf-8" \
-d "" \
"https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/schedules/SCHEDULE_ID:pause"

PowerShell

Execute the following command:

$cred = gcloud auth print-access-token
$headers = @{ "Authorization" = "Bearer $cred" }

Invoke-WebRequest `
-Method POST `
-Headers $headers `
-Uri "https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/schedules/SCHEDULE_ID:pause" | Select-Object -Expand Content

You should receive a successful status code (2xx) and an empty response.

Vertex AI SDK for Python

Use the following sample to pause a pipeline run schedule:

from google.cloud import aiplatform

pipeline_job_schedule = aiplatform.PipelineJobSchedule.get(schedule_id=SCHEDULE_ID)

pipeline_job_schedule.pause()

SCHEDULE_ID: Unique schedule ID generated while creating the schedule.

Update a schedule

You can update an existing pipeline schedule that was created for your Google Cloud project.

Updating a schedule is similar to deleting and recreating a schedule. When you update a schedule, new runs are scheduled based on the frequency of the updated schedule. New runs are no longer created based on the old schedule and any queued runs are dropped. Pipeline runs that are already created by the old schedule aren't paused or canceled.

REST

To update a pipeline run schedule in your project, send a PATCH request by using the projects.locations.schedules.patch method.

Before using any of the request data, make the following replacements:

  • LOCATION: The region where you want to run the pipeline. For more information about the regions where Vertex AI Pipelines is available, see the Vertex AI locations guide.
  • PROJECT_ID: The Google Cloud project where you want to run the pipeline.
  • DISPLAY_NAME: The name of the pipeline schedule. You can specify a name having a maximum length of 128 UTF-8 characters.
  • MAX_CONCURRENT_RUN_COUNT: The maximum number of concurrent runs for the schedule.

HTTP method and URL:

POST https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/schedules/SCHEDULE_ID?updateMask=display_name,max_run_count -d '{"display_name":"DISPLAY_NAME", "max_concurrent_run_count": MAX_CONCURRENT_RUN_COUNT}'

To send your request, choose one of these options:

curl

Execute the following command:

curl -X POST \
-H "Authorization: Bearer $(gcloud auth print-access-token)" \
-H "Content-Type: application/json; charset=utf-8" \
-d "" \
"https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/schedules/SCHEDULE_ID?updateMask=display_name,max_run_count -d '{"display_name":"DISPLAY_NAME", "max_concurrent_run_count": MAX_CONCURRENT_RUN_COUNT}'"

PowerShell

Execute the following command:

$cred = gcloud auth print-access-token
$headers = @{ "Authorization" = "Bearer $cred" }

Invoke-WebRequest `
-Method POST `
-Headers $headers `
-Uri "https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/schedules/SCHEDULE_ID?updateMask=display_name,max_run_count -d '{"display_name":"DISPLAY_NAME", "max_concurrent_run_count": MAX_CONCURRENT_RUN_COUNT}'" | Select-Object -Expand Content

You should see output similar to the following. Based on the update, the NEXT_RUN_TIME is recalculated. When you update the schedule, the START_TIME remains unchanged.

{
  "name": "projects/PROJECT_ID/locations/LOCATION/schedules/SCHEDULE_ID",
  "displayName": "DISPLAY_NAME",
  "startTime": "START_TIME",
  "state": "ACTIVE",
  "createTime": "2025-01-01T00:00:00.000000Z",
  "nextRunTime": NEXT_RUN_TIME,
  "maxConcurrentRunCount": "MAX_CONCURRENT_RUN_COUNT",
}

Vertex AI SDK for Python

Use the following sample to schedule pipeline runs using the PipelineJobSchedule.update method:

from google.cloud import aiplatform

pipeline_job_schedule = aiplatform.PipelineJobSchedule.get(schedule_id=SCHEDULE_ID)

pipeline_job_schedule.update(
  display_name='DISPLAY_NAME',
  max_concurrent_run_count=MAX_CONCURRENT_RUN_COUNT,
)

  • SCHEDULE_ID: Unique schedule ID generated while creating the schedule.
  • DISPLAY_NAME: The name of the pipeline schedule. You can specify a name having a maximum length of 128 UTF-8 characters.
  • MAX_CONCURRENT_RUN_COUNT: The maximum number of concurrent runs for the schedule.

Resume a schedule

You can resume a paused pipeline schedule by specifying the schedule ID. When you resume a schedule, its state changes from PAUSED to ACTIVE.

Console

You can resume a pipeline run schedule that's currently paused.

Use the following instructions to resume a schedule:

  1. In the Google Cloud console, in the Vertex AI section, go to the Schedules tab on the Pipelines page.

    Go to Schedules

  2. Go to the options menu that's in the same row as the schedule you want to resume, and then click Resume. You can resume any schedule where the Status column shows Paused.

REST

To resume a pipeline run schedule in your project, send a POST request by using the projects.locations.schedules.resume method.

Before using any of the request data, make the following replacements:

  • LOCATION: The region where the pipeline run schedule is currently paused. For more information about the regions where Vertex AI Pipelines is available, see the Vertex AI locations guide.
  • PROJECT_ID: The Google Cloud project where the pipeline run schedule is currently paused.
  • SCHEDULE_ID: Unique schedule ID generated while creating the schedule.
  • CATCH_UP: (Optional) Indicate whether the paused schedule should backfill the skipped pipeline runs. To backfill and reschedule the skipped pipeline runs, enter the following:
    { "catch_up":true } This parameter is set to `false`, by default.

HTTP method and URL:

POST https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/schedules/SCHEDULE_ID:resume -d 'CATCH_UP'

To send your request, choose one of these options:

curl

Execute the following command:

curl -X POST \
-H "Authorization: Bearer $(gcloud auth print-access-token)" \
-H "Content-Type: application/json; charset=utf-8" \
-d "" \
"https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/schedules/SCHEDULE_ID:resume -d 'CATCH_UP'"

PowerShell

Execute the following command:

$cred = gcloud auth print-access-token
$headers = @{ "Authorization" = "Bearer $cred" }

Invoke-WebRequest `
-Method POST `
-Headers $headers `
-Uri "https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/schedules/SCHEDULE_ID:resume -d 'CATCH_UP'" | Select-Object -Expand Content

You should receive a successful status code (2xx) and an empty response.

Vertex AI SDK for Python

Use the following sample to resume a paused pipeline run schedule:

from google.cloud import aiplatform

pipeline_job_schedule = aiplatform.PipelineJobSchedule.get(schedule_id=SCHEDULE_ID)

pipeline_job_schedule.resume(catch_up=CATCH_UP)
  • SCHEDULE_ID: Unique schedule ID generated while creating the schedule.
  • CATCH_UP: (Optional) Indicate whether the paused schedule should backfill the skipped pipeline runs. To backfill and reschedule the skipped pipeline runs, enter the following:
    { "catch_up":true }

Delete a schedule

You can delete a pipeline schedule by specifying the schedule ID.

Console

You can delete a pipeline run schedule regardless of its status.

Use the following instructions to delete a schedule:

  1. In the Google Cloud console, in the Vertex AI section, go to the Schedules tab on the Pipelines page.

    Go to Schedules

  2. Go to the options menu that's in the same row as the schedule you want to delete, and then click Delete.

  3. To confirm deletion, click Delete.

REST

To delete a pipeline run schedule in your project, send a DELETE request by using the projects.locations.schedules.delete method.

Before using any of the request data, make the following replacements:

  • LOCATION: The region where you want to delete the pipeline schedule. For more information about the regions where Vertex AI Pipelines is available, see the Vertex AI locations guide.
  • PROJECT_ID: The Google Cloud project where you want to delete the schedule.
  • SCHEDULE_ID: The unique schedule ID generated while creating the schedule.

HTTP method and URL:

DELETE https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/schedules/SCHEDULE_ID

To send your request, choose one of these options:

curl

Execute the following command:

curl -X DELETE \
-H "Authorization: Bearer $(gcloud auth print-access-token)" \
"https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/schedules/SCHEDULE_ID"

PowerShell

Execute the following command:

$cred = gcloud auth print-access-token
$headers = @{ "Authorization" = "Bearer $cred" }

Invoke-WebRequest `
-Method DELETE `
-Headers $headers `
-Uri "https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/schedules/SCHEDULE_ID" | Select-Object -Expand Content

You should see output similar to the following. OPERATION_ID represents the delete operation.

{
  "name": "projects/PROJECT_ID/locations/LOCATION/operations/OPERATION_ID",
  "metadata": {
    "@type": "type.googleapis.com/google.cloud.aiplatform.v1.DeleteOperationMetadata",
    "genericMetadata": {
      "createTime": "20xx-01-01T00:00:00.000000Z",
      "updateTime": "20xx-01-01T00:00:00.000000Z"
    }
  },
  "done": true,
  "response": {
    "@type": "type.googleapis.com/google.protobuf.Empty"
  }
}

Vertex AI SDK for Python

Use the following sample to delete a pipeline run schedule:

from google.cloud import aiplatform

pipeline_job_schedule = aiplatform.PipelineJobSchedule.get(schedule_id=SCHEDULE_ID)

pipeline_job_schedule.delete()

SCHEDULE_ID: Unique schedule ID generated while creating the schedule.

List all pipeline jobs created by a schedule

You can view a list of all the pipeline jobs created by a schedule by specifying the schedule ID.

REST

To list all the pipeline runs that have been created by a pipeline schedule, send a GET request by using the projects.locations.pipelineJobs method.

Before using any of the request data, make the following replacements:

  • LOCATION: The region where you want to run the pipeline. For more information about the regions where Vertex AI Pipelines is available, see the Vertex AI locations guide.
  • PROJECT_ID: The Google Cloud project where you want to run the pipeline.
  • SCHEDULE_ID: Unique schedule ID generated while creating the schedule.

HTTP method and URL:

GET https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/pipelineJobs?filter=schedule_name=projects/PROJECT/locations/LOCATION/schedules/SCHEDULE_ID

To send your request, choose one of these options:

curl

Execute the following command:

curl -X GET \
-H "Authorization: Bearer $(gcloud auth print-access-token)" \
"https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/pipelineJobs?filter=schedule_name=projects/PROJECT/locations/LOCATION/schedules/SCHEDULE_ID"

PowerShell

Execute the following command:

$cred = gcloud auth print-access-token
$headers = @{ "Authorization" = "Bearer $cred" }

Invoke-WebRequest `
-Method GET `
-Headers $headers `
-Uri "https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/pipelineJobs?filter=schedule_name=projects/PROJECT/locations/LOCATION/schedules/SCHEDULE_ID" | Select-Object -Expand Content

You should see output similar to the following.

{
  "pipelineJobs": [
    PIPELINE_JOB_ENTITY_1,
    PIPELINE_JOB_ENTITY_2,
    ...
  ],
}