Class ScheduleServiceClient (1.25.0)

A service for creating and managing Vertex AI's Schedule resources to periodically launch shceudled runs to make API calls.

Returns the transport used by the client instance.

Instantiates the schedule service client.

Releases underlying transport's resources.

Returns a fully-qualified artifact string.

Starts asynchronous cancellation on a long-running operation.

The server makes a best effort to cancel the operation, but success is not guaranteed. If the server doesn't support this method, it returns google.rpc.Code.UNIMPLEMENTED.

Returns a fully-qualified billing_account string.

Returns a fully-qualified folder string.

Returns a fully-qualified location string.

Returns a fully-qualified organization string.

Returns a fully-qualified project string.

Returns a fully-qualified context string.

Creates a Schedule.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1beta1

def sample_create_schedule():
    # Create a client
    client = aiplatform_v1beta1.ScheduleServiceClient()

    # Initialize request argument(s)
    schedule = aiplatform_v1beta1.Schedule()
    schedule.cron = "cron_value"
    schedule.create_pipeline_job_request.parent = "parent_value"
    schedule.display_name = "display_name_value"
    schedule.max_concurrent_run_count = 2596

    request = aiplatform_v1beta1.CreateScheduleRequest(
        parent="parent_value",
        schedule=schedule,
    )

    # Make the request
    response = client.create_schedule(request=request)

    # Handle the response
    print(response)

Returns a fully-qualified custom_job string.

Deletes a long-running operation.

This method indicates that the client is no longer interested in the operation result. It does not cancel the operation. If the server doesn't support this method, it returns google.rpc.Code.UNIMPLEMENTED.

Deletes a Schedule.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1beta1

def sample_delete_schedule():
    # Create a client
    client = aiplatform_v1beta1.ScheduleServiceClient()

    # Initialize request argument(s)
    request = aiplatform_v1beta1.DeleteScheduleRequest(
        name="name_value",
    )

    # Make the request
    operation = client.delete_schedule(request=request)

    print("Waiting for operation to complete...")

    response = operation.result()

    # Handle the response
    print(response)

Returns a fully-qualified execution string.

Creates an instance of this client using the provided credentials file.

Creates an instance of this client using the provided credentials info.

Creates an instance of this client using the provided credentials file.

Gets the IAM access control policy for a function.

Returns an empty policy if the function exists and does not have a policy set.

Gets information about a location.

Return the API endpoint and client cert source for mutual TLS.

The client cert source is determined in the following order: (1) if GOOGLE_API_USE_CLIENT_CERTIFICATE environment variable is not "true", the client cert source is None. (2) if client_options.client_cert_source is provided, use the provided one; if the default client cert source exists, use the default one; otherwise the client cert source is None.

The API endpoint is determined in the following order: (1) if client_options.api_endpoint if provided, use the provided one. (2) if GOOGLE_API_USE_CLIENT_CERTIFICATE environment variable is "always", use the default mTLS endpoint; if the environment variable is "never", use the default API endpoint; otherwise if client cert source exists, use the default mTLS endpoint, otherwise use the default API endpoint.

More details can be found at https://google.aip.dev/auth/4114.

Gets the latest state of a long-running operation.

Gets a Schedule.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1beta1

def sample_get_schedule():
    # Create a client
    client = aiplatform_v1beta1.ScheduleServiceClient()

    # Initialize request argument(s)
    request = aiplatform_v1beta1.GetScheduleRequest(
        name="name_value",
    )

    # Make the request
    response = client.get_schedule(request=request)

    # Handle the response
    print(response)

Lists information about the supported locations for this service.

Lists operations that match the specified filter in the request.

Lists Schedules in a Location.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1beta1

def sample_list_schedules():
    # Create a client
    client = aiplatform_v1beta1.ScheduleServiceClient()

    # Initialize request argument(s)
    request = aiplatform_v1beta1.ListSchedulesRequest(
        parent="parent_value",
    )

    # Make the request
    page_result = client.list_schedules(request=request)

    # Handle the response
    for response in page_result:
        print(response)

Returns a fully-qualified network string.

Parses a artifact path into its component segments.

Parse a billing_account path into its component segments.

Parse a folder path into its component segments.

Parse a location path into its component segments.

Parse a organization path into its component segments.

Parse a project path into its component segments.

Parses a context path into its component segments.

Parses a custom_job path into its component segments.

Parses a execution path into its component segments.

Parses a network path into its component segments.

Parses a pipeline_job path into its component segments.

Parses a schedule path into its component segments.

Pauses a Schedule. Will mark xref_Schedule.state to 'PAUSED'. If the schedule is paused, no new runs will be created. Already created runs will NOT be paused or canceled.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1beta1

def sample_pause_schedule():
    # Create a client
    client = aiplatform_v1beta1.ScheduleServiceClient()

    # Initialize request argument(s)
    request = aiplatform_v1beta1.PauseScheduleRequest(
        name="name_value",
    )

    # Make the request
    client.pause_schedule(request=request)

Returns a fully-qualified pipeline_job string.

Resumes a paused Schedule to start scheduling new runs. Will mark xref_Schedule.state to 'ACTIVE'. Only paused Schedule can be resumed.

When the Schedule is resumed, new runs will be scheduled starting from the next execution time after the current time based on the time_specification in the Schedule. If [Schedule.catchUp][] is set up true, all missed runs will be scheduled for backfill first.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1beta1

def sample_resume_schedule():
    # Create a client
    client = aiplatform_v1beta1.ScheduleServiceClient()

    # Initialize request argument(s)
    request = aiplatform_v1beta1.ResumeScheduleRequest(
        name="name_value",
    )

    # Make the request
    client.resume_schedule(request=request)

Returns a fully-qualified schedule string.

Sets the IAM access control policy on the specified function.

Replaces any existing policy.

Tests the specified IAM permissions against the IAM access control policy for a function.

If the function does not exist, this will return an empty set of permissions, not a NOT_FOUND error.

Waits until the specified long-running operation is done or reaches at most a specified timeout, returning the latest state.

If the operation is already done, the latest state is immediately returned. If the timeout specified is greater than the default HTTP/RPC timeout, the HTTP/RPC timeout is used. If the server does not support this method, it returns google.rpc.Code.UNIMPLEMENTED.