--- title: "GET /v1/projects/{projectsId}/locations/{locationsId}/schedules" method: GET path: "/v1/projects/{projectsId}/locations/{locationsId}/schedules" tags: ["projects"] --- # GET /v1/projects/{projectsId}/locations/{locationsId}/schedules `GET /v1/projects/{projectsId}/locations/{locationsId}/schedules` ## Request - Base URL: `https://aiplatform.googleapis.com` - URL: `https://aiplatform.googleapis.com/v1/projects/{projectsId}/locations/{locationsId}/schedules` - Auth: OAuth 2 and OAuth 2 - Implicit: `https://accounts.google.com/o/oauth2/auth` - Scopes: `https://www.googleapis.com/auth/cloud-platform` - Authorization code: `https://accounts.google.com/o/oauth2/auth` - Token URL: `https://accounts.google.com/o/oauth2/token` scopes: `https://www.googleapis.com/auth/cloud-platform` Lists Schedules in a Location. ## Path parameters - `projectsId` string, required - `locationsId` string, required ## Query parameters - `filter` string - `orderBy` string - `pageSize` integer - `pageToken` string ## Response `200` Successful response - GoogleCloudAiplatformV1ListSchedulesResponse: Response message for ScheduleService.ListSchedules - `nextPageToken` string: A token to retrieve the next page of results. Pass to ListSchedulesRequest.page_token to obtain that page. - `schedules` GoogleCloudAiplatformV1Schedule[]: List of Schedules in the requested page. - `allowQueueing` boolean: Optional. Whether new scheduled runs can be queued when max_concurrent_runs limit is reached. If set to true, new runs will be queued instead of skipped. Default to false. - `catchUp` boolean: Output only. Whether to backfill missed runs when the schedule is resumed from PAUSED state. If set to true, all missed runs will be scheduled. New runs will be scheduled after the backfill is complete. Default to false. - `createNotebookExecutionJobRequest` GoogleCloudAiplatformV1CreateNotebookExecutionJobRequest: Request message for [NotebookService.CreateNotebookExecutionJob] - `notebookExecutionJob` GoogleCloudAiplatformV1NotebookExecutionJob: NotebookExecutionJob represents an instance of a notebook execution. - `createTime` string, google-datetime: Output only. Timestamp when this NotebookExecutionJob was created. - `customEnvironmentSpec` GoogleCloudAiplatformV1NotebookExecutionJobCustomEnvironmentSpec: Compute configuration to use for an execution job. - `machineSpec` GoogleCloudAiplatformV1MachineSpec: Specification of a single machine. - `acceleratorCount` integer: The number of accelerators to attach to the machine. For [accelerator optimized machine types](https://cloud.google.com/compute/docs/accelerator-optimized-machines), One may set the accelerator_count from 1 to N for machine with N GPUs. If accelerator_count is less than or equal to N / 2, Agent Platform co-schedules the replicas of the model into the same VM to save cost. For example, if the machine type is a3-highgpu-8g, which has 8 H100 GPUs, one can set accelerator_count to 1 to 8. If accelerator_count is 1, 2, 3, or 4, Agent Platform co-schedules 8, 4, 2, or 2 replicas of the model into the same VM to save cost. When co-scheduling, CPU, memory and storage on the VM will be distributed to replicas on the VM. For example, one can expect a co-scheduled replica requesting 2 GPUs out of a 8-GPU VM will receive 25% of the CPU, memory and storage of the VM. Note that the feature is not compatible with multihost_gpu_node_count. When multihost_gpu_node_count is set, the co-scheduling will not be enabled. - `acceleratorType` 'ACCELERATOR_TYPE_UNSPECIFIED' | 'NVIDIA_TESLA_K80' | 'NVIDIA_TESLA_P100' | 'NVIDIA_TESLA_V100' | 'NVIDIA_TESLA_P4' | 'NVIDIA_TESLA_T4' | 'NVIDIA_TESLA_A100' | 'NVIDIA_A100_80GB' | 'NVIDIA_L4' | 'NVIDIA_H100_80GB' | 'NVIDIA_H100_MEGA_80GB' | 'NVIDIA_H200_141GB' | 'NVIDIA_B200' | 'NVIDIA_GB200' | 'NVIDIA_RTX_PRO_6000' | 'TPU_V2' | 'TPU_V3' | 'TPU_V4_POD' | 'TPU_V5_LITEPOD': Immutable. The type of accelerator(s) that may be attached to the machine as per accelerator_count. - `gpuPartitionSize` string: Optional. Immutable. The Nvidia GPU partition size. When specified, the requested accelerators will be partitioned into smaller GPU partitions. For example, if the request is for 8 units of NVIDIA A100 GPUs, and gpu_partition_size="1g.10gb", the service will create 8 * 7 = 56 partitioned MIG instances. The partition size must be a value supported by the requested accelerator. Refer to [Nvidia GPU Partitioning](https://cloud.google.com/kubernetes-engine/docs/how-to/gpus-multi#multi-instance_gpu_partitions) for the available partition sizes. If set, the accelerator_count should be set to 1. - `machineType` string: Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/gemini-enterprise-agent-platform/machine-learning/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/gemini-enterprise-agent-platform/machine-learning/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required. - `reservationAffinity` GoogleCloudAiplatformV1ReservationAffinity: A ReservationAffinity can be used to configure a Vertex AI resource (e.g., a DeployedModel) to draw its Compute Engine resources from a Shared Reservation, or exclusively from on-demand capacity. - … - `tpuTopology` string: Immutable. The topology of the TPUs. Corresponds to the TPU topologies available from GKE. (Example: tpu_topology: "2x2x1"). - `networkSpec` GoogleCloudAiplatformV1NetworkSpec: Network spec. - `enableInternetAccess` boolean: Whether to enable public internet access. Default false. - `network` string: The full name of the Google Compute Engine [network](https://cloud.google.com//compute/docs/networks-and-firewalls#networks) - `subnetwork` string: The name of the subnet that this instance is in. Format: `projects/{project_id_or_number}/regions/{region}/subnetworks/{subnetwork_id}` - `persistentDiskSpec` GoogleCloudAiplatformV1PersistentDiskSpec: Represents the spec of persistent disk and hyperdisk options. - `diskSizeGb` string, int64: Size in GB of the disk (default is 100GB). - `diskType` string: Type of the disk (default is "pd-standard"). Valid values: "pd-ssd" (Persistent Disk Solid State Drive) "pd-standard" (Persistent Disk Hard Disk Drive) "pd-balanced" (Balanced Persistent Disk) "pd-extreme" (Extreme Persistent Disk) "hyperdisk-balanced" (Hyperdisk Balanced) "hyperdisk-extreme" (Hyperdisk Extreme) "hyperdisk-balanced-high-availability" (Hyperdisk Balanced High Availability) "hyperdisk-ml" (Hyperdisk ML) "hyperdisk-throughput" (Hyperdisk Throughput) - `shieldedInstanceConfig` GoogleCloudAiplatformV1NotebookExecutionJobCustomEnvironmentSpecShieldedInstanceConfig: A set of Shielded VM options for the execution VM. Mirrors `google.cloud.notebooks.v2.ShieldedInstanceConfig`. See https://cloud.google.com/compute/docs/instances/modifying-shielded-vm. - `enableIntegrityMonitoring` boolean: Optional. Whether the VM instance has integrity monitoring enabled. - `enableSecureBoot` boolean: Optional. Whether the VM instance has Secure Boot enabled. Disabled by default. - `enableVtpm` boolean: Optional. Whether the VM instance has vTPM enabled. - `dataformRepositorySource` GoogleCloudAiplatformV1NotebookExecutionJobDataformRepositorySource: The Dataform Repository containing the input notebook. - `commitSha` string: The commit SHA to read repository with. If unset, the file will be read at HEAD. - `dataformRepositoryResourceName` string: The resource name of the Dataform Repository. Format: `projects/{project_id}/locations/{location}/repositories/{repository_id}` - `directNotebookSource` GoogleCloudAiplatformV1NotebookExecutionJobDirectNotebookSource: The content of the input notebook in ipynb format. - `content` string, byte: The base64-encoded contents of the input notebook file. - `displayName` string: The display name of the NotebookExecutionJob. The name can be up to 128 characters long and can consist of any UTF-8 characters. - `encryptionSpec` GoogleCloudAiplatformV1EncryptionSpec: Represents a customer-managed encryption key specification that can be applied to a Vertex AI resource. - `kmsKeyName` string: Required. Resource name of the Cloud KMS key used to protect the resource. The Cloud KMS key must be in the same region as the resource. It must have the format `projects/{project}/locations/{location}/keyRings/{key_ring}/cryptoKeys/{crypto_key}`. - `executionTimeout` string, google-duration: Max running time of the execution job in seconds (default 86400s / 24 hrs). - `executionUser` string: The user email to run the execution as. Only supported by Colab runtimes. - `gcsNotebookSource` GoogleCloudAiplatformV1NotebookExecutionJobGcsNotebookSource: The Cloud Storage uri for the input notebook. - `generation` string: The version of the Cloud Storage object to read. If unset, the current version of the object is read. See https://cloud.google.com/storage/docs/metadata#generation-number. - `uri` string: The Cloud Storage uri pointing to the ipynb file. Format: `gs://bucket/notebook_file.ipynb` - `gcsOutputUri` string: The Cloud Storage location to upload the result to. Format: `gs://bucket-name` - `jobState` 'JOB_STATE_UNSPECIFIED' | 'JOB_STATE_QUEUED' | 'JOB_STATE_PENDING' | 'JOB_STATE_RUNNING' | 'JOB_STATE_SUCCEEDED' | 'JOB_STATE_FAILED' | 'JOB_STATE_CANCELLING' | 'JOB_STATE_CANCELLED' | 'JOB_STATE_PAUSED' | 'JOB_STATE_EXPIRED' | 'JOB_STATE_UPDATING' | 'JOB_STATE_PARTIALLY_SUCCEEDED': Output only. The state of the NotebookExecutionJob. - `kernelName` string: The name of the kernel to use during notebook execution. If unset, the default kernel is used. - `labels` object: The labels with user-defined metadata to organize NotebookExecutionJobs. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels. System reserved label keys are prefixed with "aiplatform.googleapis.com/" and are immutable. - `name` string: Output only. The resource name of this NotebookExecutionJob. Format: `projects/{project_id}/locations/{location}/notebookExecutionJobs/{job_id}` - `notebookRuntimeTemplateResourceName` string: The NotebookRuntimeTemplate to source compute configuration from. - `scheduleResourceName` string: The Schedule resource name if this job is triggered by one. Format: `projects/{project_id}/locations/{location}/schedules/{schedule_id}` - `serviceAccount` string: The service account to run the execution as. - `status` GoogleRpcStatus: The `Status` type defines a logical error model that is suitable for different programming environments, including REST APIs and RPC APIs. It is used by [gRPC](https://github.com/grpc). Each `Status` message contains three pieces of data: error code, error message, and error details. You can find out more about this error model and how to work with it in the [API Design Guide](https://cloud.google.com/apis/design/errors). - `code` integer: The status code, which should be an enum value of google.rpc.Code. - `details` object[]: A list of messages that carry the error details. There is a common set of message types for APIs to use. - `message` string: A developer-facing error message, which should be in English. Any user-facing error message should be localized and sent in the google.rpc.Status.details field, or localized by the client. - `updateTime` string, google-datetime: Output only. Timestamp when this NotebookExecutionJob was most recently updated. - `workbenchRuntime` GoogleCloudAiplatformV1NotebookExecutionJobWorkbenchRuntime: Configuration for a Workbench Instances-based environment. - `customContainerImage` GoogleCloudAiplatformV1NotebookExecutionJobWorkbenchRuntimeContainerImage: The definition of a user-provided container image to run the notebook execution in. Mirrors `google.cloud.notebooks.v2.ContainerImage`. - `repository` string: Required. The path to the container image repository. For example: `gcr.io/{project_id}/{image_name}`. - `tag` string: Optional. The tag of the container image. If unset, defaults to `latest`. - `vmImage` GoogleCloudAiplatformV1NotebookExecutionJobWorkbenchRuntimeVmImage: The definition of a Compute Engine VM image to run the notebook execution on. Mirrors `google.cloud.notebooks.v2.VmImage`. - `family` string: Use this VM image family to find the image; the newest image in this family is used. - `name` string: Use this VM image name to find the image. - `project` string: Required. The name of the Google Cloud project that this VM image belongs to. Format: `{project_id}`. - `notebookExecutionJobId` string: Optional. User specified ID for the NotebookExecutionJob. - `parent` string: Required. The resource name of the Location to create the NotebookExecutionJob. Format: `projects/{project}/locations/{location}` - `createPipelineJobRequest` GoogleCloudAiplatformV1CreatePipelineJobRequest: Request message for PipelineService.CreatePipelineJob. - `parent` string: Required. The resource name of the Location to create the PipelineJob in. Format: `projects/{project}/locations/{location}` - `pipelineJob` GoogleCloudAiplatformV1PipelineJob: An instance of a machine learning PipelineJob. - `createTime` string, google-datetime: Output only. Pipeline creation time. - `displayName` string: The display name of the Pipeline. The name can be up to 128 characters long and can consist of any UTF-8 characters. - `encryptionSpec` GoogleCloudAiplatformV1EncryptionSpec: Represents a customer-managed encryption key specification that can be applied to a Vertex AI resource. - `kmsKeyName` string: Required. Resource name of the Cloud KMS key used to protect the resource. The Cloud KMS key must be in the same region as the resource. It must have the format `projects/{project}/locations/{location}/keyRings/{key_ring}/cryptoKeys/{crypto_key}`. - `endTime` string, google-datetime: Output only. Pipeline end time. - `error` GoogleRpcStatus: The `Status` type defines a logical error model that is suitable for different programming environments, including REST APIs and RPC APIs. It is used by [gRPC](https://github.com/grpc). Each `Status` message contains three pieces of data: error code, error message, and error details. You can find out more about this error model and how to work with it in the [API Design Guide](https://cloud.google.com/apis/design/errors). - `code` integer: The status code, which should be an enum value of google.rpc.Code. - `details` object[]: A list of messages that carry the error details. There is a common set of message types for APIs to use. - `message` string: A developer-facing error message, which should be in English. Any user-facing error message should be localized and sent in the google.rpc.Status.details field, or localized by the client. - `jobDetail` GoogleCloudAiplatformV1PipelineJobDetail: The runtime detail of PipelineJob. - `pipelineContext` GoogleCloudAiplatformV1Context: Instance of a general context. - `createTime` string, google-datetime: Output only. Timestamp when this Context was created. - `description` string: Description of the Context - `displayName` string: User provided display name of the Context. May be up to 128 Unicode characters. - `etag` string: An eTag used to perform consistent read-modify-write updates. If not set, a blind "overwrite" update happens. - `labels` object: The labels with user-defined metadata to organize your Contexts. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. No more than 64 user labels can be associated with one Context (System labels are excluded). - `metadata` object: Properties of the Context. Top level metadata keys' heading and trailing spaces will be trimmed. The size of this field should not exceed 200KB. - `name` string: Immutable. The resource name of the Context. - `parentContexts` string[]: Output only. A list of resource names of Contexts that are parents of this Context. A Context may have at most 10 parent_contexts. - `schemaTitle` string: The title of the schema describing the metadata. Schema title and version is expected to be registered in earlier Create Schema calls. And both are used together as unique identifiers to identify schemas within the local metadata store. - `schemaVersion` string: The version of the schema in schema_name to use. Schema title and version is expected to be registered in earlier Create Schema calls. And both are used together as unique identifiers to identify schemas within the local metadata store. - `updateTime` string, google-datetime: Output only. Timestamp when this Context was last updated. - `pipelineRunContext` GoogleCloudAiplatformV1Context: Instance of a general context. - `createTime` string, google-datetime: Output only. Timestamp when this Context was created. - `description` string: Description of the Context - `displayName` string: User provided display name of the Context. May be up to 128 Unicode characters. - `etag` string: An eTag used to perform consistent read-modify-write updates. If not set, a blind "overwrite" update happens. - `labels` object: The labels with user-defined metadata to organize your Contexts. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. No more than 64 user labels can be associated with one Context (System labels are excluded). - `metadata` object: Properties of the Context. Top level metadata keys' heading and trailing spaces will be trimmed. The size of this field should not exceed 200KB. - `name` string: Immutable. The resource name of the Context. - `parentContexts` string[]: Output only. A list of resource names of Contexts that are parents of this Context. A Context may have at most 10 parent_contexts. - `schemaTitle` string: The title of the schema describing the metadata. Schema title and version is expected to be registered in earlier Create Schema calls. And both are used together as unique identifiers to identify schemas within the local metadata store. - `schemaVersion` string: The version of the schema in schema_name to use. Schema title and version is expected to be registered in earlier Create Schema calls. And both are used together as unique identifiers to identify schemas within the local metadata store. - `updateTime` string, google-datetime: Output only. Timestamp when this Context was last updated. - `taskDetails` GoogleCloudAiplatformV1PipelineTaskDetail[]: Output only. The runtime details of the tasks under the pipeline. - `createTime` string, google-datetime: Output only. Task create time. - `endTime` string, google-datetime: Output only. Task end time. - `error` GoogleRpcStatus: The `Status` type defines a logical error model that is suitable for different programming environments, including REST APIs and RPC APIs. It is used by [gRPC](https://github.com/grpc). Each `Status` message contains three pieces of data: error code, error message, and error details. You can find out more about this error model and how to work with it in the [API Design Guide](https://cloud.google.com/apis/design/errors). - … - `execution` GoogleCloudAiplatformV1Execution: Instance of a general execution. - … - `executorDetail` GoogleCloudAiplatformV1PipelineTaskExecutorDetail: The runtime detail of a pipeline executor. - … - `inputs` object: Output only. The runtime input artifacts of the task. - `outputs` object: Output only. The runtime output artifacts of the task. - `parentTaskId` string, int64: Output only. The id of the parent task if the task is within a component scope. Empty if the task is at the root level. - `pipelineTaskStatus` GoogleCloudAiplatformV1PipelineTaskDetailPipelineTaskStatus[]: Output only. A list of task status. This field keeps a record of task status evolving over time. - … - `startTime` string, google-datetime: Output only. Task start time. - `state` 'STATE_UNSPECIFIED' | 'PENDING' | 'RUNNING' | 'SUCCEEDED' | 'CANCEL_PENDING' | 'CANCELLING' | 'CANCELLED' | 'FAILED' | 'SKIPPED' | 'NOT_TRIGGERED': Output only. State of the task. - `taskId` string, int64: Output only. The system generated ID of the task. - `taskName` string: Output only. The user specified name of the task that is defined in pipeline_spec. - `taskUniqueName` string: Output only. The unique name of a task. This field is used by rerun pipeline job. Console UI and Vertex AI SDK will support triggering pipeline job reruns. The name is constructed by concatenating all the parent tasks name with the task name. For example, if a task named "child_task" has a parent task named "parent_task_1" and parent task 1 has a parent task named "parent_task_2", the task unique name will be "parent_task_2.parent_task_1.child_task". - `labels` object: The labels with user-defined metadata to organize PipelineJob. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels. Note there is some reserved label key for Vertex AI Pipelines. - `vertex-ai-pipelines-run-billing-id`, user set value will get overrided. - `name` string: Output only. The resource name of the PipelineJob. - `network` string: The full name of the Compute Engine [network](/compute/docs/networks-and-firewalls#networks) to which the Pipeline Job's workload should be peered. For example, `projects/12345/global/networks/myVPC`. [Format](/compute/docs/reference/rest/v1/networks/insert) is of the form `projects/{project}/global/networks/{network}`. Where {project} is a project number, as in `12345`, and {network} is a network name. Private services access must already be configured for the network. Pipeline job will apply the network configuration to the Google Cloud resources being launched, if applied, such as Vertex AI Training or Dataflow job. If left unspecified, the workload is not peered with any network. - `pipelineSpec` object: A compiled definition of a pipeline, represented as a `JSON` object. Defines the structure of the pipeline, including its components, tasks, and parameters. This specification is generated by compiling a pipeline function defined in `Python` using the `Kubeflow Pipelines SDK`. - `preflightValidations` boolean: Optional. Whether to do component level validations before job creation. - `pscInterfaceConfig` GoogleCloudAiplatformV1PscInterfaceConfig: Configuration for PSC-I. - `dnsPeeringConfigs` GoogleCloudAiplatformV1DnsPeeringConfig[]: Optional. DNS peering configurations. When specified, Vertex AI will attempt to configure DNS peering zones in the tenant project VPC to resolve the specified domains using the target network's Cloud DNS. The user must grant the dns.peer role to the Vertex AI Service Agent on the target project. - `domain` string: Required. The DNS name suffix of the zone being peered to, e.g., "my-internal-domain.corp.". Must end with a dot. - `targetNetwork` string: Required. The VPC network name in the target_project where the DNS zone specified by 'domain' is visible. - `targetProject` string: Required. The project ID hosting the Cloud DNS managed zone that contains the 'domain'. The Vertex AI Service Agent requires the dns.peer role on this project. - `networkAttachment` string: Optional. The name of the Compute Engine [network attachment](https://cloud.google.com/vpc/docs/about-network-attachments) to attach to the resource within the region and user project. To specify this field, you must have already [created a network attachment] (https://cloud.google.com/vpc/docs/create-manage-network-attachments#create-network-attachments). This field is only used for resources using PSC-I. - `reservedIpRanges` string[]: A list of names for the reserved ip ranges under the VPC network that can be used for this Pipeline Job's workload. If set, we will deploy the Pipeline Job's workload within the provided ip ranges. Otherwise, the job will be deployed to any ip ranges under the provided VPC network. Example: ['vertex-ai-ip-range']. - `runtimeConfig` GoogleCloudAiplatformV1PipelineJobRuntimeConfig: The runtime config of a PipelineJob. - `failurePolicy` 'PIPELINE_FAILURE_POLICY_UNSPECIFIED' | 'PIPELINE_FAILURE_POLICY_FAIL_SLOW' | 'PIPELINE_FAILURE_POLICY_FAIL_FAST': Represents the failure policy of a pipeline. 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. However, if a pipeline is set to PIPELINE_FAILURE_POLICY_FAIL_FAST, it will stop scheduling any new tasks when a task has failed. Any scheduled tasks will continue to completion. - `gcsOutputDirectory` string: Required. A path in a Cloud Storage bucket, which will be treated as the root output directory of the pipeline. It is used by the system to generate the paths of output artifacts. The artifact paths are generated with a sub-path pattern `{job_id}/{task_id}/{output_key}` under the specified output directory. The service account specified in this pipeline must have the `storage.objects.get` and `storage.objects.create` permissions for this bucket. - `inputArtifacts` object: The runtime artifacts of the PipelineJob. The key will be the input artifact name and the value would be one of the InputArtifact. - `parameterValues` object: The runtime parameters of the PipelineJob. The parameters will be passed into PipelineJob.pipeline_spec to replace the placeholders at runtime. This field is used by pipelines built using `PipelineJob.pipeline_spec.schema_version` 2.1.0, such as pipelines built using Kubeflow Pipelines SDK 1.9 or higher and the v2 DSL. - `parameters` object: Deprecated. Use RuntimeConfig.parameter_values instead. The runtime parameters of the PipelineJob. The parameters will be passed into PipelineJob.pipeline_spec to replace the placeholders at runtime. This field is used by pipelines built using `PipelineJob.pipeline_spec.schema_version` 2.0.0 or lower, such as pipelines built using Kubeflow Pipelines SDK 1.8 or lower. - `scheduleName` string: Output only. The schedule resource name. Only returned if the Pipeline is created by Schedule API. - `serviceAccount` string: The service account that the pipeline workload runs as. If not specified, the Compute Engine default service account in the project will be used. See https://cloud.google.com/compute/docs/access/service-accounts#default_service_account Users starting the pipeline must have the `iam.serviceAccounts.actAs` permission on this service account. - `startTime` string, google-datetime: Output only. Pipeline start time. - `state` 'PIPELINE_STATE_UNSPECIFIED' | 'PIPELINE_STATE_QUEUED' | 'PIPELINE_STATE_PENDING' | 'PIPELINE_STATE_RUNNING' | 'PIPELINE_STATE_SUCCEEDED' | 'PIPELINE_STATE_FAILED' | 'PIPELINE_STATE_CANCELLING' | 'PIPELINE_STATE_CANCELLED' | 'PIPELINE_STATE_PAUSED': Output only. The detailed state of the job. - `templateMetadata` GoogleCloudAiplatformV1PipelineTemplateMetadata: Pipeline template metadata if PipelineJob.template_uri is from supported template registry. Currently, the only supported registry is Artifact Registry. - `version` string: The version_name in artifact registry. Will always be presented in output if the PipelineJob.template_uri is from supported template registry. Format is "sha256:abcdef123456...". - `templateUri` string: A template uri from where the PipelineJob.pipeline_spec, if empty, will be downloaded. Currently, only uri from Vertex Template Registry & Gallery is supported. Reference to https://cloud.google.com/vertex-ai/docs/pipelines/create-pipeline-template. - `updateTime` string, google-datetime: Output only. Timestamp when this PipelineJob was most recently updated. - `pipelineJobId` string: The ID to use for the PipelineJob, which will become the final component of the PipelineJob name. If not provided, an ID will be automatically generated. This value should be less than 128 characters, and valid characters are `/a-z-/`. - `createTime` string, google-datetime: Output only. Timestamp when this Schedule was created. - `cron` string: Cron schedule (https://en.wikipedia.org/wiki/Cron) to launch scheduled runs. To explicitly set a timezone to the cron tab, apply a prefix in the cron tab: "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 * * * *". - `displayName` string: Required. User provided name of the Schedule. The name can be up to 128 characters long and can consist of any UTF-8 characters. - `endTime` string, google-datetime: Optional. Timestamp after which no new runs can be scheduled. If specified, The schedule will be completed when either end_time is reached or when scheduled_run_count >= max_run_count. If not specified, new runs will keep getting scheduled until this Schedule is paused or deleted. Already scheduled runs will be allowed to complete. Unset if not specified. - `lastPauseTime` string, google-datetime: Output only. Timestamp when this Schedule was last paused. Unset if never paused. - `lastResumeTime` string, google-datetime: Output only. Timestamp when this Schedule was last resumed. Unset if never resumed from pause. - `lastScheduledRunResponse` GoogleCloudAiplatformV1ScheduleRunResponse: Status of a scheduled run. - `runResponse` string: The response of the scheduled run. - `scheduledRunTime` string, google-datetime: The scheduled run time based on the user-specified schedule. - `maxConcurrentActiveRunCount` string, int64: Optional. Specifies the maximum number of active runs that can be executed concurrently for this Schedule. This limits the number of runs that can be in a non-terminal state at the same time. Currently, this field is only supported for requests of type CreatePipelineJobRequest. - `maxConcurrentRunCount` string, int64: Required. Maximum number of runs that can be started concurrently for this Schedule. This is the limit for starting the scheduled requests and not the execution of the operations/jobs created by the requests (if applicable). - `maxRunCount` string, int64: 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. If not specified, new runs will keep getting scheduled until this Schedule is paused or deleted. Already scheduled runs will be allowed to complete. Unset if not specified. - `name` string: Immutable. The resource name of the Schedule. - `nextRunTime` string, google-datetime: Output only. Timestamp when this Schedule should schedule the next run. Having a next_run_time in the past means the runs are being started behind schedule. - `startTime` string, google-datetime: Optional. Timestamp after which the first run can be scheduled. Default to Schedule create time if not specified. - `startedRunCount` string, int64: Output only. The number of runs started by this schedule. - `state` 'STATE_UNSPECIFIED' | 'ACTIVE' | 'PAUSED' | 'COMPLETED': Output only. The state of this Schedule. - `updateTime` string, google-datetime: Output only. Timestamp when this Schedule was updated. --- [API](https://skmtc.dev/google/apis/aiplatform.md) - [All operations](https://skmtc.dev/google/apis/aiplatform/llms.txt) - [OpenAPI document](https://skmtc.dev/google/apis/aiplatform/revisions/3aca3b348f0c?raw)