Viewing docs for Google Cloud v9.29.0
published on Wednesday, Jun 24, 2026 by Pulumi
published on Wednesday, Jun 24, 2026 by Pulumi
Viewing docs for Google Cloud v9.29.0
published on Wednesday, Jun 24, 2026 by Pulumi
published on Wednesday, Jun 24, 2026 by Pulumi
An online schedule that triggers running pipeline jobs or notebook execution jobs.
Example Usage
Vertex Ai Schedule
import * as pulumi from "@pulumi/pulumi";
import * as gcp from "@pulumi/gcp";
const project = gcp.organizations.getProject({});
const bucket = new gcp.storage.Bucket("bucket", {
name: "pipeline-job",
location: "us-central1",
uniformBucketLevelAccess: true,
forceDestroy: true,
});
const schedule = new gcp.vertex.AiSchedule("schedule", {
displayName: "test-schedule",
location: "us-central1",
maxConcurrentRunCount: "2",
cron: "*/5 * * * *",
allowQueueing: true,
maxConcurrentActiveRunCount: "2",
maxRunCount: "10",
startTime: "2030-01-01T00:00:00Z",
endTime: "2030-01-02T00:00:00Z",
createPipelineJobRequest: {
parent: project.then(project => `projects/${project.projectId}/locations/us-central1`),
pipelineJob: {
displayName: "test-pipeline-job",
preflightValidations: true,
labels: {
key: "value-one",
},
pipelineSpec: JSON.stringify({
pipelineInfo: {
name: "hello-world",
},
root: {
dag: {
tasks: {},
},
},
schemaVersion: "2.1.0",
sdkVersion: "kfp-2.0.0",
}),
runtimeConfig: {
gcsOutputDirectory: pulumi.interpolate`gs://${bucket.name}/pipeline_root`,
failurePolicy: "PIPELINE_FAILURE_POLICY_FAIL_FAST",
},
},
},
});
import pulumi
import json
import pulumi_gcp as gcp
project = gcp.organizations.get_project()
bucket = gcp.storage.Bucket("bucket",
name="pipeline-job",
location="us-central1",
uniform_bucket_level_access=True,
force_destroy=True)
schedule = gcp.vertex.AiSchedule("schedule",
display_name="test-schedule",
location="us-central1",
max_concurrent_run_count="2",
cron="*/5 * * * *",
allow_queueing=True,
max_concurrent_active_run_count="2",
max_run_count="10",
start_time="2030-01-01T00:00:00Z",
end_time="2030-01-02T00:00:00Z",
create_pipeline_job_request={
"parent": f"projects/{project.project_id}/locations/us-central1",
"pipeline_job": {
"display_name": "test-pipeline-job",
"preflight_validations": True,
"labels": {
"key": "value-one",
},
"pipeline_spec": json.dumps({
"pipelineInfo": {
"name": "hello-world",
},
"root": {
"dag": {
"tasks": {},
},
},
"schemaVersion": "2.1.0",
"sdkVersion": "kfp-2.0.0",
}),
"runtime_config": {
"gcs_output_directory": bucket.name.apply(lambda name: f"gs://{name}/pipeline_root"),
"failure_policy": "PIPELINE_FAILURE_POLICY_FAIL_FAST",
},
},
})
package main
import (
"encoding/json"
"fmt"
"github.com/pulumi/pulumi-gcp/sdk/v9/go/gcp/organizations"
"github.com/pulumi/pulumi-gcp/sdk/v9/go/gcp/storage"
"github.com/pulumi/pulumi-gcp/sdk/v9/go/gcp/vertex"
"github.com/pulumi/pulumi/sdk/v3/go/pulumi"
)
func main() {
pulumi.Run(func(ctx *pulumi.Context) error {
project, err := organizations.LookupProject(ctx, &organizations.LookupProjectArgs{}, nil)
if err != nil {
return err
}
bucket, err := storage.NewBucket(ctx, "bucket", &storage.BucketArgs{
Name: pulumi.String("pipeline-job"),
Location: pulumi.String("us-central1"),
UniformBucketLevelAccess: pulumi.Bool(true),
ForceDestroy: pulumi.Bool(true),
})
if err != nil {
return err
}
tmpJSON0, err := json.Marshal(map[string]interface{}{
"pipelineInfo": map[string]interface{}{
"name": "hello-world",
},
"root": map[string]interface{}{
"dag": map[string]interface{}{
"tasks": map[string]interface{}{},
},
},
"schemaVersion": "2.1.0",
"sdkVersion": "kfp-2.0.0",
})
if err != nil {
return err
}
json0 := string(tmpJSON0)
_, err = vertex.NewAiSchedule(ctx, "schedule", &vertex.AiScheduleArgs{
DisplayName: pulumi.String("test-schedule"),
Location: pulumi.String("us-central1"),
MaxConcurrentRunCount: pulumi.String("2"),
Cron: pulumi.String("*/5 * * * *"),
AllowQueueing: pulumi.Bool(true),
MaxConcurrentActiveRunCount: pulumi.String("2"),
MaxRunCount: pulumi.String("10"),
StartTime: pulumi.String("2030-01-01T00:00:00Z"),
EndTime: pulumi.String("2030-01-02T00:00:00Z"),
CreatePipelineJobRequest: &vertex.AiScheduleCreatePipelineJobRequestArgs{
Parent: pulumi.Sprintf("projects/%v/locations/us-central1", project.ProjectId),
PipelineJob: &vertex.AiScheduleCreatePipelineJobRequestPipelineJobArgs{
DisplayName: pulumi.String("test-pipeline-job"),
PreflightValidations: pulumi.Bool(true),
Labels: pulumi.StringMap{
"key": pulumi.String("value-one"),
},
PipelineSpec: pulumi.String(json0),
RuntimeConfig: &vertex.AiScheduleCreatePipelineJobRequestPipelineJobRuntimeConfigArgs{
GcsOutputDirectory: bucket.Name.ApplyT(func(name string) (string, error) {
return fmt.Sprintf("gs://%v/pipeline_root", name), nil
}).(pulumi.StringOutput),
FailurePolicy: pulumi.String("PIPELINE_FAILURE_POLICY_FAIL_FAST"),
},
},
},
})
if err != nil {
return err
}
return nil
})
}
using System.Collections.Generic;
using System.Linq;
using System.Text.Json;
using Pulumi;
using Gcp = Pulumi.Gcp;
return await Deployment.RunAsync(() =>
{
var project = Gcp.Organizations.GetProject.Invoke();
var bucket = new Gcp.Storage.Bucket("bucket", new()
{
Name = "pipeline-job",
Location = "us-central1",
UniformBucketLevelAccess = true,
ForceDestroy = true,
});
var schedule = new Gcp.Vertex.AiSchedule("schedule", new()
{
DisplayName = "test-schedule",
Location = "us-central1",
MaxConcurrentRunCount = "2",
Cron = "*/5 * * * *",
AllowQueueing = true,
MaxConcurrentActiveRunCount = "2",
MaxRunCount = "10",
StartTime = "2030-01-01T00:00:00Z",
EndTime = "2030-01-02T00:00:00Z",
CreatePipelineJobRequest = new Gcp.Vertex.Inputs.AiScheduleCreatePipelineJobRequestArgs
{
Parent = $"projects/{project.Apply(getProjectResult => getProjectResult.ProjectId)}/locations/us-central1",
PipelineJob = new Gcp.Vertex.Inputs.AiScheduleCreatePipelineJobRequestPipelineJobArgs
{
DisplayName = "test-pipeline-job",
PreflightValidations = true,
Labels =
{
{ "key", "value-one" },
},
PipelineSpec = JsonSerializer.Serialize(new Dictionary<string, object?>
{
["pipelineInfo"] = new Dictionary<string, object?>
{
["name"] = "hello-world",
},
["root"] = new Dictionary<string, object?>
{
["dag"] = new Dictionary<string, object?>
{
["tasks"] = new Dictionary<string, object?>
{
},
},
},
["schemaVersion"] = "2.1.0",
["sdkVersion"] = "kfp-2.0.0",
}),
RuntimeConfig = new Gcp.Vertex.Inputs.AiScheduleCreatePipelineJobRequestPipelineJobRuntimeConfigArgs
{
GcsOutputDirectory = bucket.Name.Apply(name => $"gs://{name}/pipeline_root"),
FailurePolicy = "PIPELINE_FAILURE_POLICY_FAIL_FAST",
},
},
},
});
});
package generated_program;
import com.pulumi.Context;
import com.pulumi.Pulumi;
import com.pulumi.core.Output;
import com.pulumi.gcp.organizations.OrganizationsFunctions;
import com.pulumi.gcp.organizations.inputs.GetProjectArgs;
import com.pulumi.gcp.storage.Bucket;
import com.pulumi.gcp.storage.BucketArgs;
import com.pulumi.gcp.vertex.AiSchedule;
import com.pulumi.gcp.vertex.AiScheduleArgs;
import com.pulumi.gcp.vertex.inputs.AiScheduleCreatePipelineJobRequestArgs;
import com.pulumi.gcp.vertex.inputs.AiScheduleCreatePipelineJobRequestPipelineJobArgs;
import com.pulumi.gcp.vertex.inputs.AiScheduleCreatePipelineJobRequestPipelineJobRuntimeConfigArgs;
import static com.pulumi.codegen.internal.Serialization.*;
import java.util.ArrayList;
import java.util.Arrays;
import java.util.Map;
import java.io.File;
import java.nio.file.Files;
import java.nio.file.Paths;
public class App {
public static void main(String[] args) {
Pulumi.run(App::stack);
}
public static void stack(Context ctx) {
final var project = OrganizationsFunctions.getProject(GetProjectArgs.builder()
.build());
var bucket = new Bucket("bucket", BucketArgs.builder()
.name("pipeline-job")
.location("us-central1")
.uniformBucketLevelAccess(true)
.forceDestroy(true)
.build());
var schedule = new AiSchedule("schedule", AiScheduleArgs.builder()
.displayName("test-schedule")
.location("us-central1")
.maxConcurrentRunCount("2")
.cron("*/5 * * * *")
.allowQueueing(true)
.maxConcurrentActiveRunCount("2")
.maxRunCount("10")
.startTime("2030-01-01T00:00:00Z")
.endTime("2030-01-02T00:00:00Z")
.createPipelineJobRequest(AiScheduleCreatePipelineJobRequestArgs.builder()
.parent(String.format("projects/%s/locations/us-central1", project.projectId()))
.pipelineJob(AiScheduleCreatePipelineJobRequestPipelineJobArgs.builder()
.displayName("test-pipeline-job")
.preflightValidations(true)
.labels(Map.of("key", "value-one"))
.pipelineSpec(serializeJson(
jsonObject(
jsonProperty("pipelineInfo", jsonObject(
jsonProperty("name", "hello-world")
)),
jsonProperty("root", jsonObject(
jsonProperty("dag", jsonObject(
jsonProperty("tasks", jsonObject(
))
))
)),
jsonProperty("schemaVersion", "2.1.0"),
jsonProperty("sdkVersion", "kfp-2.0.0")
)))
.runtimeConfig(AiScheduleCreatePipelineJobRequestPipelineJobRuntimeConfigArgs.builder()
.gcsOutputDirectory(bucket.name().applyValue(_name -> String.format("gs://%s/pipeline_root", _name)))
.failurePolicy("PIPELINE_FAILURE_POLICY_FAIL_FAST")
.build())
.build())
.build())
.build());
}
}
resources:
bucket:
type: gcp:storage:Bucket
properties:
name: pipeline-job
location: us-central1
uniformBucketLevelAccess: true
forceDestroy: true
schedule:
type: gcp:vertex:AiSchedule
properties:
displayName: test-schedule
location: us-central1
maxConcurrentRunCount: 2
cron: '*/5 * * * *'
allowQueueing: true
maxConcurrentActiveRunCount: 2
maxRunCount: '10'
startTime: 2030-01-01T00:00:00Z
endTime: 2030-01-02T00:00:00Z
createPipelineJobRequest:
parent: projects/${project.projectId}/locations/us-central1
pipelineJob:
displayName: test-pipeline-job
preflightValidations: true
labels:
key: value-one
pipelineSpec:
fn::toJSON:
pipelineInfo:
name: hello-world
root:
dag:
tasks: {}
schemaVersion: 2.1.0
sdkVersion: kfp-2.0.0
runtimeConfig:
gcsOutputDirectory: gs://${bucket.name}/pipeline_root
failurePolicy: PIPELINE_FAILURE_POLICY_FAIL_FAST
variables:
project:
fn::invoke:
function: gcp:organizations:getProject
arguments: {}
pulumi {
required_providers {
gcp = {
source = "pulumi/gcp"
}
}
}
data "gcp_organizations_getproject" "project" {
}
resource "gcp_storage_bucket" "bucket" {
name = "pipeline-job"
location = "us-central1"
uniform_bucket_level_access = true
force_destroy = true
}
resource "gcp_vertex_aischedule" "schedule" {
display_name = "test-schedule"
location = "us-central1"
max_concurrent_run_count = 2
cron = "*/5 * * * *"
allow_queueing = true
max_concurrent_active_run_count = 2
max_run_count = "10"
start_time = "2030-01-01T00:00:00Z"
end_time = "2030-01-02T00:00:00Z"
create_pipeline_job_request = {
parent ="projects/${data.gcp_organizations_getproject.project.project_id}/locations/us-central1"
pipeline_job = {
display_name = "test-pipeline-job"
preflight_validations = true
labels = {
"key" = "value-one"
}
pipeline_spec = jsonencode({
"pipelineInfo" = {
"name" = "hello-world"
}
"root" = {
"dag" = {
"tasks" = {}
}
}
"schemaVersion" = "2.1.0"
"sdkVersion" = "kfp-2.0.0"
})
runtime_config = {
gcs_output_directory ="gs://${gcp_storage_bucket.bucket.name}/pipeline_root"
failure_policy = "PIPELINE_FAILURE_POLICY_FAIL_FAST"
}
}
}
}
Vertex Ai Schedule Notebook
import * as pulumi from "@pulumi/pulumi";
import * as gcp from "@pulumi/gcp";
const project = gcp.organizations.getProject({});
const bucket = new gcp.storage.Bucket("bucket", {
name: "notebook-job",
location: "us-central1",
uniformBucketLevelAccess: true,
forceDestroy: true,
});
const notebook = new gcp.storage.BucketObject("notebook", {
name: "notebook.ipynb",
content: "{ \"cells\": [], \"metadata\": {}, \"nbformat\": 4, \"nbminor\": 0 }",
bucket: bucket.name,
});
const schedule = new gcp.vertex.AiSchedule("schedule", {
displayName: "test-schedule-notebook",
location: "us-central1",
maxConcurrentRunCount: "2",
cron: "*/5 * * * *",
startTime: "2030-01-01T00:00:00Z",
createNotebookExecutionJobRequest: {
parent: project.then(project => `projects/${project.projectId}/locations/us-central1`),
notebookExecutionJob: {
displayName: "test-notebook-execution-job",
gcsOutputUri: pulumi.interpolate`gs://${bucket.name}`,
serviceAccount: project.then(project => `${project.number}-compute@developer.gserviceaccount.com`),
gcsNotebookSource: {
uri: pulumi.interpolate`gs://${bucket.name}/${notebook.name}`,
generation: notebook.generation.apply(x =>String(x)),
},
customEnvironmentSpec: {
machineSpec: {
machineType: "n1-standard-4",
acceleratorType: "NVIDIA_TESLA_T4",
acceleratorCount: 1,
},
persistentDiskSpec: {
diskSizeGb: "100",
diskType: "pd-standard",
},
networkSpec: {
enableInternetAccess: true,
},
},
executionTimeout: "86400s",
kernelName: "python3",
labels: {
test: "value",
},
},
},
});
import pulumi
import pulumi_gcp as gcp
project = gcp.organizations.get_project()
bucket = gcp.storage.Bucket("bucket",
name="notebook-job",
location="us-central1",
uniform_bucket_level_access=True,
force_destroy=True)
notebook = gcp.storage.BucketObject("notebook",
name="notebook.ipynb",
content="{ \"cells\": [], \"metadata\": {}, \"nbformat\": 4, \"nbminor\": 0 }",
bucket=bucket.name)
schedule = gcp.vertex.AiSchedule("schedule",
display_name="test-schedule-notebook",
location="us-central1",
max_concurrent_run_count="2",
cron="*/5 * * * *",
start_time="2030-01-01T00:00:00Z",
create_notebook_execution_job_request={
"parent": f"projects/{project.project_id}/locations/us-central1",
"notebook_execution_job": {
"display_name": "test-notebook-execution-job",
"gcs_output_uri": bucket.name.apply(lambda name: f"gs://{name}"),
"service_account": f"{project.number}-compute@developer.gserviceaccount.com",
"gcs_notebook_source": {
"uri": pulumi.Output.all(
bucketName=bucket.name,
notebookName=notebook.name
).apply(lambda resolved_outputs: f"gs://{resolved_outputs['bucketName']}/{resolved_outputs['notebookName']}")
,
"generation": notebook.generation.apply(lambda x: str(x)),
},
"custom_environment_spec": {
"machine_spec": {
"machine_type": "n1-standard-4",
"accelerator_type": "NVIDIA_TESLA_T4",
"accelerator_count": 1,
},
"persistent_disk_spec": {
"disk_size_gb": "100",
"disk_type": "pd-standard",
},
"network_spec": {
"enable_internet_access": True,
},
},
"execution_timeout": "86400s",
"kernel_name": "python3",
"labels": {
"test": "value",
},
},
})
package main
import (
"fmt"
"github.com/pulumi/pulumi-gcp/sdk/v9/go/gcp/organizations"
"github.com/pulumi/pulumi-gcp/sdk/v9/go/gcp/storage"
"github.com/pulumi/pulumi-gcp/sdk/v9/go/gcp/vertex"
"github.com/pulumi/pulumi/sdk/v3/go/pulumi"
)
func main() {
pulumi.Run(func(ctx *pulumi.Context) error {
project, err := organizations.LookupProject(ctx, &organizations.LookupProjectArgs{}, nil)
if err != nil {
return err
}
bucket, err := storage.NewBucket(ctx, "bucket", &storage.BucketArgs{
Name: pulumi.String("notebook-job"),
Location: pulumi.String("us-central1"),
UniformBucketLevelAccess: pulumi.Bool(true),
ForceDestroy: pulumi.Bool(true),
})
if err != nil {
return err
}
notebook, err := storage.NewBucketObject(ctx, "notebook", &storage.BucketObjectArgs{
Name: pulumi.String("notebook.ipynb"),
Content: pulumi.String("{ \"cells\": [], \"metadata\": {}, \"nbformat\": 4, \"nbminor\": 0 }"),
Bucket: bucket.Name,
})
if err != nil {
return err
}
_, err = vertex.NewAiSchedule(ctx, "schedule", &vertex.AiScheduleArgs{
DisplayName: pulumi.String("test-schedule-notebook"),
Location: pulumi.String("us-central1"),
MaxConcurrentRunCount: pulumi.String("2"),
Cron: pulumi.String("*/5 * * * *"),
StartTime: pulumi.String("2030-01-01T00:00:00Z"),
CreateNotebookExecutionJobRequest: &vertex.AiScheduleCreateNotebookExecutionJobRequestArgs{
Parent: pulumi.Sprintf("projects/%v/locations/us-central1", project.ProjectId),
NotebookExecutionJob: &vertex.AiScheduleCreateNotebookExecutionJobRequestNotebookExecutionJobArgs{
DisplayName: pulumi.String("test-notebook-execution-job"),
GcsOutputUri: bucket.Name.ApplyT(func(name string) (string, error) {
return fmt.Sprintf("gs://%v", name), nil
}).(pulumi.StringOutput),
ServiceAccount: pulumi.Sprintf("%v-compute@developer.gserviceaccount.com", project.Number),
GcsNotebookSource: &vertex.AiScheduleCreateNotebookExecutionJobRequestNotebookExecutionJobGcsNotebookSourceArgs{
Uri: pulumi.All(bucket.Name, notebook.Name).ApplyT(func(_args []interface{}) (string, error) {
bucketName := _args[0].(string)
notebookName := _args[1].(string)
return fmt.Sprintf("gs://%v/%v", bucketName, notebookName), nil
}).(pulumi.StringOutput),
Generation: notebook.Generation,
},
CustomEnvironmentSpec: &vertex.AiScheduleCreateNotebookExecutionJobRequestNotebookExecutionJobCustomEnvironmentSpecArgs{
MachineSpec: &vertex.AiScheduleCreateNotebookExecutionJobRequestNotebookExecutionJobCustomEnvironmentSpecMachineSpecArgs{
MachineType: pulumi.String("n1-standard-4"),
AcceleratorType: pulumi.String("NVIDIA_TESLA_T4"),
AcceleratorCount: pulumi.Int(1),
},
PersistentDiskSpec: &vertex.AiScheduleCreateNotebookExecutionJobRequestNotebookExecutionJobCustomEnvironmentSpecPersistentDiskSpecArgs{
DiskSizeGb: pulumi.String("100"),
DiskType: pulumi.String("pd-standard"),
},
NetworkSpec: &vertex.AiScheduleCreateNotebookExecutionJobRequestNotebookExecutionJobCustomEnvironmentSpecNetworkSpecArgs{
EnableInternetAccess: pulumi.Bool(true),
},
},
ExecutionTimeout: pulumi.String("86400s"),
KernelName: pulumi.String("python3"),
Labels: pulumi.StringMap{
"test": pulumi.String("value"),
},
},
},
})
if err != nil {
return err
}
return nil
})
}
using System.Collections.Generic;
using System.Linq;
using Pulumi;
using Gcp = Pulumi.Gcp;
return await Deployment.RunAsync(() =>
{
var project = Gcp.Organizations.GetProject.Invoke();
var bucket = new Gcp.Storage.Bucket("bucket", new()
{
Name = "notebook-job",
Location = "us-central1",
UniformBucketLevelAccess = true,
ForceDestroy = true,
});
var notebook = new Gcp.Storage.BucketObject("notebook", new()
{
Name = "notebook.ipynb",
Content = "{ \"cells\": [], \"metadata\": {}, \"nbformat\": 4, \"nbminor\": 0 }",
Bucket = bucket.Name,
});
var schedule = new Gcp.Vertex.AiSchedule("schedule", new()
{
DisplayName = "test-schedule-notebook",
Location = "us-central1",
MaxConcurrentRunCount = "2",
Cron = "*/5 * * * *",
StartTime = "2030-01-01T00:00:00Z",
CreateNotebookExecutionJobRequest = new Gcp.Vertex.Inputs.AiScheduleCreateNotebookExecutionJobRequestArgs
{
Parent = $"projects/{project.Apply(getProjectResult => getProjectResult.ProjectId)}/locations/us-central1",
NotebookExecutionJob = new Gcp.Vertex.Inputs.AiScheduleCreateNotebookExecutionJobRequestNotebookExecutionJobArgs
{
DisplayName = "test-notebook-execution-job",
GcsOutputUri = bucket.Name.Apply(name => $"gs://{name}"),
ServiceAccount = $"{project.Apply(getProjectResult => getProjectResult.Number)}-compute@developer.gserviceaccount.com",
GcsNotebookSource = new Gcp.Vertex.Inputs.AiScheduleCreateNotebookExecutionJobRequestNotebookExecutionJobGcsNotebookSourceArgs
{
Uri = Output.Tuple(bucket.Name, notebook.Name).Apply(values =>
{
var bucketName = values.Item1;
var notebookName = values.Item2;
return $"gs://{bucketName}/{notebookName}";
}),
Generation = notebook.Generation,
},
CustomEnvironmentSpec = new Gcp.Vertex.Inputs.AiScheduleCreateNotebookExecutionJobRequestNotebookExecutionJobCustomEnvironmentSpecArgs
{
MachineSpec = new Gcp.Vertex.Inputs.AiScheduleCreateNotebookExecutionJobRequestNotebookExecutionJobCustomEnvironmentSpecMachineSpecArgs
{
MachineType = "n1-standard-4",
AcceleratorType = "NVIDIA_TESLA_T4",
AcceleratorCount = 1,
},
PersistentDiskSpec = new Gcp.Vertex.Inputs.AiScheduleCreateNotebookExecutionJobRequestNotebookExecutionJobCustomEnvironmentSpecPersistentDiskSpecArgs
{
DiskSizeGb = "100",
DiskType = "pd-standard",
},
NetworkSpec = new Gcp.Vertex.Inputs.AiScheduleCreateNotebookExecutionJobRequestNotebookExecutionJobCustomEnvironmentSpecNetworkSpecArgs
{
EnableInternetAccess = true,
},
},
ExecutionTimeout = "86400s",
KernelName = "python3",
Labels =
{
{ "test", "value" },
},
},
},
});
});
package generated_program;
import com.pulumi.Context;
import com.pulumi.Pulumi;
import com.pulumi.core.Output;
import com.pulumi.gcp.organizations.OrganizationsFunctions;
import com.pulumi.gcp.organizations.inputs.GetProjectArgs;
import com.pulumi.gcp.storage.Bucket;
import com.pulumi.gcp.storage.BucketArgs;
import com.pulumi.gcp.storage.BucketObject;
import com.pulumi.gcp.storage.BucketObjectArgs;
import com.pulumi.gcp.vertex.AiSchedule;
import com.pulumi.gcp.vertex.AiScheduleArgs;
import com.pulumi.gcp.vertex.inputs.AiScheduleCreateNotebookExecutionJobRequestArgs;
import com.pulumi.gcp.vertex.inputs.AiScheduleCreateNotebookExecutionJobRequestNotebookExecutionJobArgs;
import com.pulumi.gcp.vertex.inputs.AiScheduleCreateNotebookExecutionJobRequestNotebookExecutionJobGcsNotebookSourceArgs;
import com.pulumi.gcp.vertex.inputs.AiScheduleCreateNotebookExecutionJobRequestNotebookExecutionJobCustomEnvironmentSpecArgs;
import com.pulumi.gcp.vertex.inputs.AiScheduleCreateNotebookExecutionJobRequestNotebookExecutionJobCustomEnvironmentSpecMachineSpecArgs;
import com.pulumi.gcp.vertex.inputs.AiScheduleCreateNotebookExecutionJobRequestNotebookExecutionJobCustomEnvironmentSpecPersistentDiskSpecArgs;
import com.pulumi.gcp.vertex.inputs.AiScheduleCreateNotebookExecutionJobRequestNotebookExecutionJobCustomEnvironmentSpecNetworkSpecArgs;
import java.util.ArrayList;
import java.util.Arrays;
import java.util.Map;
import java.io.File;
import java.nio.file.Files;
import java.nio.file.Paths;
public class App {
public static void main(String[] args) {
Pulumi.run(App::stack);
}
public static void stack(Context ctx) {
final var project = OrganizationsFunctions.getProject(GetProjectArgs.builder()
.build());
var bucket = new Bucket("bucket", BucketArgs.builder()
.name("notebook-job")
.location("us-central1")
.uniformBucketLevelAccess(true)
.forceDestroy(true)
.build());
var notebook = new BucketObject("notebook", BucketObjectArgs.builder()
.name("notebook.ipynb")
.content("{ \"cells\": [], \"metadata\": {}, \"nbformat\": 4, \"nbminor\": 0 }")
.bucket(bucket.name())
.build());
var schedule = new AiSchedule("schedule", AiScheduleArgs.builder()
.displayName("test-schedule-notebook")
.location("us-central1")
.maxConcurrentRunCount("2")
.cron("*/5 * * * *")
.startTime("2030-01-01T00:00:00Z")
.createNotebookExecutionJobRequest(AiScheduleCreateNotebookExecutionJobRequestArgs.builder()
.parent(String.format("projects/%s/locations/us-central1", project.projectId()))
.notebookExecutionJob(AiScheduleCreateNotebookExecutionJobRequestNotebookExecutionJobArgs.builder()
.displayName("test-notebook-execution-job")
.gcsOutputUri(bucket.name().applyValue(_name -> String.format("gs://%s", _name)))
.serviceAccount(String.format("%s-compute@developer.gserviceaccount.com", project.number()))
.gcsNotebookSource(AiScheduleCreateNotebookExecutionJobRequestNotebookExecutionJobGcsNotebookSourceArgs.builder()
.uri(Output.tuple(bucket.name(), notebook.name()).applyValue(values -> {
var bucketName = values.t1;
var notebookName = values.t2;
return String.format("gs://%s/%s", bucketName,notebookName);
}))
.generation(notebook.generation())
.build())
.customEnvironmentSpec(AiScheduleCreateNotebookExecutionJobRequestNotebookExecutionJobCustomEnvironmentSpecArgs.builder()
.machineSpec(AiScheduleCreateNotebookExecutionJobRequestNotebookExecutionJobCustomEnvironmentSpecMachineSpecArgs.builder()
.machineType("n1-standard-4")
.acceleratorType("NVIDIA_TESLA_T4")
.acceleratorCount(1)
.build())
.persistentDiskSpec(AiScheduleCreateNotebookExecutionJobRequestNotebookExecutionJobCustomEnvironmentSpecPersistentDiskSpecArgs.builder()
.diskSizeGb("100")
.diskType("pd-standard")
.build())
.networkSpec(AiScheduleCreateNotebookExecutionJobRequestNotebookExecutionJobCustomEnvironmentSpecNetworkSpecArgs.builder()
.enableInternetAccess(true)
.build())
.build())
.executionTimeout("86400s")
.kernelName("python3")
.labels(Map.of("test", "value"))
.build())
.build())
.build());
}
}
resources:
bucket:
type: gcp:storage:Bucket
properties:
name: notebook-job
location: us-central1
uniformBucketLevelAccess: true
forceDestroy: true
notebook:
type: gcp:storage:BucketObject
properties:
name: notebook.ipynb
content: '{ "cells": [], "metadata": {}, "nbformat": 4, "nbminor": 0 }'
bucket: ${bucket.name}
schedule:
type: gcp:vertex:AiSchedule
properties:
displayName: test-schedule-notebook
location: us-central1
maxConcurrentRunCount: 2
cron: '*/5 * * * *'
startTime: 2030-01-01T00:00:00Z
createNotebookExecutionJobRequest:
parent: projects/${project.projectId}/locations/us-central1
notebookExecutionJob:
displayName: test-notebook-execution-job
gcsOutputUri: gs://${bucket.name}
serviceAccount: ${project.number}-compute@developer.gserviceaccount.com
gcsNotebookSource:
uri: gs://${bucket.name}/${notebook.name}
generation: ${notebook.generation}
customEnvironmentSpec:
machineSpec:
machineType: n1-standard-4
acceleratorType: NVIDIA_TESLA_T4
acceleratorCount: 1
persistentDiskSpec:
diskSizeGb: '100'
diskType: pd-standard
networkSpec:
enableInternetAccess: true
executionTimeout: 86400s
kernelName: python3
labels:
test: value
variables:
project:
fn::invoke:
function: gcp:organizations:getProject
arguments: {}
pulumi {
required_providers {
gcp = {
source = "pulumi/gcp"
}
}
}
data "gcp_organizations_getproject" "project" {
}
resource "gcp_storage_bucket" "bucket" {
name = "notebook-job"
location = "us-central1"
uniform_bucket_level_access = true
force_destroy = true
}
resource "gcp_storage_bucketobject" "notebook" {
name = "notebook.ipynb"
content = "{ \"cells\": [], \"metadata\": {}, \"nbformat\": 4, \"nbminor\": 0 }"
bucket = gcp_storage_bucket.bucket.name
}
resource "gcp_vertex_aischedule" "schedule" {
display_name = "test-schedule-notebook"
location = "us-central1"
max_concurrent_run_count = 2
cron = "*/5 * * * *"
start_time = "2030-01-01T00:00:00Z"
create_notebook_execution_job_request = {
parent ="projects/${data.gcp_organizations_getproject.project.project_id}/locations/us-central1"
notebook_execution_job = {
display_name = "test-notebook-execution-job"
gcs_output_uri ="gs://${gcp_storage_bucket.bucket.name}"
service_account ="${data.gcp_organizations_getproject.project.number}-compute@developer.gserviceaccount.com"
gcs_notebook_source = {
uri ="gs://${gcp_storage_bucket.bucket.name}/${gcp_storage_bucketobject.notebook.name}"
generation = gcp_storage_bucketobject.notebook.generation
}
custom_environment_spec = {
machine_spec = {
machine_type = "n1-standard-4"
accelerator_type = "NVIDIA_TESLA_T4"
accelerator_count = 1
}
persistent_disk_spec = {
disk_size_gb = "100"
disk_type = "pd-standard"
}
network_spec = {
enable_internet_access = true
}
}
execution_timeout = "86400s"
kernel_name = "python3"
labels = {
"test" = "value"
}
}
}
}
Create AiSchedule Resource
Resources are created with functions called constructors. To learn more about declaring and configuring resources, see Resources.
Constructor syntax
new AiSchedule(name: string, args: AiScheduleArgs, opts?: CustomResourceOptions);@overload
def AiSchedule(resource_name: str,
args: AiScheduleArgs,
opts: Optional[ResourceOptions] = None)
@overload
def AiSchedule(resource_name: str,
opts: Optional[ResourceOptions] = None,
display_name: Optional[str] = None,
max_concurrent_run_count: Optional[str] = None,
location: Optional[str] = None,
cron: Optional[str] = None,
deletion_policy: Optional[str] = None,
allow_queueing: Optional[bool] = None,
end_time: Optional[str] = None,
create_pipeline_job_request: Optional[AiScheduleCreatePipelineJobRequestArgs] = None,
max_concurrent_active_run_count: Optional[str] = None,
create_notebook_execution_job_request: Optional[AiScheduleCreateNotebookExecutionJobRequestArgs] = None,
max_run_count: Optional[str] = None,
project: Optional[str] = None,
start_time: Optional[str] = None)func NewAiSchedule(ctx *Context, name string, args AiScheduleArgs, opts ...ResourceOption) (*AiSchedule, error)public AiSchedule(string name, AiScheduleArgs args, CustomResourceOptions? opts = null)
public AiSchedule(String name, AiScheduleArgs args)
public AiSchedule(String name, AiScheduleArgs args, CustomResourceOptions options)
type: gcp:vertex:AiSchedule
properties: # The arguments to resource properties.
options: # Bag of options to control resource's behavior.
resource "gcp_vertex_aischedule" "name" {
# resource properties
}Parameters
- name string
- The unique name of the resource.
- args AiScheduleArgs
- The arguments to resource properties.
- opts CustomResourceOptions
- Bag of options to control resource's behavior.
- resource_name str
- The unique name of the resource.
- args AiScheduleArgs
- The arguments to resource properties.
- opts ResourceOptions
- Bag of options to control resource's behavior.
- ctx Context
- Context object for the current deployment.
- name string
- The unique name of the resource.
- args AiScheduleArgs
- The arguments to resource properties.
- opts ResourceOption
- Bag of options to control resource's behavior.
- name string
- The unique name of the resource.
- args AiScheduleArgs
- The arguments to resource properties.
- opts CustomResourceOptions
- Bag of options to control resource's behavior.
- name String
- The unique name of the resource.
- args AiScheduleArgs
- The arguments to resource properties.
- options CustomResourceOptions
- Bag of options to control resource's behavior.
Constructor example
The following reference example uses placeholder values for all input properties.
var aiScheduleResource = new Gcp.Vertex.AiSchedule("aiScheduleResource", new()
{
DisplayName = "string",
MaxConcurrentRunCount = "string",
Location = "string",
Cron = "string",
DeletionPolicy = "string",
AllowQueueing = false,
EndTime = "string",
CreatePipelineJobRequest = new Gcp.Vertex.Inputs.AiScheduleCreatePipelineJobRequestArgs
{
Parent = "string",
PipelineJob = new Gcp.Vertex.Inputs.AiScheduleCreatePipelineJobRequestPipelineJobArgs
{
CreateTime = "string",
DisplayName = "string",
EncryptionSpec = new Gcp.Vertex.Inputs.AiScheduleCreatePipelineJobRequestPipelineJobEncryptionSpecArgs
{
KmsKeyName = "string",
},
EndTime = "string",
Labels =
{
{ "string", "string" },
},
Name = "string",
Network = "string",
PipelineSpec = "string",
PreflightValidations = false,
PscInterfaceConfig = new Gcp.Vertex.Inputs.AiScheduleCreatePipelineJobRequestPipelineJobPscInterfaceConfigArgs
{
DnsPeeringConfigs = new[]
{
new Gcp.Vertex.Inputs.AiScheduleCreatePipelineJobRequestPipelineJobPscInterfaceConfigDnsPeeringConfigArgs
{
Domain = "string",
TargetNetwork = "string",
TargetProject = "string",
},
},
NetworkAttachment = "string",
},
ReservedIpRanges = new[]
{
"string",
},
RuntimeConfig = new Gcp.Vertex.Inputs.AiScheduleCreatePipelineJobRequestPipelineJobRuntimeConfigArgs
{
GcsOutputDirectory = "string",
FailurePolicy = "string",
InputArtifacts =
{
{ "string", "string" },
},
ParameterValues =
{
{ "string", "string" },
},
},
ScheduleName = "string",
ServiceAccount = "string",
StartTime = "string",
State = "string",
TemplateMetadatas = new[]
{
new Gcp.Vertex.Inputs.AiScheduleCreatePipelineJobRequestPipelineJobTemplateMetadataArgs
{
Version = "string",
},
},
TemplateUri = "string",
UpdateTime = "string",
},
PipelineJobId = "string",
},
MaxConcurrentActiveRunCount = "string",
CreateNotebookExecutionJobRequest = new Gcp.Vertex.Inputs.AiScheduleCreateNotebookExecutionJobRequestArgs
{
NotebookExecutionJob = new Gcp.Vertex.Inputs.AiScheduleCreateNotebookExecutionJobRequestNotebookExecutionJobArgs
{
CreateTime = "string",
CustomEnvironmentSpec = new Gcp.Vertex.Inputs.AiScheduleCreateNotebookExecutionJobRequestNotebookExecutionJobCustomEnvironmentSpecArgs
{
MachineSpec = new Gcp.Vertex.Inputs.AiScheduleCreateNotebookExecutionJobRequestNotebookExecutionJobCustomEnvironmentSpecMachineSpecArgs
{
AcceleratorCount = 0,
AcceleratorType = "string",
GpuPartitionSize = "string",
MachineType = "string",
ReservationAffinity = new Gcp.Vertex.Inputs.AiScheduleCreateNotebookExecutionJobRequestNotebookExecutionJobCustomEnvironmentSpecMachineSpecReservationAffinityArgs
{
ReservationAffinityType = "string",
Key = "string",
UseReservationPool = false,
Values = new[]
{
"string",
},
},
TpuTopology = "string",
},
NetworkSpec = new Gcp.Vertex.Inputs.AiScheduleCreateNotebookExecutionJobRequestNotebookExecutionJobCustomEnvironmentSpecNetworkSpecArgs
{
EnableInternetAccess = false,
Network = "string",
Subnetwork = "string",
},
PersistentDiskSpec = new Gcp.Vertex.Inputs.AiScheduleCreateNotebookExecutionJobRequestNotebookExecutionJobCustomEnvironmentSpecPersistentDiskSpecArgs
{
DiskSizeGb = "string",
DiskType = "string",
},
},
DataformRepositorySource = new Gcp.Vertex.Inputs.AiScheduleCreateNotebookExecutionJobRequestNotebookExecutionJobDataformRepositorySourceArgs
{
CommitSha = "string",
DataformRepositoryResourceName = "string",
},
DirectNotebookSource = new Gcp.Vertex.Inputs.AiScheduleCreateNotebookExecutionJobRequestNotebookExecutionJobDirectNotebookSourceArgs
{
Content = "string",
},
DisplayName = "string",
EncryptionSpec = new Gcp.Vertex.Inputs.AiScheduleCreateNotebookExecutionJobRequestNotebookExecutionJobEncryptionSpecArgs
{
KmsKeyName = "string",
},
ExecutionTimeout = "string",
ExecutionUser = "string",
GcsNotebookSource = new Gcp.Vertex.Inputs.AiScheduleCreateNotebookExecutionJobRequestNotebookExecutionJobGcsNotebookSourceArgs
{
Generation = "string",
Uri = "string",
},
GcsOutputUri = "string",
JobState = "string",
KernelName = "string",
Labels =
{
{ "string", "string" },
},
Name = "string",
NotebookRuntimeTemplateResourceName = "string",
Parameters =
{
{ "string", "string" },
},
ScheduleResourceName = "string",
ServiceAccount = "string",
UpdateTime = "string",
WorkbenchRuntime = null,
},
Parent = "string",
NotebookExecutionJobId = "string",
},
MaxRunCount = "string",
Project = "string",
StartTime = "string",
});
example, err := vertex.NewAiSchedule(ctx, "aiScheduleResource", &vertex.AiScheduleArgs{
DisplayName: pulumi.String("string"),
MaxConcurrentRunCount: pulumi.String("string"),
Location: pulumi.String("string"),
Cron: pulumi.String("string"),
DeletionPolicy: pulumi.String("string"),
AllowQueueing: pulumi.Bool(false),
EndTime: pulumi.String("string"),
CreatePipelineJobRequest: &vertex.AiScheduleCreatePipelineJobRequestArgs{
Parent: pulumi.String("string"),
PipelineJob: &vertex.AiScheduleCreatePipelineJobRequestPipelineJobArgs{
CreateTime: pulumi.String("string"),
DisplayName: pulumi.String("string"),
EncryptionSpec: &vertex.AiScheduleCreatePipelineJobRequestPipelineJobEncryptionSpecArgs{
KmsKeyName: pulumi.String("string"),
},
EndTime: pulumi.String("string"),
Labels: pulumi.StringMap{
"string": pulumi.String("string"),
},
Name: pulumi.String("string"),
Network: pulumi.String("string"),
PipelineSpec: pulumi.String("string"),
PreflightValidations: pulumi.Bool(false),
PscInterfaceConfig: &vertex.AiScheduleCreatePipelineJobRequestPipelineJobPscInterfaceConfigArgs{
DnsPeeringConfigs: vertex.AiScheduleCreatePipelineJobRequestPipelineJobPscInterfaceConfigDnsPeeringConfigArray{
&vertex.AiScheduleCreatePipelineJobRequestPipelineJobPscInterfaceConfigDnsPeeringConfigArgs{
Domain: pulumi.String("string"),
TargetNetwork: pulumi.String("string"),
TargetProject: pulumi.String("string"),
},
},
NetworkAttachment: pulumi.String("string"),
},
ReservedIpRanges: pulumi.StringArray{
pulumi.String("string"),
},
RuntimeConfig: &vertex.AiScheduleCreatePipelineJobRequestPipelineJobRuntimeConfigArgs{
GcsOutputDirectory: pulumi.String("string"),
FailurePolicy: pulumi.String("string"),
InputArtifacts: pulumi.StringMap{
"string": pulumi.String("string"),
},
ParameterValues: pulumi.StringMap{
"string": pulumi.String("string"),
},
},
ScheduleName: pulumi.String("string"),
ServiceAccount: pulumi.String("string"),
StartTime: pulumi.String("string"),
State: pulumi.String("string"),
TemplateMetadatas: vertex.AiScheduleCreatePipelineJobRequestPipelineJobTemplateMetadataArray{
&vertex.AiScheduleCreatePipelineJobRequestPipelineJobTemplateMetadataArgs{
Version: pulumi.String("string"),
},
},
TemplateUri: pulumi.String("string"),
UpdateTime: pulumi.String("string"),
},
PipelineJobId: pulumi.String("string"),
},
MaxConcurrentActiveRunCount: pulumi.String("string"),
CreateNotebookExecutionJobRequest: &vertex.AiScheduleCreateNotebookExecutionJobRequestArgs{
NotebookExecutionJob: &vertex.AiScheduleCreateNotebookExecutionJobRequestNotebookExecutionJobArgs{
CreateTime: pulumi.String("string"),
CustomEnvironmentSpec: &vertex.AiScheduleCreateNotebookExecutionJobRequestNotebookExecutionJobCustomEnvironmentSpecArgs{
MachineSpec: &vertex.AiScheduleCreateNotebookExecutionJobRequestNotebookExecutionJobCustomEnvironmentSpecMachineSpecArgs{
AcceleratorCount: pulumi.Int(0),
AcceleratorType: pulumi.String("string"),
GpuPartitionSize: pulumi.String("string"),
MachineType: pulumi.String("string"),
ReservationAffinity: &vertex.AiScheduleCreateNotebookExecutionJobRequestNotebookExecutionJobCustomEnvironmentSpecMachineSpecReservationAffinityArgs{
ReservationAffinityType: pulumi.String("string"),
Key: pulumi.String("string"),
UseReservationPool: pulumi.Bool(false),
Values: pulumi.StringArray{
pulumi.String("string"),
},
},
TpuTopology: pulumi.String("string"),
},
NetworkSpec: &vertex.AiScheduleCreateNotebookExecutionJobRequestNotebookExecutionJobCustomEnvironmentSpecNetworkSpecArgs{
EnableInternetAccess: pulumi.Bool(false),
Network: pulumi.String("string"),
Subnetwork: pulumi.String("string"),
},
PersistentDiskSpec: &vertex.AiScheduleCreateNotebookExecutionJobRequestNotebookExecutionJobCustomEnvironmentSpecPersistentDiskSpecArgs{
DiskSizeGb: pulumi.String("string"),
DiskType: pulumi.String("string"),
},
},
DataformRepositorySource: &vertex.AiScheduleCreateNotebookExecutionJobRequestNotebookExecutionJobDataformRepositorySourceArgs{
CommitSha: pulumi.String("string"),
DataformRepositoryResourceName: pulumi.String("string"),
},
DirectNotebookSource: &vertex.AiScheduleCreateNotebookExecutionJobRequestNotebookExecutionJobDirectNotebookSourceArgs{
Content: pulumi.String("string"),
},
DisplayName: pulumi.String("string"),
EncryptionSpec: &vertex.AiScheduleCreateNotebookExecutionJobRequestNotebookExecutionJobEncryptionSpecArgs{
KmsKeyName: pulumi.String("string"),
},
ExecutionTimeout: pulumi.String("string"),
ExecutionUser: pulumi.String("string"),
GcsNotebookSource: &vertex.AiScheduleCreateNotebookExecutionJobRequestNotebookExecutionJobGcsNotebookSourceArgs{
Generation: pulumi.String("string"),
Uri: pulumi.String("string"),
},
GcsOutputUri: pulumi.String("string"),
JobState: pulumi.String("string"),
KernelName: pulumi.String("string"),
Labels: pulumi.StringMap{
"string": pulumi.String("string"),
},
Name: pulumi.String("string"),
NotebookRuntimeTemplateResourceName: pulumi.String("string"),
Parameters: pulumi.StringMap{
"string": pulumi.String("string"),
},
ScheduleResourceName: pulumi.String("string"),
ServiceAccount: pulumi.String("string"),
UpdateTime: pulumi.String("string"),
WorkbenchRuntime: &vertex.AiScheduleCreateNotebookExecutionJobRequestNotebookExecutionJobWorkbenchRuntimeArgs{},
},
Parent: pulumi.String("string"),
NotebookExecutionJobId: pulumi.String("string"),
},
MaxRunCount: pulumi.String("string"),
Project: pulumi.String("string"),
StartTime: pulumi.String("string"),
})
resource "gcp_vertex_aischedule" "aiScheduleResource" {
display_name = "string"
max_concurrent_run_count = "string"
location = "string"
cron = "string"
deletion_policy = "string"
allow_queueing = false
end_time = "string"
create_pipeline_job_request = {
parent = "string"
pipeline_job = {
create_time = "string"
display_name = "string"
encryption_spec = {
kms_key_name = "string"
}
end_time = "string"
labels = {
"string" = "string"
}
name = "string"
network = "string"
pipeline_spec = "string"
preflight_validations = false
psc_interface_config = {
dns_peering_configs = [{
"domain" = "string"
"targetNetwork" = "string"
"targetProject" = "string"
}]
network_attachment = "string"
}
reserved_ip_ranges = ["string"]
runtime_config = {
gcs_output_directory = "string"
failure_policy = "string"
input_artifacts = {
"string" = "string"
}
parameter_values = {
"string" = "string"
}
}
schedule_name = "string"
service_account = "string"
start_time = "string"
state = "string"
template_metadatas = [{
"version" = "string"
}]
template_uri = "string"
update_time = "string"
}
pipeline_job_id = "string"
}
max_concurrent_active_run_count = "string"
create_notebook_execution_job_request = {
notebook_execution_job = {
create_time = "string"
custom_environment_spec = {
machine_spec = {
accelerator_count = 0
accelerator_type = "string"
gpu_partition_size = "string"
machine_type = "string"
reservation_affinity = {
reservation_affinity_type = "string"
key = "string"
use_reservation_pool = false
values = ["string"]
}
tpu_topology = "string"
}
network_spec = {
enable_internet_access = false
network = "string"
subnetwork = "string"
}
persistent_disk_spec = {
disk_size_gb = "string"
disk_type = "string"
}
}
dataform_repository_source = {
commit_sha = "string"
dataform_repository_resource_name = "string"
}
direct_notebook_source = {
content = "string"
}
display_name = "string"
encryption_spec = {
kms_key_name = "string"
}
execution_timeout = "string"
execution_user = "string"
gcs_notebook_source = {
generation = "string"
uri = "string"
}
gcs_output_uri = "string"
job_state = "string"
kernel_name = "string"
labels = {
"string" = "string"
}
name = "string"
notebook_runtime_template_resource_name = "string"
parameters = {
"string" = "string"
}
schedule_resource_name = "string"
service_account = "string"
update_time = "string"
workbench_runtime = {}
}
parent = "string"
notebook_execution_job_id = "string"
}
max_run_count = "string"
project = "string"
start_time = "string"
}
var aiScheduleResource = new AiSchedule("aiScheduleResource", AiScheduleArgs.builder()
.displayName("string")
.maxConcurrentRunCount("string")
.location("string")
.cron("string")
.deletionPolicy("string")
.allowQueueing(false)
.endTime("string")
.createPipelineJobRequest(AiScheduleCreatePipelineJobRequestArgs.builder()
.parent("string")
.pipelineJob(AiScheduleCreatePipelineJobRequestPipelineJobArgs.builder()
.createTime("string")
.displayName("string")
.encryptionSpec(AiScheduleCreatePipelineJobRequestPipelineJobEncryptionSpecArgs.builder()
.kmsKeyName("string")
.build())
.endTime("string")
.labels(Map.of("string", "string"))
.name("string")
.network("string")
.pipelineSpec("string")
.preflightValidations(false)
.pscInterfaceConfig(AiScheduleCreatePipelineJobRequestPipelineJobPscInterfaceConfigArgs.builder()
.dnsPeeringConfigs(AiScheduleCreatePipelineJobRequestPipelineJobPscInterfaceConfigDnsPeeringConfigArgs.builder()
.domain("string")
.targetNetwork("string")
.targetProject("string")
.build())
.networkAttachment("string")
.build())
.reservedIpRanges("string")
.runtimeConfig(AiScheduleCreatePipelineJobRequestPipelineJobRuntimeConfigArgs.builder()
.gcsOutputDirectory("string")
.failurePolicy("string")
.inputArtifacts(Map.of("string", "string"))
.parameterValues(Map.of("string", "string"))
.build())
.scheduleName("string")
.serviceAccount("string")
.startTime("string")
.state("string")
.templateMetadatas(AiScheduleCreatePipelineJobRequestPipelineJobTemplateMetadataArgs.builder()
.version("string")
.build())
.templateUri("string")
.updateTime("string")
.build())
.pipelineJobId("string")
.build())
.maxConcurrentActiveRunCount("string")
.createNotebookExecutionJobRequest(AiScheduleCreateNotebookExecutionJobRequestArgs.builder()
.notebookExecutionJob(AiScheduleCreateNotebookExecutionJobRequestNotebookExecutionJobArgs.builder()
.createTime("string")
.customEnvironmentSpec(AiScheduleCreateNotebookExecutionJobRequestNotebookExecutionJobCustomEnvironmentSpecArgs.builder()
.machineSpec(AiScheduleCreateNotebookExecutionJobRequestNotebookExecutionJobCustomEnvironmentSpecMachineSpecArgs.builder()
.acceleratorCount(0)
.acceleratorType("string")
.gpuPartitionSize("string")
.machineType("string")
.reservationAffinity(AiScheduleCreateNotebookExecutionJobRequestNotebookExecutionJobCustomEnvironmentSpecMachineSpecReservationAffinityArgs.builder()
.reservationAffinityType("string")
.key("string")
.useReservationPool(false)
.values("string")
.build())
.tpuTopology("string")
.build())
.networkSpec(AiScheduleCreateNotebookExecutionJobRequestNotebookExecutionJobCustomEnvironmentSpecNetworkSpecArgs.builder()
.enableInternetAccess(false)
.network("string")
.subnetwork("string")
.build())
.persistentDiskSpec(AiScheduleCreateNotebookExecutionJobRequestNotebookExecutionJobCustomEnvironmentSpecPersistentDiskSpecArgs.builder()
.diskSizeGb("string")
.diskType("string")
.build())
.build())
.dataformRepositorySource(AiScheduleCreateNotebookExecutionJobRequestNotebookExecutionJobDataformRepositorySourceArgs.builder()
.commitSha("string")
.dataformRepositoryResourceName("string")
.build())
.directNotebookSource(AiScheduleCreateNotebookExecutionJobRequestNotebookExecutionJobDirectNotebookSourceArgs.builder()
.content("string")
.build())
.displayName("string")
.encryptionSpec(AiScheduleCreateNotebookExecutionJobRequestNotebookExecutionJobEncryptionSpecArgs.builder()
.kmsKeyName("string")
.build())
.executionTimeout("string")
.executionUser("string")
.gcsNotebookSource(AiScheduleCreateNotebookExecutionJobRequestNotebookExecutionJobGcsNotebookSourceArgs.builder()
.generation("string")
.uri("string")
.build())
.gcsOutputUri("string")
.jobState("string")
.kernelName("string")
.labels(Map.of("string", "string"))
.name("string")
.notebookRuntimeTemplateResourceName("string")
.parameters(Map.of("string", "string"))
.scheduleResourceName("string")
.serviceAccount("string")
.updateTime("string")
.workbenchRuntime(AiScheduleCreateNotebookExecutionJobRequestNotebookExecutionJobWorkbenchRuntimeArgs.builder()
.build())
.build())
.parent("string")
.notebookExecutionJobId("string")
.build())
.maxRunCount("string")
.project("string")
.startTime("string")
.build());
ai_schedule_resource = gcp.vertex.AiSchedule("aiScheduleResource",
display_name="string",
max_concurrent_run_count="string",
location="string",
cron="string",
deletion_policy="string",
allow_queueing=False,
end_time="string",
create_pipeline_job_request={
"parent": "string",
"pipeline_job": {
"create_time": "string",
"display_name": "string",
"encryption_spec": {
"kms_key_name": "string",
},
"end_time": "string",
"labels": {
"string": "string",
},
"name": "string",
"network": "string",
"pipeline_spec": "string",
"preflight_validations": False,
"psc_interface_config": {
"dns_peering_configs": [{
"domain": "string",
"target_network": "string",
"target_project": "string",
}],
"network_attachment": "string",
},
"reserved_ip_ranges": ["string"],
"runtime_config": {
"gcs_output_directory": "string",
"failure_policy": "string",
"input_artifacts": {
"string": "string",
},
"parameter_values": {
"string": "string",
},
},
"schedule_name": "string",
"service_account": "string",
"start_time": "string",
"state": "string",
"template_metadatas": [{
"version": "string",
}],
"template_uri": "string",
"update_time": "string",
},
"pipeline_job_id": "string",
},
max_concurrent_active_run_count="string",
create_notebook_execution_job_request={
"notebook_execution_job": {
"create_time": "string",
"custom_environment_spec": {
"machine_spec": {
"accelerator_count": 0,
"accelerator_type": "string",
"gpu_partition_size": "string",
"machine_type": "string",
"reservation_affinity": {
"reservation_affinity_type": "string",
"key": "string",
"use_reservation_pool": False,
"values": ["string"],
},
"tpu_topology": "string",
},
"network_spec": {
"enable_internet_access": False,
"network": "string",
"subnetwork": "string",
},
"persistent_disk_spec": {
"disk_size_gb": "string",
"disk_type": "string",
},
},
"dataform_repository_source": {
"commit_sha": "string",
"dataform_repository_resource_name": "string",
},
"direct_notebook_source": {
"content": "string",
},
"display_name": "string",
"encryption_spec": {
"kms_key_name": "string",
},
"execution_timeout": "string",
"execution_user": "string",
"gcs_notebook_source": {
"generation": "string",
"uri": "string",
},
"gcs_output_uri": "string",
"job_state": "string",
"kernel_name": "string",
"labels": {
"string": "string",
},
"name": "string",
"notebook_runtime_template_resource_name": "string",
"parameters": {
"string": "string",
},
"schedule_resource_name": "string",
"service_account": "string",
"update_time": "string",
"workbench_runtime": {},
},
"parent": "string",
"notebook_execution_job_id": "string",
},
max_run_count="string",
project="string",
start_time="string")
const aiScheduleResource = new gcp.vertex.AiSchedule("aiScheduleResource", {
displayName: "string",
maxConcurrentRunCount: "string",
location: "string",
cron: "string",
deletionPolicy: "string",
allowQueueing: false,
endTime: "string",
createPipelineJobRequest: {
parent: "string",
pipelineJob: {
createTime: "string",
displayName: "string",
encryptionSpec: {
kmsKeyName: "string",
},
endTime: "string",
labels: {
string: "string",
},
name: "string",
network: "string",
pipelineSpec: "string",
preflightValidations: false,
pscInterfaceConfig: {
dnsPeeringConfigs: [{
domain: "string",
targetNetwork: "string",
targetProject: "string",
}],
networkAttachment: "string",
},
reservedIpRanges: ["string"],
runtimeConfig: {
gcsOutputDirectory: "string",
failurePolicy: "string",
inputArtifacts: {
string: "string",
},
parameterValues: {
string: "string",
},
},
scheduleName: "string",
serviceAccount: "string",
startTime: "string",
state: "string",
templateMetadatas: [{
version: "string",
}],
templateUri: "string",
updateTime: "string",
},
pipelineJobId: "string",
},
maxConcurrentActiveRunCount: "string",
createNotebookExecutionJobRequest: {
notebookExecutionJob: {
createTime: "string",
customEnvironmentSpec: {
machineSpec: {
acceleratorCount: 0,
acceleratorType: "string",
gpuPartitionSize: "string",
machineType: "string",
reservationAffinity: {
reservationAffinityType: "string",
key: "string",
useReservationPool: false,
values: ["string"],
},
tpuTopology: "string",
},
networkSpec: {
enableInternetAccess: false,
network: "string",
subnetwork: "string",
},
persistentDiskSpec: {
diskSizeGb: "string",
diskType: "string",
},
},
dataformRepositorySource: {
commitSha: "string",
dataformRepositoryResourceName: "string",
},
directNotebookSource: {
content: "string",
},
displayName: "string",
encryptionSpec: {
kmsKeyName: "string",
},
executionTimeout: "string",
executionUser: "string",
gcsNotebookSource: {
generation: "string",
uri: "string",
},
gcsOutputUri: "string",
jobState: "string",
kernelName: "string",
labels: {
string: "string",
},
name: "string",
notebookRuntimeTemplateResourceName: "string",
parameters: {
string: "string",
},
scheduleResourceName: "string",
serviceAccount: "string",
updateTime: "string",
workbenchRuntime: {},
},
parent: "string",
notebookExecutionJobId: "string",
},
maxRunCount: "string",
project: "string",
startTime: "string",
});
type: gcp:vertex:AiSchedule
properties:
allowQueueing: false
createNotebookExecutionJobRequest:
notebookExecutionJob:
createTime: string
customEnvironmentSpec:
machineSpec:
acceleratorCount: 0
acceleratorType: string
gpuPartitionSize: string
machineType: string
reservationAffinity:
key: string
reservationAffinityType: string
useReservationPool: false
values:
- string
tpuTopology: string
networkSpec:
enableInternetAccess: false
network: string
subnetwork: string
persistentDiskSpec:
diskSizeGb: string
diskType: string
dataformRepositorySource:
commitSha: string
dataformRepositoryResourceName: string
directNotebookSource:
content: string
displayName: string
encryptionSpec:
kmsKeyName: string
executionTimeout: string
executionUser: string
gcsNotebookSource:
generation: string
uri: string
gcsOutputUri: string
jobState: string
kernelName: string
labels:
string: string
name: string
notebookRuntimeTemplateResourceName: string
parameters:
string: string
scheduleResourceName: string
serviceAccount: string
updateTime: string
workbenchRuntime: {}
notebookExecutionJobId: string
parent: string
createPipelineJobRequest:
parent: string
pipelineJob:
createTime: string
displayName: string
encryptionSpec:
kmsKeyName: string
endTime: string
labels:
string: string
name: string
network: string
pipelineSpec: string
preflightValidations: false
pscInterfaceConfig:
dnsPeeringConfigs:
- domain: string
targetNetwork: string
targetProject: string
networkAttachment: string
reservedIpRanges:
- string
runtimeConfig:
failurePolicy: string
gcsOutputDirectory: string
inputArtifacts:
string: string
parameterValues:
string: string
scheduleName: string
serviceAccount: string
startTime: string
state: string
templateMetadatas:
- version: string
templateUri: string
updateTime: string
pipelineJobId: string
cron: string
deletionPolicy: string
displayName: string
endTime: string
location: string
maxConcurrentActiveRunCount: string
maxConcurrentRunCount: string
maxRunCount: string
project: string
startTime: string
AiSchedule Resource Properties
To learn more about resource properties and how to use them, see Inputs and Outputs in the Architecture and Concepts docs.
Inputs
In Python, inputs that are objects can be passed either as argument classes or as dictionary literals.
The AiSchedule resource accepts the following input properties:
- Display
Name string - User provided name of the Schedule. The name can be up to 128 characters long and can consist of any UTF-8 characters.
- Location string
- The location of the Schedule. eg us-central1
- Max
Concurrent stringRun Count - 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).
- Allow
Queueing bool - Whether new scheduled runs can be queued when maxConcurrentRuns limit is reached. If set to true, new runs will be queued instead of skipped. Default to false.
- Create
Notebook AiExecution Job Request Schedule Create Notebook Execution Job Request - Request message for [NotebookService.CreateNotebookExecutionJob] Structure is documented below.
- Create
Pipeline AiJob Request Schedule Create Pipeline Job Request - Request message for PipelineService.CreatePipelineJob. Structure is documented below.
- 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 * * * *".
- Deletion
Policy string - Whether Terraform will be prevented from destroying the resource. Defaults to DELETE. When a 'terraform destroy' or 'pulumi up' would delete the resource, the command will fail if this field is set to "PREVENT" in Terraform state. When set to "ABANDON", the command will remove the resource from Terraform management without updating or deleting the resource in the API. When set to "DELETE", deleting the resource is allowed.
- End
Time string - Timestamp after which no new runs can be scheduled. If specified, The schedule will be completed when either endTime is reached or when scheduledRunCount >= 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.
- Max
Concurrent stringActive Run Count - 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.
- Max
Run stringCount - Maximum run count of the schedule. If specified, The schedule will be completed when either startedRunCount >= maxRunCount or when endTime 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.
- Project string
- The ID of the project in which the resource belongs. If it is not provided, the provider project is used.
- Start
Time string - Timestamp after which the first run can be scheduled. Default to Schedule create time if not specified.
- Display
Name string - User provided name of the Schedule. The name can be up to 128 characters long and can consist of any UTF-8 characters.
- Location string
- The location of the Schedule. eg us-central1
- Max
Concurrent stringRun Count - 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).
- Allow
Queueing bool - Whether new scheduled runs can be queued when maxConcurrentRuns limit is reached. If set to true, new runs will be queued instead of skipped. Default to false.
- Create
Notebook AiExecution Job Request Schedule Create Notebook Execution Job Request Args - Request message for [NotebookService.CreateNotebookExecutionJob] Structure is documented below.
- Create
Pipeline AiJob Request Schedule Create Pipeline Job Request Args - Request message for PipelineService.CreatePipelineJob. Structure is documented below.
- 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 * * * *".
- Deletion
Policy string - Whether Terraform will be prevented from destroying the resource. Defaults to DELETE. When a 'terraform destroy' or 'pulumi up' would delete the resource, the command will fail if this field is set to "PREVENT" in Terraform state. When set to "ABANDON", the command will remove the resource from Terraform management without updating or deleting the resource in the API. When set to "DELETE", deleting the resource is allowed.
- End
Time string - Timestamp after which no new runs can be scheduled. If specified, The schedule will be completed when either endTime is reached or when scheduledRunCount >= 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.
- Max
Concurrent stringActive Run Count - 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.
- Max
Run stringCount - Maximum run count of the schedule. If specified, The schedule will be completed when either startedRunCount >= maxRunCount or when endTime 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.
- Project string
- The ID of the project in which the resource belongs. If it is not provided, the provider project is used.
- Start
Time string - Timestamp after which the first run can be scheduled. Default to Schedule create time if not specified.
- display_
name string - User provided name of the Schedule. The name can be up to 128 characters long and can consist of any UTF-8 characters.
- location string
- The location of the Schedule. eg us-central1
- max_
concurrent_ stringrun_ count - 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).
- allow_
queueing bool - Whether new scheduled runs can be queued when maxConcurrentRuns limit is reached. If set to true, new runs will be queued instead of skipped. Default to false.
- create_
notebook_ objectexecution_ job_ request - Request message for [NotebookService.CreateNotebookExecutionJob] Structure is documented below.
- create_
pipeline_ objectjob_ request - Request message for PipelineService.CreatePipelineJob. Structure is documented below.
- 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 * * * *".
- deletion_
policy string - Whether Terraform will be prevented from destroying the resource. Defaults to DELETE. When a 'terraform destroy' or 'pulumi up' would delete the resource, the command will fail if this field is set to "PREVENT" in Terraform state. When set to "ABANDON", the command will remove the resource from Terraform management without updating or deleting the resource in the API. When set to "DELETE", deleting the resource is allowed.
- end_
time string - Timestamp after which no new runs can be scheduled. If specified, The schedule will be completed when either endTime is reached or when scheduledRunCount >= 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.
- max_
concurrent_ stringactive_ run_ count - 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.
- max_
run_ stringcount - Maximum run count of the schedule. If specified, The schedule will be completed when either startedRunCount >= maxRunCount or when endTime 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.
- project string
- The ID of the project in which the resource belongs. If it is not provided, the provider project is used.
- start_
time string - Timestamp after which the first run can be scheduled. Default to Schedule create time if not specified.
- display
Name String - User provided name of the Schedule. The name can be up to 128 characters long and can consist of any UTF-8 characters.
- location String
- The location of the Schedule. eg us-central1
- max
Concurrent StringRun Count - 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).
- allow
Queueing Boolean - Whether new scheduled runs can be queued when maxConcurrentRuns limit is reached. If set to true, new runs will be queued instead of skipped. Default to false.
- create
Notebook AiExecution Job Request Schedule Create Notebook Execution Job Request - Request message for [NotebookService.CreateNotebookExecutionJob] Structure is documented below.
- create
Pipeline AiJob Request Schedule Create Pipeline Job Request - Request message for PipelineService.CreatePipelineJob. Structure is documented below.
- 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 * * * *".
- deletion
Policy String - Whether Terraform will be prevented from destroying the resource. Defaults to DELETE. When a 'terraform destroy' or 'pulumi up' would delete the resource, the command will fail if this field is set to "PREVENT" in Terraform state. When set to "ABANDON", the command will remove the resource from Terraform management without updating or deleting the resource in the API. When set to "DELETE", deleting the resource is allowed.
- end
Time String - Timestamp after which no new runs can be scheduled. If specified, The schedule will be completed when either endTime is reached or when scheduledRunCount >= 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.
- max
Concurrent StringActive Run Count - 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.
- max
Run StringCount - Maximum run count of the schedule. If specified, The schedule will be completed when either startedRunCount >= maxRunCount or when endTime 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.
- project String
- The ID of the project in which the resource belongs. If it is not provided, the provider project is used.
- start
Time String - Timestamp after which the first run can be scheduled. Default to Schedule create time if not specified.
- display
Name string - User provided name of the Schedule. The name can be up to 128 characters long and can consist of any UTF-8 characters.
- location string
- The location of the Schedule. eg us-central1
- max
Concurrent stringRun Count - 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).
- allow
Queueing boolean - Whether new scheduled runs can be queued when maxConcurrentRuns limit is reached. If set to true, new runs will be queued instead of skipped. Default to false.
- create
Notebook AiExecution Job Request Schedule Create Notebook Execution Job Request - Request message for [NotebookService.CreateNotebookExecutionJob] Structure is documented below.
- create
Pipeline AiJob Request Schedule Create Pipeline Job Request - Request message for PipelineService.CreatePipelineJob. Structure is documented below.
- 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 * * * *".
- deletion
Policy string - Whether Terraform will be prevented from destroying the resource. Defaults to DELETE. When a 'terraform destroy' or 'pulumi up' would delete the resource, the command will fail if this field is set to "PREVENT" in Terraform state. When set to "ABANDON", the command will remove the resource from Terraform management without updating or deleting the resource in the API. When set to "DELETE", deleting the resource is allowed.
- end
Time string - Timestamp after which no new runs can be scheduled. If specified, The schedule will be completed when either endTime is reached or when scheduledRunCount >= 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.
- max
Concurrent stringActive Run Count - 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.
- max
Run stringCount - Maximum run count of the schedule. If specified, The schedule will be completed when either startedRunCount >= maxRunCount or when endTime 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.
- project string
- The ID of the project in which the resource belongs. If it is not provided, the provider project is used.
- start
Time string - Timestamp after which the first run can be scheduled. Default to Schedule create time if not specified.
- display_
name str - User provided name of the Schedule. The name can be up to 128 characters long and can consist of any UTF-8 characters.
- location str
- The location of the Schedule. eg us-central1
- max_
concurrent_ strrun_ count - 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).
- allow_
queueing bool - Whether new scheduled runs can be queued when maxConcurrentRuns limit is reached. If set to true, new runs will be queued instead of skipped. Default to false.
- create_
notebook_ Aiexecution_ job_ request Schedule Create Notebook Execution Job Request Args - Request message for [NotebookService.CreateNotebookExecutionJob] Structure is documented below.
- create_
pipeline_ Aijob_ request Schedule Create Pipeline Job Request Args - Request message for PipelineService.CreatePipelineJob. Structure is documented below.
- cron str
- 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 * * * *".
- deletion_
policy str - Whether Terraform will be prevented from destroying the resource. Defaults to DELETE. When a 'terraform destroy' or 'pulumi up' would delete the resource, the command will fail if this field is set to "PREVENT" in Terraform state. When set to "ABANDON", the command will remove the resource from Terraform management without updating or deleting the resource in the API. When set to "DELETE", deleting the resource is allowed.
- end_
time str - Timestamp after which no new runs can be scheduled. If specified, The schedule will be completed when either endTime is reached or when scheduledRunCount >= 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.
- max_
concurrent_ stractive_ run_ count - 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.
- max_
run_ strcount - Maximum run count of the schedule. If specified, The schedule will be completed when either startedRunCount >= maxRunCount or when endTime 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.
- project str
- The ID of the project in which the resource belongs. If it is not provided, the provider project is used.
- start_
time str - Timestamp after which the first run can be scheduled. Default to Schedule create time if not specified.
- display
Name String - User provided name of the Schedule. The name can be up to 128 characters long and can consist of any UTF-8 characters.
- location String
- The location of the Schedule. eg us-central1
- max
Concurrent StringRun Count - 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).
- allow
Queueing Boolean - Whether new scheduled runs can be queued when maxConcurrentRuns limit is reached. If set to true, new runs will be queued instead of skipped. Default to false.
- create
Notebook Property MapExecution Job Request - Request message for [NotebookService.CreateNotebookExecutionJob] Structure is documented below.
- create
Pipeline Property MapJob Request - Request message for PipelineService.CreatePipelineJob. Structure is documented below.
- 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 * * * *".
- deletion
Policy String - Whether Terraform will be prevented from destroying the resource. Defaults to DELETE. When a 'terraform destroy' or 'pulumi up' would delete the resource, the command will fail if this field is set to "PREVENT" in Terraform state. When set to "ABANDON", the command will remove the resource from Terraform management without updating or deleting the resource in the API. When set to "DELETE", deleting the resource is allowed.
- end
Time String - Timestamp after which no new runs can be scheduled. If specified, The schedule will be completed when either endTime is reached or when scheduledRunCount >= 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.
- max
Concurrent StringActive Run Count - 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.
- max
Run StringCount - Maximum run count of the schedule. If specified, The schedule will be completed when either startedRunCount >= maxRunCount or when endTime 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.
- project String
- The ID of the project in which the resource belongs. If it is not provided, the provider project is used.
- start
Time String - Timestamp after which the first run can be scheduled. Default to Schedule create time if not specified.
Outputs
All input properties are implicitly available as output properties. Additionally, the AiSchedule resource produces the following output properties:
- Catch
Up bool - 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.
- Create
Time string - Timestamp when this Schedule was created.
- Id string
- The provider-assigned unique ID for this managed resource.
- Last
Pause stringTime - Timestamp when this Schedule was last paused. Unset if never paused.
- Last
Resume stringTime - Timestamp when this Schedule was last resumed. Unset if never resumed from pause.
- Last
Scheduled List<AiRun Responses Schedule Last Scheduled Run Response> - Status of a scheduled run. Structure is documented below.
- Name string
- The resource name of the Schedule.
- Next
Run stringTime - Timestamp when this Schedule should schedule the next run. Having a nextRunTime in the past means the runs are being started behind schedule.
- Started
Run stringCount - The number of runs started by this schedule.
- State string
- The state of this Schedule. Possible values: ACTIVE PAUSED COMPLETED
- Update
Time string - Timestamp when this Schedule was updated.
- Catch
Up bool - 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.
- Create
Time string - Timestamp when this Schedule was created.
- Id string
- The provider-assigned unique ID for this managed resource.
- Last
Pause stringTime - Timestamp when this Schedule was last paused. Unset if never paused.
- Last
Resume stringTime - Timestamp when this Schedule was last resumed. Unset if never resumed from pause.
- Last
Scheduled []AiRun Responses Schedule Last Scheduled Run Response - Status of a scheduled run. Structure is documented below.
- Name string
- The resource name of the Schedule.
- Next
Run stringTime - Timestamp when this Schedule should schedule the next run. Having a nextRunTime in the past means the runs are being started behind schedule.
- Started
Run stringCount - The number of runs started by this schedule.
- State string
- The state of this Schedule. Possible values: ACTIVE PAUSED COMPLETED
- Update
Time string - Timestamp when this Schedule was updated.
- catch_
up bool - 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.
- create_
time string - Timestamp when this Schedule was created.
- id string
- The provider-assigned unique ID for this managed resource.
- last_
pause_ stringtime - Timestamp when this Schedule was last paused. Unset if never paused.
- last_
resume_ stringtime - Timestamp when this Schedule was last resumed. Unset if never resumed from pause.
- last_
scheduled_ list(object)run_ responses - Status of a scheduled run. Structure is documented below.
- name string
- The resource name of the Schedule.
- next_
run_ stringtime - Timestamp when this Schedule should schedule the next run. Having a nextRunTime in the past means the runs are being started behind schedule.
- started_
run_ stringcount - The number of runs started by this schedule.
- state string
- The state of this Schedule. Possible values: ACTIVE PAUSED COMPLETED
- update_
time string - Timestamp when this Schedule was updated.
- catch
Up Boolean - 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.
- create
Time String - Timestamp when this Schedule was created.
- id String
- The provider-assigned unique ID for this managed resource.
- last
Pause StringTime - Timestamp when this Schedule was last paused. Unset if never paused.
- last
Resume StringTime - Timestamp when this Schedule was last resumed. Unset if never resumed from pause.
- last
Scheduled List<AiRun Responses Schedule Last Scheduled Run Response> - Status of a scheduled run. Structure is documented below.
- name String
- The resource name of the Schedule.
- next
Run StringTime - Timestamp when this Schedule should schedule the next run. Having a nextRunTime in the past means the runs are being started behind schedule.
- started
Run StringCount - The number of runs started by this schedule.
- state String
- The state of this Schedule. Possible values: ACTIVE PAUSED COMPLETED
- update
Time String - Timestamp when this Schedule was updated.
- catch
Up boolean - 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.
- create
Time string - Timestamp when this Schedule was created.
- id string
- The provider-assigned unique ID for this managed resource.
- last
Pause stringTime - Timestamp when this Schedule was last paused. Unset if never paused.
- last
Resume stringTime - Timestamp when this Schedule was last resumed. Unset if never resumed from pause.
- last
Scheduled AiRun Responses Schedule Last Scheduled Run Response[] - Status of a scheduled run. Structure is documented below.
- name string
- The resource name of the Schedule.
- next
Run stringTime - Timestamp when this Schedule should schedule the next run. Having a nextRunTime in the past means the runs are being started behind schedule.
- started
Run stringCount - The number of runs started by this schedule.
- state string
- The state of this Schedule. Possible values: ACTIVE PAUSED COMPLETED
- update
Time string - Timestamp when this Schedule was updated.
- catch_
up bool - 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.
- create_
time str - Timestamp when this Schedule was created.
- id str
- The provider-assigned unique ID for this managed resource.
- last_
pause_ strtime - Timestamp when this Schedule was last paused. Unset if never paused.
- last_
resume_ strtime - Timestamp when this Schedule was last resumed. Unset if never resumed from pause.
- last_
scheduled_ Sequence[Airun_ responses Schedule Last Scheduled Run Response] - Status of a scheduled run. Structure is documented below.
- name str
- The resource name of the Schedule.
- next_
run_ strtime - Timestamp when this Schedule should schedule the next run. Having a nextRunTime in the past means the runs are being started behind schedule.
- started_
run_ strcount - The number of runs started by this schedule.
- state str
- The state of this Schedule. Possible values: ACTIVE PAUSED COMPLETED
- update_
time str - Timestamp when this Schedule was updated.
- catch
Up Boolean - 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.
- create
Time String - Timestamp when this Schedule was created.
- id String
- The provider-assigned unique ID for this managed resource.
- last
Pause StringTime - Timestamp when this Schedule was last paused. Unset if never paused.
- last
Resume StringTime - Timestamp when this Schedule was last resumed. Unset if never resumed from pause.
- last
Scheduled List<Property Map>Run Responses - Status of a scheduled run. Structure is documented below.
- name String
- The resource name of the Schedule.
- next
Run StringTime - Timestamp when this Schedule should schedule the next run. Having a nextRunTime in the past means the runs are being started behind schedule.
- started
Run StringCount - The number of runs started by this schedule.
- state String
- The state of this Schedule. Possible values: ACTIVE PAUSED COMPLETED
- update
Time String - Timestamp when this Schedule was updated.
Look up Existing AiSchedule Resource
Get an existing AiSchedule resource’s state with the given name, ID, and optional extra properties used to qualify the lookup.
public static get(name: string, id: Input<ID>, state?: AiScheduleState, opts?: CustomResourceOptions): AiSchedule@staticmethod
def get(resource_name: str,
id: str,
opts: Optional[ResourceOptions] = None,
allow_queueing: Optional[bool] = None,
catch_up: Optional[bool] = None,
create_notebook_execution_job_request: Optional[AiScheduleCreateNotebookExecutionJobRequestArgs] = None,
create_pipeline_job_request: Optional[AiScheduleCreatePipelineJobRequestArgs] = None,
create_time: Optional[str] = None,
cron: Optional[str] = None,
deletion_policy: Optional[str] = None,
display_name: Optional[str] = None,
end_time: Optional[str] = None,
last_pause_time: Optional[str] = None,
last_resume_time: Optional[str] = None,
last_scheduled_run_responses: Optional[Sequence[AiScheduleLastScheduledRunResponseArgs]] = None,
location: Optional[str] = None,
max_concurrent_active_run_count: Optional[str] = None,
max_concurrent_run_count: Optional[str] = None,
max_run_count: Optional[str] = None,
name: Optional[str] = None,
next_run_time: Optional[str] = None,
project: Optional[str] = None,
start_time: Optional[str] = None,
started_run_count: Optional[str] = None,
state: Optional[str] = None,
update_time: Optional[str] = None) -> AiSchedulefunc GetAiSchedule(ctx *Context, name string, id IDInput, state *AiScheduleState, opts ...ResourceOption) (*AiSchedule, error)public static AiSchedule Get(string name, Input<string> id, AiScheduleState? state, CustomResourceOptions? opts = null)public static AiSchedule get(String name, Output<String> id, AiScheduleState state, CustomResourceOptions options)resources: _: type: gcp:vertex:AiSchedule get: id: ${id}import {
to = gcp_vertex_aischedule.example
id = "${id}"
}
- name
- The unique name of the resulting resource.
- id
- The unique provider ID of the resource to lookup.
- state
- Any extra arguments used during the lookup.
- opts
- A bag of options that control this resource's behavior.
- resource_name
- The unique name of the resulting resource.
- id
- The unique provider ID of the resource to lookup.
- name
- The unique name of the resulting resource.
- id
- The unique provider ID of the resource to lookup.
- state
- Any extra arguments used during the lookup.
- opts
- A bag of options that control this resource's behavior.
- name
- The unique name of the resulting resource.
- id
- The unique provider ID of the resource to lookup.
- state
- Any extra arguments used during the lookup.
- opts
- A bag of options that control this resource's behavior.
- name
- The unique name of the resulting resource.
- id
- The unique provider ID of the resource to lookup.
- state
- Any extra arguments used during the lookup.
- opts
- A bag of options that control this resource's behavior.
- Allow
Queueing bool - Whether new scheduled runs can be queued when maxConcurrentRuns limit is reached. If set to true, new runs will be queued instead of skipped. Default to false.
- Catch
Up bool - 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.
- Create
Notebook AiExecution Job Request Schedule Create Notebook Execution Job Request - Request message for [NotebookService.CreateNotebookExecutionJob] Structure is documented below.
- Create
Pipeline AiJob Request Schedule Create Pipeline Job Request - Request message for PipelineService.CreatePipelineJob. Structure is documented below.
- Create
Time string - 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 * * * *".
- Deletion
Policy string - Whether Terraform will be prevented from destroying the resource. Defaults to DELETE. When a 'terraform destroy' or 'pulumi up' would delete the resource, the command will fail if this field is set to "PREVENT" in Terraform state. When set to "ABANDON", the command will remove the resource from Terraform management without updating or deleting the resource in the API. When set to "DELETE", deleting the resource is allowed.
- Display
Name string - User provided name of the Schedule. The name can be up to 128 characters long and can consist of any UTF-8 characters.
- End
Time string - Timestamp after which no new runs can be scheduled. If specified, The schedule will be completed when either endTime is reached or when scheduledRunCount >= 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.
- Last
Pause stringTime - Timestamp when this Schedule was last paused. Unset if never paused.
- Last
Resume stringTime - Timestamp when this Schedule was last resumed. Unset if never resumed from pause.
- Last
Scheduled List<AiRun Responses Schedule Last Scheduled Run Response> - Status of a scheduled run. Structure is documented below.
- Location string
- The location of the Schedule. eg us-central1
- Max
Concurrent stringActive Run Count - 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.
- Max
Concurrent stringRun Count - 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).
- Max
Run stringCount - Maximum run count of the schedule. If specified, The schedule will be completed when either startedRunCount >= maxRunCount or when endTime 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
- The resource name of the Schedule.
- Next
Run stringTime - Timestamp when this Schedule should schedule the next run. Having a nextRunTime in the past means the runs are being started behind schedule.
- Project string
- The ID of the project in which the resource belongs. If it is not provided, the provider project is used.
- Start
Time string - Timestamp after which the first run can be scheduled. Default to Schedule create time if not specified.
- Started
Run stringCount - The number of runs started by this schedule.
- State string
- The state of this Schedule. Possible values: ACTIVE PAUSED COMPLETED
- Update
Time string - Timestamp when this Schedule was updated.
- Allow
Queueing bool - Whether new scheduled runs can be queued when maxConcurrentRuns limit is reached. If set to true, new runs will be queued instead of skipped. Default to false.
- Catch
Up bool - 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.
- Create
Notebook AiExecution Job Request Schedule Create Notebook Execution Job Request Args - Request message for [NotebookService.CreateNotebookExecutionJob] Structure is documented below.
- Create
Pipeline AiJob Request Schedule Create Pipeline Job Request Args - Request message for PipelineService.CreatePipelineJob. Structure is documented below.
- Create
Time string - 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 * * * *".
- Deletion
Policy string - Whether Terraform will be prevented from destroying the resource. Defaults to DELETE. When a 'terraform destroy' or 'pulumi up' would delete the resource, the command will fail if this field is set to "PREVENT" in Terraform state. When set to "ABANDON", the command will remove the resource from Terraform management without updating or deleting the resource in the API. When set to "DELETE", deleting the resource is allowed.
- Display
Name string - User provided name of the Schedule. The name can be up to 128 characters long and can consist of any UTF-8 characters.
- End
Time string - Timestamp after which no new runs can be scheduled. If specified, The schedule will be completed when either endTime is reached or when scheduledRunCount >= 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.
- Last
Pause stringTime - Timestamp when this Schedule was last paused. Unset if never paused.
- Last
Resume stringTime - Timestamp when this Schedule was last resumed. Unset if never resumed from pause.
- Last
Scheduled []AiRun Responses Schedule Last Scheduled Run Response Args - Status of a scheduled run. Structure is documented below.
- Location string
- The location of the Schedule. eg us-central1
- Max
Concurrent stringActive Run Count - 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.
- Max
Concurrent stringRun Count - 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).
- Max
Run stringCount - Maximum run count of the schedule. If specified, The schedule will be completed when either startedRunCount >= maxRunCount or when endTime 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
- The resource name of the Schedule.
- Next
Run stringTime - Timestamp when this Schedule should schedule the next run. Having a nextRunTime in the past means the runs are being started behind schedule.
- Project string
- The ID of the project in which the resource belongs. If it is not provided, the provider project is used.
- Start
Time string - Timestamp after which the first run can be scheduled. Default to Schedule create time if not specified.
- Started
Run stringCount - The number of runs started by this schedule.
- State string
- The state of this Schedule. Possible values: ACTIVE PAUSED COMPLETED
- Update
Time string - Timestamp when this Schedule was updated.
- allow_
queueing bool - Whether new scheduled runs can be queued when maxConcurrentRuns limit is reached. If set to true, new runs will be queued instead of skipped. Default to false.
- catch_
up bool - 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.
- create_
notebook_ objectexecution_ job_ request - Request message for [NotebookService.CreateNotebookExecutionJob] Structure is documented below.
- create_
pipeline_ objectjob_ request - Request message for PipelineService.CreatePipelineJob. Structure is documented below.
- create_
time string - 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 * * * *".
- deletion_
policy string - Whether Terraform will be prevented from destroying the resource. Defaults to DELETE. When a 'terraform destroy' or 'pulumi up' would delete the resource, the command will fail if this field is set to "PREVENT" in Terraform state. When set to "ABANDON", the command will remove the resource from Terraform management without updating or deleting the resource in the API. When set to "DELETE", deleting the resource is allowed.
- display_
name string - User provided name of the Schedule. The name can be up to 128 characters long and can consist of any UTF-8 characters.
- end_
time string - Timestamp after which no new runs can be scheduled. If specified, The schedule will be completed when either endTime is reached or when scheduledRunCount >= 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.
- last_
pause_ stringtime - Timestamp when this Schedule was last paused. Unset if never paused.
- last_
resume_ stringtime - Timestamp when this Schedule was last resumed. Unset if never resumed from pause.
- last_
scheduled_ list(object)run_ responses - Status of a scheduled run. Structure is documented below.
- location string
- The location of the Schedule. eg us-central1
- max_
concurrent_ stringactive_ run_ count - 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.
- max_
concurrent_ stringrun_ count - 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).
- max_
run_ stringcount - Maximum run count of the schedule. If specified, The schedule will be completed when either startedRunCount >= maxRunCount or when endTime 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
- The resource name of the Schedule.
- next_
run_ stringtime - Timestamp when this Schedule should schedule the next run. Having a nextRunTime in the past means the runs are being started behind schedule.
- project string
- The ID of the project in which the resource belongs. If it is not provided, the provider project is used.
- start_
time string - Timestamp after which the first run can be scheduled. Default to Schedule create time if not specified.
- started_
run_ stringcount - The number of runs started by this schedule.
- state string
- The state of this Schedule. Possible values: ACTIVE PAUSED COMPLETED
- update_
time string - Timestamp when this Schedule was updated.
- allow
Queueing Boolean - Whether new scheduled runs can be queued when maxConcurrentRuns limit is reached. If set to true, new runs will be queued instead of skipped. Default to false.
- catch
Up Boolean - 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.
- create
Notebook AiExecution Job Request Schedule Create Notebook Execution Job Request - Request message for [NotebookService.CreateNotebookExecutionJob] Structure is documented below.
- create
Pipeline AiJob Request Schedule Create Pipeline Job Request - Request message for PipelineService.CreatePipelineJob. Structure is documented below.
- create
Time String - 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 * * * *".
- deletion
Policy String - Whether Terraform will be prevented from destroying the resource. Defaults to DELETE. When a 'terraform destroy' or 'pulumi up' would delete the resource, the command will fail if this field is set to "PREVENT" in Terraform state. When set to "ABANDON", the command will remove the resource from Terraform management without updating or deleting the resource in the API. When set to "DELETE", deleting the resource is allowed.
- display
Name String - User provided name of the Schedule. The name can be up to 128 characters long and can consist of any UTF-8 characters.
- end
Time String - Timestamp after which no new runs can be scheduled. If specified, The schedule will be completed when either endTime is reached or when scheduledRunCount >= 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.
- last
Pause StringTime - Timestamp when this Schedule was last paused. Unset if never paused.
- last
Resume StringTime - Timestamp when this Schedule was last resumed. Unset if never resumed from pause.
- last
Scheduled List<AiRun Responses Schedule Last Scheduled Run Response> - Status of a scheduled run. Structure is documented below.
- location String
- The location of the Schedule. eg us-central1
- max
Concurrent StringActive Run Count - 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.
- max
Concurrent StringRun Count - 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).
- max
Run StringCount - Maximum run count of the schedule. If specified, The schedule will be completed when either startedRunCount >= maxRunCount or when endTime 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
- The resource name of the Schedule.
- next
Run StringTime - Timestamp when this Schedule should schedule the next run. Having a nextRunTime in the past means the runs are being started behind schedule.
- project String
- The ID of the project in which the resource belongs. If it is not provided, the provider project is used.
- start
Time String - Timestamp after which the first run can be scheduled. Default to Schedule create time if not specified.
- started
Run StringCount - The number of runs started by this schedule.
- state String
- The state of this Schedule. Possible values: ACTIVE PAUSED COMPLETED
- update
Time String - Timestamp when this Schedule was updated.
- allow
Queueing boolean - Whether new scheduled runs can be queued when maxConcurrentRuns limit is reached. If set to true, new runs will be queued instead of skipped. Default to false.
- catch
Up boolean - 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.
- create
Notebook AiExecution Job Request Schedule Create Notebook Execution Job Request - Request message for [NotebookService.CreateNotebookExecutionJob] Structure is documented below.
- create
Pipeline AiJob Request Schedule Create Pipeline Job Request - Request message for PipelineService.CreatePipelineJob. Structure is documented below.
- create
Time string - 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 * * * *".
- deletion
Policy string - Whether Terraform will be prevented from destroying the resource. Defaults to DELETE. When a 'terraform destroy' or 'pulumi up' would delete the resource, the command will fail if this field is set to "PREVENT" in Terraform state. When set to "ABANDON", the command will remove the resource from Terraform management without updating or deleting the resource in the API. When set to "DELETE", deleting the resource is allowed.
- display
Name string - User provided name of the Schedule. The name can be up to 128 characters long and can consist of any UTF-8 characters.
- end
Time string - Timestamp after which no new runs can be scheduled. If specified, The schedule will be completed when either endTime is reached or when scheduledRunCount >= 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.
- last
Pause stringTime - Timestamp when this Schedule was last paused. Unset if never paused.
- last
Resume stringTime - Timestamp when this Schedule was last resumed. Unset if never resumed from pause.
- last
Scheduled AiRun Responses Schedule Last Scheduled Run Response[] - Status of a scheduled run. Structure is documented below.
- location string
- The location of the Schedule. eg us-central1
- max
Concurrent stringActive Run Count - 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.
- max
Concurrent stringRun Count - 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).
- max
Run stringCount - Maximum run count of the schedule. If specified, The schedule will be completed when either startedRunCount >= maxRunCount or when endTime 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
- The resource name of the Schedule.
- next
Run stringTime - Timestamp when this Schedule should schedule the next run. Having a nextRunTime in the past means the runs are being started behind schedule.
- project string
- The ID of the project in which the resource belongs. If it is not provided, the provider project is used.
- start
Time string - Timestamp after which the first run can be scheduled. Default to Schedule create time if not specified.
- started
Run stringCount - The number of runs started by this schedule.
- state string
- The state of this Schedule. Possible values: ACTIVE PAUSED COMPLETED
- update
Time string - Timestamp when this Schedule was updated.
- allow_
queueing bool - Whether new scheduled runs can be queued when maxConcurrentRuns limit is reached. If set to true, new runs will be queued instead of skipped. Default to false.
- catch_
up bool - 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.
- create_
notebook_ Aiexecution_ job_ request Schedule Create Notebook Execution Job Request Args - Request message for [NotebookService.CreateNotebookExecutionJob] Structure is documented below.
- create_
pipeline_ Aijob_ request Schedule Create Pipeline Job Request Args - Request message for PipelineService.CreatePipelineJob. Structure is documented below.
- create_
time str - Timestamp when this Schedule was created.
- cron str
- 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 * * * *".
- deletion_
policy str - Whether Terraform will be prevented from destroying the resource. Defaults to DELETE. When a 'terraform destroy' or 'pulumi up' would delete the resource, the command will fail if this field is set to "PREVENT" in Terraform state. When set to "ABANDON", the command will remove the resource from Terraform management without updating or deleting the resource in the API. When set to "DELETE", deleting the resource is allowed.
- display_
name str - User provided name of the Schedule. The name can be up to 128 characters long and can consist of any UTF-8 characters.
- end_
time str - Timestamp after which no new runs can be scheduled. If specified, The schedule will be completed when either endTime is reached or when scheduledRunCount >= 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.
- last_
pause_ strtime - Timestamp when this Schedule was last paused. Unset if never paused.
- last_
resume_ strtime - Timestamp when this Schedule was last resumed. Unset if never resumed from pause.
- last_
scheduled_ Sequence[Airun_ responses Schedule Last Scheduled Run Response Args] - Status of a scheduled run. Structure is documented below.
- location str
- The location of the Schedule. eg us-central1
- max_
concurrent_ stractive_ run_ count - 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.
- max_
concurrent_ strrun_ count - 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).
- max_
run_ strcount - Maximum run count of the schedule. If specified, The schedule will be completed when either startedRunCount >= maxRunCount or when endTime 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 str
- The resource name of the Schedule.
- next_
run_ strtime - Timestamp when this Schedule should schedule the next run. Having a nextRunTime in the past means the runs are being started behind schedule.
- project str
- The ID of the project in which the resource belongs. If it is not provided, the provider project is used.
- start_
time str - Timestamp after which the first run can be scheduled. Default to Schedule create time if not specified.
- started_
run_ strcount - The number of runs started by this schedule.
- state str
- The state of this Schedule. Possible values: ACTIVE PAUSED COMPLETED
- update_
time str - Timestamp when this Schedule was updated.
- allow
Queueing Boolean - Whether new scheduled runs can be queued when maxConcurrentRuns limit is reached. If set to true, new runs will be queued instead of skipped. Default to false.
- catch
Up Boolean - 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.
- create
Notebook Property MapExecution Job Request - Request message for [NotebookService.CreateNotebookExecutionJob] Structure is documented below.
- create
Pipeline Property MapJob Request - Request message for PipelineService.CreatePipelineJob. Structure is documented below.
- create
Time String - 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 * * * *".
- deletion
Policy String - Whether Terraform will be prevented from destroying the resource. Defaults to DELETE. When a 'terraform destroy' or 'pulumi up' would delete the resource, the command will fail if this field is set to "PREVENT" in Terraform state. When set to "ABANDON", the command will remove the resource from Terraform management without updating or deleting the resource in the API. When set to "DELETE", deleting the resource is allowed.
- display
Name String - User provided name of the Schedule. The name can be up to 128 characters long and can consist of any UTF-8 characters.
- end
Time String - Timestamp after which no new runs can be scheduled. If specified, The schedule will be completed when either endTime is reached or when scheduledRunCount >= 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.
- last
Pause StringTime - Timestamp when this Schedule was last paused. Unset if never paused.
- last
Resume StringTime - Timestamp when this Schedule was last resumed. Unset if never resumed from pause.
- last
Scheduled List<Property Map>Run Responses - Status of a scheduled run. Structure is documented below.
- location String
- The location of the Schedule. eg us-central1
- max
Concurrent StringActive Run Count - 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.
- max
Concurrent StringRun Count - 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).
- max
Run StringCount - Maximum run count of the schedule. If specified, The schedule will be completed when either startedRunCount >= maxRunCount or when endTime 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
- The resource name of the Schedule.
- next
Run StringTime - Timestamp when this Schedule should schedule the next run. Having a nextRunTime in the past means the runs are being started behind schedule.
- project String
- The ID of the project in which the resource belongs. If it is not provided, the provider project is used.
- start
Time String - Timestamp after which the first run can be scheduled. Default to Schedule create time if not specified.
- started
Run StringCount - The number of runs started by this schedule.
- state String
- The state of this Schedule. Possible values: ACTIVE PAUSED COMPLETED
- update
Time String - Timestamp when this Schedule was updated.
Supporting Types
AiScheduleCreateNotebookExecutionJobRequest, AiScheduleCreateNotebookExecutionJobRequestArgs
- Notebook
Execution AiJob Schedule Create Notebook Execution Job Request Notebook Execution Job - NotebookExecutionJob represents an instance of a notebook execution. Structure is documented below.
- Parent string
- The resource name of the Location to create the NotebookExecutionJob. Format:
projects/{project}/locations/{location} - Notebook
Execution stringJob Id - User specified ID for the NotebookExecutionJob.
- Notebook
Execution AiJob Schedule Create Notebook Execution Job Request Notebook Execution Job - NotebookExecutionJob represents an instance of a notebook execution. Structure is documented below.
- Parent string
- The resource name of the Location to create the NotebookExecutionJob. Format:
projects/{project}/locations/{location} - Notebook
Execution stringJob Id - User specified ID for the NotebookExecutionJob.
- notebook_
execution_ objectjob - NotebookExecutionJob represents an instance of a notebook execution. Structure is documented below.
- parent string
- The resource name of the Location to create the NotebookExecutionJob. Format:
projects/{project}/locations/{location} - notebook_
execution_ stringjob_ id - User specified ID for the NotebookExecutionJob.
- notebook
Execution AiJob Schedule Create Notebook Execution Job Request Notebook Execution Job - NotebookExecutionJob represents an instance of a notebook execution. Structure is documented below.
- parent String
- The resource name of the Location to create the NotebookExecutionJob. Format:
projects/{project}/locations/{location} - notebook
Execution StringJob Id - User specified ID for the NotebookExecutionJob.
- notebook
Execution AiJob Schedule Create Notebook Execution Job Request Notebook Execution Job - NotebookExecutionJob represents an instance of a notebook execution. Structure is documented below.
- parent string
- The resource name of the Location to create the NotebookExecutionJob. Format:
projects/{project}/locations/{location} - notebook
Execution stringJob Id - User specified ID for the NotebookExecutionJob.
- notebook_
execution_ Aijob Schedule Create Notebook Execution Job Request Notebook Execution Job - NotebookExecutionJob represents an instance of a notebook execution. Structure is documented below.
- parent str
- The resource name of the Location to create the NotebookExecutionJob. Format:
projects/{project}/locations/{location} - notebook_
execution_ strjob_ id - User specified ID for the NotebookExecutionJob.
- notebook
Execution Property MapJob - NotebookExecutionJob represents an instance of a notebook execution. Structure is documented below.
- parent String
- The resource name of the Location to create the NotebookExecutionJob. Format:
projects/{project}/locations/{location} - notebook
Execution StringJob Id - User specified ID for the NotebookExecutionJob.
AiScheduleCreateNotebookExecutionJobRequestNotebookExecutionJob, AiScheduleCreateNotebookExecutionJobRequestNotebookExecutionJobArgs
- Create
Time string - (Output) Timestamp when this NotebookExecutionJob was created.
- Custom
Environment AiSpec Schedule Create Notebook Execution Job Request Notebook Execution Job Custom Environment Spec - Compute configuration to use for an execution job. Structure is documented below.
- Dataform
Repository AiSource Schedule Create Notebook Execution Job Request Notebook Execution Job Dataform Repository Source - The Dataform Repository containing the input notebook. Structure is documented below.
- Direct
Notebook AiSource Schedule Create Notebook Execution Job Request Notebook Execution Job Direct Notebook Source - The content of the input notebook in ipynb format. Structure is documented below.
- Display
Name string - The display name of the NotebookExecutionJob. The name can be up to 128 characters long and can consist of any UTF-8 characters.
- Encryption
Spec AiSchedule Create Notebook Execution Job Request Notebook Execution Job Encryption Spec - Represents a customer-managed encryption key specification that can be applied to a Vertex AI resource. Structure is documented below.
- Execution
Timeout string - Max running time of the execution job in seconds (default 86400s / 24 hrs).
- Execution
User string - The user email to run the execution as. Only supported by Colab runtimes.
- Gcs
Notebook AiSource Schedule Create Notebook Execution Job Request Notebook Execution Job Gcs Notebook Source - The Cloud Storage uri for the input notebook. Structure is documented below.
- Gcs
Output stringUri - The Cloud Storage location to upload the result to. Format:
gs://bucket-name - Job
State string - (Output) Possible values: 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
- Kernel
Name string - The name of the kernel to use during notebook execution. If unset, the default kernel is used.
- Labels Dictionary<string, string>
- 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)
The resource name of this NotebookExecutionJob. Format:
projects/{project_id}/locations/{location}/notebookExecutionJobs/{job_id} - Notebook
Runtime stringTemplate Resource Name - The NotebookRuntimeTemplate to source compute configuration from.
- Parameters Dictionary<string, string>
- The user-defined parameters to use during notebook execution.
- Schedule
Resource stringName - (Output)
The Schedule resource name if this job is triggered by one. Format:
projects/{project_id}/locations/{location}/schedules/{schedule_id} - Service
Account string - The service account to run the execution as.
- Update
Time string - (Output) Timestamp when this NotebookExecutionJob was most recently updated.
- Workbench
Runtime AiSchedule Create Notebook Execution Job Request Notebook Execution Job Workbench Runtime - Configuration for a Workbench Instances-based environment.
- Create
Time string - (Output) Timestamp when this NotebookExecutionJob was created.
- Custom
Environment AiSpec Schedule Create Notebook Execution Job Request Notebook Execution Job Custom Environment Spec - Compute configuration to use for an execution job. Structure is documented below.
- Dataform
Repository AiSource Schedule Create Notebook Execution Job Request Notebook Execution Job Dataform Repository Source - The Dataform Repository containing the input notebook. Structure is documented below.
- Direct
Notebook AiSource Schedule Create Notebook Execution Job Request Notebook Execution Job Direct Notebook Source - The content of the input notebook in ipynb format. Structure is documented below.
- Display
Name string - The display name of the NotebookExecutionJob. The name can be up to 128 characters long and can consist of any UTF-8 characters.
- Encryption
Spec AiSchedule Create Notebook Execution Job Request Notebook Execution Job Encryption Spec - Represents a customer-managed encryption key specification that can be applied to a Vertex AI resource. Structure is documented below.
- Execution
Timeout string - Max running time of the execution job in seconds (default 86400s / 24 hrs).
- Execution
User string - The user email to run the execution as. Only supported by Colab runtimes.
- Gcs
Notebook AiSource Schedule Create Notebook Execution Job Request Notebook Execution Job Gcs Notebook Source - The Cloud Storage uri for the input notebook. Structure is documented below.
- Gcs
Output stringUri - The Cloud Storage location to upload the result to. Format:
gs://bucket-name - Job
State string - (Output) Possible values: 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
- Kernel
Name string - The name of the kernel to use during notebook execution. If unset, the default kernel is used.
- Labels map[string]string
- 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)
The resource name of this NotebookExecutionJob. Format:
projects/{project_id}/locations/{location}/notebookExecutionJobs/{job_id} - Notebook
Runtime stringTemplate Resource Name - The NotebookRuntimeTemplate to source compute configuration from.
- Parameters map[string]string
- The user-defined parameters to use during notebook execution.
- Schedule
Resource stringName - (Output)
The Schedule resource name if this job is triggered by one. Format:
projects/{project_id}/locations/{location}/schedules/{schedule_id} - Service
Account string - The service account to run the execution as.
- Update
Time string - (Output) Timestamp when this NotebookExecutionJob was most recently updated.
- Workbench
Runtime AiSchedule Create Notebook Execution Job Request Notebook Execution Job Workbench Runtime - Configuration for a Workbench Instances-based environment.
- create_
time string - (Output) Timestamp when this NotebookExecutionJob was created.
- custom_
environment_ objectspec - Compute configuration to use for an execution job. Structure is documented below.
- dataform_
repository_ objectsource - The Dataform Repository containing the input notebook. Structure is documented below.
- direct_
notebook_ objectsource - The content of the input notebook in ipynb format. Structure is documented below.
- display_
name string - The display name of the NotebookExecutionJob. The name can be up to 128 characters long and can consist of any UTF-8 characters.
- encryption_
spec object - Represents a customer-managed encryption key specification that can be applied to a Vertex AI resource. Structure is documented below.
- execution_
timeout string - Max running time of the execution job in seconds (default 86400s / 24 hrs).
- execution_
user string - The user email to run the execution as. Only supported by Colab runtimes.
- gcs_
notebook_ objectsource - The Cloud Storage uri for the input notebook. Structure is documented below.
- gcs_
output_ stringuri - The Cloud Storage location to upload the result to. Format:
gs://bucket-name - job_
state string - (Output) Possible values: 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
- kernel_
name string - The name of the kernel to use during notebook execution. If unset, the default kernel is used.
- labels map(string)
- 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)
The resource name of this NotebookExecutionJob. Format:
projects/{project_id}/locations/{location}/notebookExecutionJobs/{job_id} - notebook_
runtime_ stringtemplate_ resource_ name - The NotebookRuntimeTemplate to source compute configuration from.
- parameters map(string)
- The user-defined parameters to use during notebook execution.
- schedule_
resource_ stringname - (Output)
The Schedule resource name if this job is triggered by one. Format:
projects/{project_id}/locations/{location}/schedules/{schedule_id} - service_
account string - The service account to run the execution as.
- update_
time string - (Output) Timestamp when this NotebookExecutionJob was most recently updated.
- workbench_
runtime object - Configuration for a Workbench Instances-based environment.
- create
Time String - (Output) Timestamp when this NotebookExecutionJob was created.
- custom
Environment AiSpec Schedule Create Notebook Execution Job Request Notebook Execution Job Custom Environment Spec - Compute configuration to use for an execution job. Structure is documented below.
- dataform
Repository AiSource Schedule Create Notebook Execution Job Request Notebook Execution Job Dataform Repository Source - The Dataform Repository containing the input notebook. Structure is documented below.
- direct
Notebook AiSource Schedule Create Notebook Execution Job Request Notebook Execution Job Direct Notebook Source - The content of the input notebook in ipynb format. Structure is documented below.
- display
Name String - The display name of the NotebookExecutionJob. The name can be up to 128 characters long and can consist of any UTF-8 characters.
- encryption
Spec AiSchedule Create Notebook Execution Job Request Notebook Execution Job Encryption Spec - Represents a customer-managed encryption key specification that can be applied to a Vertex AI resource. Structure is documented below.
- execution
Timeout String - Max running time of the execution job in seconds (default 86400s / 24 hrs).
- execution
User String - The user email to run the execution as. Only supported by Colab runtimes.
- gcs
Notebook AiSource Schedule Create Notebook Execution Job Request Notebook Execution Job Gcs Notebook Source - The Cloud Storage uri for the input notebook. Structure is documented below.
- gcs
Output StringUri - The Cloud Storage location to upload the result to. Format:
gs://bucket-name - job
State String - (Output) Possible values: 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
- kernel
Name String - The name of the kernel to use during notebook execution. If unset, the default kernel is used.
- labels Map<String,String>
- 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)
The resource name of this NotebookExecutionJob. Format:
projects/{project_id}/locations/{location}/notebookExecutionJobs/{job_id} - notebook
Runtime StringTemplate Resource Name - The NotebookRuntimeTemplate to source compute configuration from.
- parameters Map<String,String>
- The user-defined parameters to use during notebook execution.
- schedule
Resource StringName - (Output)
The Schedule resource name if this job is triggered by one. Format:
projects/{project_id}/locations/{location}/schedules/{schedule_id} - service
Account String - The service account to run the execution as.
- update
Time String - (Output) Timestamp when this NotebookExecutionJob was most recently updated.
- workbench
Runtime AiSchedule Create Notebook Execution Job Request Notebook Execution Job Workbench Runtime - Configuration for a Workbench Instances-based environment.
- create
Time string - (Output) Timestamp when this NotebookExecutionJob was created.
- custom
Environment AiSpec Schedule Create Notebook Execution Job Request Notebook Execution Job Custom Environment Spec - Compute configuration to use for an execution job. Structure is documented below.
- dataform
Repository AiSource Schedule Create Notebook Execution Job Request Notebook Execution Job Dataform Repository Source - The Dataform Repository containing the input notebook. Structure is documented below.
- direct
Notebook AiSource Schedule Create Notebook Execution Job Request Notebook Execution Job Direct Notebook Source - The content of the input notebook in ipynb format. Structure is documented below.
- display
Name string - The display name of the NotebookExecutionJob. The name can be up to 128 characters long and can consist of any UTF-8 characters.
- encryption
Spec AiSchedule Create Notebook Execution Job Request Notebook Execution Job Encryption Spec - Represents a customer-managed encryption key specification that can be applied to a Vertex AI resource. Structure is documented below.
- execution
Timeout string - Max running time of the execution job in seconds (default 86400s / 24 hrs).
- execution
User string - The user email to run the execution as. Only supported by Colab runtimes.
- gcs
Notebook AiSource Schedule Create Notebook Execution Job Request Notebook Execution Job Gcs Notebook Source - The Cloud Storage uri for the input notebook. Structure is documented below.
- gcs
Output stringUri - The Cloud Storage location to upload the result to. Format:
gs://bucket-name - job
State string - (Output) Possible values: 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
- kernel
Name string - The name of the kernel to use during notebook execution. If unset, the default kernel is used.
- labels {[key: string]: string}
- 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)
The resource name of this NotebookExecutionJob. Format:
projects/{project_id}/locations/{location}/notebookExecutionJobs/{job_id} - notebook
Runtime stringTemplate Resource Name - The NotebookRuntimeTemplate to source compute configuration from.
- parameters {[key: string]: string}
- The user-defined parameters to use during notebook execution.
- schedule
Resource stringName - (Output)
The Schedule resource name if this job is triggered by one. Format:
projects/{project_id}/locations/{location}/schedules/{schedule_id} - service
Account string - The service account to run the execution as.
- update
Time string - (Output) Timestamp when this NotebookExecutionJob was most recently updated.
- workbench
Runtime AiSchedule Create Notebook Execution Job Request Notebook Execution Job Workbench Runtime - Configuration for a Workbench Instances-based environment.
- create_
time str - (Output) Timestamp when this NotebookExecutionJob was created.
- custom_
environment_ Aispec Schedule Create Notebook Execution Job Request Notebook Execution Job Custom Environment Spec - Compute configuration to use for an execution job. Structure is documented below.
- dataform_
repository_ Aisource Schedule Create Notebook Execution Job Request Notebook Execution Job Dataform Repository Source - The Dataform Repository containing the input notebook. Structure is documented below.
- direct_
notebook_ Aisource Schedule Create Notebook Execution Job Request Notebook Execution Job Direct Notebook Source - The content of the input notebook in ipynb format. Structure is documented below.
- display_
name str - The display name of the NotebookExecutionJob. The name can be up to 128 characters long and can consist of any UTF-8 characters.
- encryption_
spec AiSchedule Create Notebook Execution Job Request Notebook Execution Job Encryption Spec - Represents a customer-managed encryption key specification that can be applied to a Vertex AI resource. Structure is documented below.
- execution_
timeout str - Max running time of the execution job in seconds (default 86400s / 24 hrs).
- execution_
user str - The user email to run the execution as. Only supported by Colab runtimes.
- gcs_
notebook_ Aisource Schedule Create Notebook Execution Job Request Notebook Execution Job Gcs Notebook Source - The Cloud Storage uri for the input notebook. Structure is documented below.
- gcs_
output_ struri - The Cloud Storage location to upload the result to. Format:
gs://bucket-name - job_
state str - (Output) Possible values: 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
- kernel_
name str - The name of the kernel to use during notebook execution. If unset, the default kernel is used.
- labels Mapping[str, str]
- 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 str
- (Output)
The resource name of this NotebookExecutionJob. Format:
projects/{project_id}/locations/{location}/notebookExecutionJobs/{job_id} - notebook_
runtime_ strtemplate_ resource_ name - The NotebookRuntimeTemplate to source compute configuration from.
- parameters Mapping[str, str]
- The user-defined parameters to use during notebook execution.
- schedule_
resource_ strname - (Output)
The Schedule resource name if this job is triggered by one. Format:
projects/{project_id}/locations/{location}/schedules/{schedule_id} - service_
account str - The service account to run the execution as.
- update_
time str - (Output) Timestamp when this NotebookExecutionJob was most recently updated.
- workbench_
runtime AiSchedule Create Notebook Execution Job Request Notebook Execution Job Workbench Runtime - Configuration for a Workbench Instances-based environment.
- create
Time String - (Output) Timestamp when this NotebookExecutionJob was created.
- custom
Environment Property MapSpec - Compute configuration to use for an execution job. Structure is documented below.
- dataform
Repository Property MapSource - The Dataform Repository containing the input notebook. Structure is documented below.
- direct
Notebook Property MapSource - The content of the input notebook in ipynb format. Structure is documented below.
- display
Name String - The display name of the NotebookExecutionJob. The name can be up to 128 characters long and can consist of any UTF-8 characters.
- encryption
Spec Property Map - Represents a customer-managed encryption key specification that can be applied to a Vertex AI resource. Structure is documented below.
- execution
Timeout String - Max running time of the execution job in seconds (default 86400s / 24 hrs).
- execution
User String - The user email to run the execution as. Only supported by Colab runtimes.
- gcs
Notebook Property MapSource - The Cloud Storage uri for the input notebook. Structure is documented below.
- gcs
Output StringUri - The Cloud Storage location to upload the result to. Format:
gs://bucket-name - job
State String - (Output) Possible values: 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
- kernel
Name String - The name of the kernel to use during notebook execution. If unset, the default kernel is used.
- labels Map<String>
- 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)
The resource name of this NotebookExecutionJob. Format:
projects/{project_id}/locations/{location}/notebookExecutionJobs/{job_id} - notebook
Runtime StringTemplate Resource Name - The NotebookRuntimeTemplate to source compute configuration from.
- parameters Map<String>
- The user-defined parameters to use during notebook execution.
- schedule
Resource StringName - (Output)
The Schedule resource name if this job is triggered by one. Format:
projects/{project_id}/locations/{location}/schedules/{schedule_id} - service
Account String - The service account to run the execution as.
- update
Time String - (Output) Timestamp when this NotebookExecutionJob was most recently updated.
- workbench
Runtime Property Map - Configuration for a Workbench Instances-based environment.
AiScheduleCreateNotebookExecutionJobRequestNotebookExecutionJobCustomEnvironmentSpec, AiScheduleCreateNotebookExecutionJobRequestNotebookExecutionJobCustomEnvironmentSpecArgs
- Machine
Spec AiSchedule Create Notebook Execution Job Request Notebook Execution Job Custom Environment Spec Machine Spec - Specification of a single machine. Structure is documented below.
- Network
Spec AiSchedule Create Notebook Execution Job Request Notebook Execution Job Custom Environment Spec Network Spec - Network spec. Structure is documented below.
- Persistent
Disk AiSpec Schedule Create Notebook Execution Job Request Notebook Execution Job Custom Environment Spec Persistent Disk Spec - Represents the spec of persistent disk options. Structure is documented below.
- Machine
Spec AiSchedule Create Notebook Execution Job Request Notebook Execution Job Custom Environment Spec Machine Spec - Specification of a single machine. Structure is documented below.
- Network
Spec AiSchedule Create Notebook Execution Job Request Notebook Execution Job Custom Environment Spec Network Spec - Network spec. Structure is documented below.
- Persistent
Disk AiSpec Schedule Create Notebook Execution Job Request Notebook Execution Job Custom Environment Spec Persistent Disk Spec - Represents the spec of persistent disk options. Structure is documented below.
- machine_
spec object - Specification of a single machine. Structure is documented below.
- network_
spec object - Network spec. Structure is documented below.
- persistent_
disk_ objectspec - Represents the spec of persistent disk options. Structure is documented below.
- machine
Spec AiSchedule Create Notebook Execution Job Request Notebook Execution Job Custom Environment Spec Machine Spec - Specification of a single machine. Structure is documented below.
- network
Spec AiSchedule Create Notebook Execution Job Request Notebook Execution Job Custom Environment Spec Network Spec - Network spec. Structure is documented below.
- persistent
Disk AiSpec Schedule Create Notebook Execution Job Request Notebook Execution Job Custom Environment Spec Persistent Disk Spec - Represents the spec of persistent disk options. Structure is documented below.
- machine
Spec AiSchedule Create Notebook Execution Job Request Notebook Execution Job Custom Environment Spec Machine Spec - Specification of a single machine. Structure is documented below.
- network
Spec AiSchedule Create Notebook Execution Job Request Notebook Execution Job Custom Environment Spec Network Spec - Network spec. Structure is documented below.
- persistent
Disk AiSpec Schedule Create Notebook Execution Job Request Notebook Execution Job Custom Environment Spec Persistent Disk Spec - Represents the spec of persistent disk options. Structure is documented below.
- machine_
spec AiSchedule Create Notebook Execution Job Request Notebook Execution Job Custom Environment Spec Machine Spec - Specification of a single machine. Structure is documented below.
- network_
spec AiSchedule Create Notebook Execution Job Request Notebook Execution Job Custom Environment Spec Network Spec - Network spec. Structure is documented below.
- persistent_
disk_ Aispec Schedule Create Notebook Execution Job Request Notebook Execution Job Custom Environment Spec Persistent Disk Spec - Represents the spec of persistent disk options. Structure is documented below.
- machine
Spec Property Map - Specification of a single machine. Structure is documented below.
- network
Spec Property Map - Network spec. Structure is documented below.
- persistent
Disk Property MapSpec - Represents the spec of persistent disk options. Structure is documented below.
AiScheduleCreateNotebookExecutionJobRequestNotebookExecutionJobCustomEnvironmentSpecMachineSpec, AiScheduleCreateNotebookExecutionJobRequestNotebookExecutionJobCustomEnvironmentSpecMachineSpecArgs
- Accelerator
Count int - 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 acceleratorCount from 1 to N for machine with N GPUs. If acceleratorCount is less than or equal to N / 2, Vertex will co-schedule 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 acceleratorCount to 1 to 8. If acceleratorCount is 1, 2, 3, or 4, Vertex will co-schedule 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 multihostGpuNodeCount is set, the co-scheduling will not be enabled.
- Accelerator
Type string - Possible values: 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
- Gpu
Partition stringSize - 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 for the available partition sizes. If set, the acceleratorCount should be set to 1.
- Machine
Type string - The type of the machine. See the list of machine types supported for prediction See the list of machine types supported for custom training. 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. - Reservation
Affinity AiSchedule Create Notebook Execution Job Request Notebook Execution Job Custom Environment Spec Machine Spec Reservation Affinity - 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. Structure is documented below.
- Tpu
Topology string - The topology of the TPUs. Corresponds to the TPU topologies available from GKE. (Example: tpu_topology: "2x2x1").
- Accelerator
Count int - 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 acceleratorCount from 1 to N for machine with N GPUs. If acceleratorCount is less than or equal to N / 2, Vertex will co-schedule 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 acceleratorCount to 1 to 8. If acceleratorCount is 1, 2, 3, or 4, Vertex will co-schedule 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 multihostGpuNodeCount is set, the co-scheduling will not be enabled.
- Accelerator
Type string - Possible values: 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
- Gpu
Partition stringSize - 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 for the available partition sizes. If set, the acceleratorCount should be set to 1.
- Machine
Type string - The type of the machine. See the list of machine types supported for prediction See the list of machine types supported for custom training. 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. - Reservation
Affinity AiSchedule Create Notebook Execution Job Request Notebook Execution Job Custom Environment Spec Machine Spec Reservation Affinity - 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. Structure is documented below.
- Tpu
Topology string - The topology of the TPUs. Corresponds to the TPU topologies available from GKE. (Example: tpu_topology: "2x2x1").
- accelerator_
count number - 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 acceleratorCount from 1 to N for machine with N GPUs. If acceleratorCount is less than or equal to N / 2, Vertex will co-schedule 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 acceleratorCount to 1 to 8. If acceleratorCount is 1, 2, 3, or 4, Vertex will co-schedule 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 multihostGpuNodeCount is set, the co-scheduling will not be enabled.
- accelerator_
type string - Possible values: 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
- gpu_
partition_ stringsize - 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 for the available partition sizes. If set, the acceleratorCount should be set to 1.
- machine_
type string - The type of the machine. See the list of machine types supported for prediction See the list of machine types supported for custom training. 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. - reservation_
affinity object - 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. Structure is documented below.
- tpu_
topology string - The topology of the TPUs. Corresponds to the TPU topologies available from GKE. (Example: tpu_topology: "2x2x1").
- accelerator
Count 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 acceleratorCount from 1 to N for machine with N GPUs. If acceleratorCount is less than or equal to N / 2, Vertex will co-schedule 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 acceleratorCount to 1 to 8. If acceleratorCount is 1, 2, 3, or 4, Vertex will co-schedule 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 multihostGpuNodeCount is set, the co-scheduling will not be enabled.
- accelerator
Type String - Possible values: 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
- gpu
Partition StringSize - 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 for the available partition sizes. If set, the acceleratorCount should be set to 1.
- machine
Type String - The type of the machine. See the list of machine types supported for prediction See the list of machine types supported for custom training. 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. - reservation
Affinity AiSchedule Create Notebook Execution Job Request Notebook Execution Job Custom Environment Spec Machine Spec Reservation Affinity - 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. Structure is documented below.
- tpu
Topology String - The topology of the TPUs. Corresponds to the TPU topologies available from GKE. (Example: tpu_topology: "2x2x1").
- accelerator
Count number - 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 acceleratorCount from 1 to N for machine with N GPUs. If acceleratorCount is less than or equal to N / 2, Vertex will co-schedule 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 acceleratorCount to 1 to 8. If acceleratorCount is 1, 2, 3, or 4, Vertex will co-schedule 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 multihostGpuNodeCount is set, the co-scheduling will not be enabled.
- accelerator
Type string - Possible values: 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
- gpu
Partition stringSize - 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 for the available partition sizes. If set, the acceleratorCount should be set to 1.
- machine
Type string - The type of the machine. See the list of machine types supported for prediction See the list of machine types supported for custom training. 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. - reservation
Affinity AiSchedule Create Notebook Execution Job Request Notebook Execution Job Custom Environment Spec Machine Spec Reservation Affinity - 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. Structure is documented below.
- tpu
Topology string - The topology of the TPUs. Corresponds to the TPU topologies available from GKE. (Example: tpu_topology: "2x2x1").
- accelerator_
count int - 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 acceleratorCount from 1 to N for machine with N GPUs. If acceleratorCount is less than or equal to N / 2, Vertex will co-schedule 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 acceleratorCount to 1 to 8. If acceleratorCount is 1, 2, 3, or 4, Vertex will co-schedule 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 multihostGpuNodeCount is set, the co-scheduling will not be enabled.
- accelerator_
type str - Possible values: 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
- gpu_
partition_ strsize - 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 for the available partition sizes. If set, the acceleratorCount should be set to 1.
- machine_
type str - The type of the machine. See the list of machine types supported for prediction See the list of machine types supported for custom training. 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. - reservation_
affinity AiSchedule Create Notebook Execution Job Request Notebook Execution Job Custom Environment Spec Machine Spec Reservation Affinity - 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. Structure is documented below.
- tpu_
topology str - The topology of the TPUs. Corresponds to the TPU topologies available from GKE. (Example: tpu_topology: "2x2x1").
- accelerator
Count Number - 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 acceleratorCount from 1 to N for machine with N GPUs. If acceleratorCount is less than or equal to N / 2, Vertex will co-schedule 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 acceleratorCount to 1 to 8. If acceleratorCount is 1, 2, 3, or 4, Vertex will co-schedule 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 multihostGpuNodeCount is set, the co-scheduling will not be enabled.
- accelerator
Type String - Possible values: 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
- gpu
Partition StringSize - 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 for the available partition sizes. If set, the acceleratorCount should be set to 1.
- machine
Type String - The type of the machine. See the list of machine types supported for prediction See the list of machine types supported for custom training. 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. - reservation
Affinity Property Map - 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. Structure is documented below.
- tpu
Topology String - The topology of the TPUs. Corresponds to the TPU topologies available from GKE. (Example: tpu_topology: "2x2x1").
AiScheduleCreateNotebookExecutionJobRequestNotebookExecutionJobCustomEnvironmentSpecMachineSpecReservationAffinity, AiScheduleCreateNotebookExecutionJobRequestNotebookExecutionJobCustomEnvironmentSpecMachineSpecReservationAffinityArgs
- Reservation
Affinity stringType - Specifies the reservation affinity type. Possible values: NO_RESERVATION ANY_RESERVATION SPECIFIC_RESERVATION SPECIFIC_THEN_ANY_RESERVATION SPECIFIC_THEN_NO_RESERVATION
- Key string
- Corresponds to the label key of a reservation resource. To target a SPECIFIC_RESERVATION by name, use
compute.googleapis.com/reservation-nameas the key and specify the name of your reservation as its value. - Use
Reservation boolPool - When set to true, resources will be drawn from go/cloud-ai-gcp-pool.
- Values List<string>
- Corresponds to the label values of a reservation resource. This must be the full resource name of the reservation or reservation block.
- Reservation
Affinity stringType - Specifies the reservation affinity type. Possible values: NO_RESERVATION ANY_RESERVATION SPECIFIC_RESERVATION SPECIFIC_THEN_ANY_RESERVATION SPECIFIC_THEN_NO_RESERVATION
- Key string
- Corresponds to the label key of a reservation resource. To target a SPECIFIC_RESERVATION by name, use
compute.googleapis.com/reservation-nameas the key and specify the name of your reservation as its value. - Use
Reservation boolPool - When set to true, resources will be drawn from go/cloud-ai-gcp-pool.
- Values []string
- Corresponds to the label values of a reservation resource. This must be the full resource name of the reservation or reservation block.
- reservation_
affinity_ stringtype - Specifies the reservation affinity type. Possible values: NO_RESERVATION ANY_RESERVATION SPECIFIC_RESERVATION SPECIFIC_THEN_ANY_RESERVATION SPECIFIC_THEN_NO_RESERVATION
- key string
- Corresponds to the label key of a reservation resource. To target a SPECIFIC_RESERVATION by name, use
compute.googleapis.com/reservation-nameas the key and specify the name of your reservation as its value. - use_
reservation_ boolpool - When set to true, resources will be drawn from go/cloud-ai-gcp-pool.
- values list(string)
- Corresponds to the label values of a reservation resource. This must be the full resource name of the reservation or reservation block.
- reservation
Affinity StringType - Specifies the reservation affinity type. Possible values: NO_RESERVATION ANY_RESERVATION SPECIFIC_RESERVATION SPECIFIC_THEN_ANY_RESERVATION SPECIFIC_THEN_NO_RESERVATION
- key String
- Corresponds to the label key of a reservation resource. To target a SPECIFIC_RESERVATION by name, use
compute.googleapis.com/reservation-nameas the key and specify the name of your reservation as its value. - use
Reservation BooleanPool - When set to true, resources will be drawn from go/cloud-ai-gcp-pool.
- values List<String>
- Corresponds to the label values of a reservation resource. This must be the full resource name of the reservation or reservation block.
- reservation
Affinity stringType - Specifies the reservation affinity type. Possible values: NO_RESERVATION ANY_RESERVATION SPECIFIC_RESERVATION SPECIFIC_THEN_ANY_RESERVATION SPECIFIC_THEN_NO_RESERVATION
- key string
- Corresponds to the label key of a reservation resource. To target a SPECIFIC_RESERVATION by name, use
compute.googleapis.com/reservation-nameas the key and specify the name of your reservation as its value. - use
Reservation booleanPool - When set to true, resources will be drawn from go/cloud-ai-gcp-pool.
- values string[]
- Corresponds to the label values of a reservation resource. This must be the full resource name of the reservation or reservation block.
- reservation_
affinity_ strtype - Specifies the reservation affinity type. Possible values: NO_RESERVATION ANY_RESERVATION SPECIFIC_RESERVATION SPECIFIC_THEN_ANY_RESERVATION SPECIFIC_THEN_NO_RESERVATION
- key str
- Corresponds to the label key of a reservation resource. To target a SPECIFIC_RESERVATION by name, use
compute.googleapis.com/reservation-nameas the key and specify the name of your reservation as its value. - use_
reservation_ boolpool - When set to true, resources will be drawn from go/cloud-ai-gcp-pool.
- values Sequence[str]
- Corresponds to the label values of a reservation resource. This must be the full resource name of the reservation or reservation block.
- reservation
Affinity StringType - Specifies the reservation affinity type. Possible values: NO_RESERVATION ANY_RESERVATION SPECIFIC_RESERVATION SPECIFIC_THEN_ANY_RESERVATION SPECIFIC_THEN_NO_RESERVATION
- key String
- Corresponds to the label key of a reservation resource. To target a SPECIFIC_RESERVATION by name, use
compute.googleapis.com/reservation-nameas the key and specify the name of your reservation as its value. - use
Reservation BooleanPool - When set to true, resources will be drawn from go/cloud-ai-gcp-pool.
- values List<String>
- Corresponds to the label values of a reservation resource. This must be the full resource name of the reservation or reservation block.
AiScheduleCreateNotebookExecutionJobRequestNotebookExecutionJobCustomEnvironmentSpecNetworkSpec, AiScheduleCreateNotebookExecutionJobRequestNotebookExecutionJobCustomEnvironmentSpecNetworkSpecArgs
- Enable
Internet boolAccess - Whether to enable public internet access. Default false.
- Network string
- The full name of the Google Compute Engine network
- Subnetwork string
- The name of the subnet that this instance is in. Format:
projects/{project_id_or_number}/regions/{region}/subnetworks/{subnetwork_id}
- Enable
Internet boolAccess - Whether to enable public internet access. Default false.
- Network string
- The full name of the Google Compute Engine network
- Subnetwork string
- The name of the subnet that this instance is in. Format:
projects/{project_id_or_number}/regions/{region}/subnetworks/{subnetwork_id}
- enable_
internet_ boolaccess - Whether to enable public internet access. Default false.
- network string
- The full name of the Google Compute Engine network
- subnetwork string
- The name of the subnet that this instance is in. Format:
projects/{project_id_or_number}/regions/{region}/subnetworks/{subnetwork_id}
- enable
Internet BooleanAccess - Whether to enable public internet access. Default false.
- network String
- The full name of the Google Compute Engine network
- subnetwork String
- The name of the subnet that this instance is in. Format:
projects/{project_id_or_number}/regions/{region}/subnetworks/{subnetwork_id}
- enable
Internet booleanAccess - Whether to enable public internet access. Default false.
- network string
- The full name of the Google Compute Engine network
- subnetwork string
- The name of the subnet that this instance is in. Format:
projects/{project_id_or_number}/regions/{region}/subnetworks/{subnetwork_id}
- enable_
internet_ boolaccess - Whether to enable public internet access. Default false.
- network str
- The full name of the Google Compute Engine network
- subnetwork str
- The name of the subnet that this instance is in. Format:
projects/{project_id_or_number}/regions/{region}/subnetworks/{subnetwork_id}
- enable
Internet BooleanAccess - Whether to enable public internet access. Default false.
- network String
- The full name of the Google Compute Engine network
- subnetwork String
- The name of the subnet that this instance is in. Format:
projects/{project_id_or_number}/regions/{region}/subnetworks/{subnetwork_id}
AiScheduleCreateNotebookExecutionJobRequestNotebookExecutionJobCustomEnvironmentSpecPersistentDiskSpec, AiScheduleCreateNotebookExecutionJobRequestNotebookExecutionJobCustomEnvironmentSpecPersistentDiskSpecArgs
- Disk
Size stringGb - Size in GB of the disk (default is 100GB).
- Disk
Type 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)
- Disk
Size stringGb - Size in GB of the disk (default is 100GB).
- Disk
Type 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)
- disk_
size_ stringgb - Size in GB of the disk (default is 100GB).
- disk_
type 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)
- disk
Size StringGb - Size in GB of the disk (default is 100GB).
- disk
Type 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)
- disk
Size stringGb - Size in GB of the disk (default is 100GB).
- disk
Type 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)
- disk_
size_ strgb - Size in GB of the disk (default is 100GB).
- disk_
type str - 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)
- disk
Size StringGb - Size in GB of the disk (default is 100GB).
- disk
Type 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)
AiScheduleCreateNotebookExecutionJobRequestNotebookExecutionJobDataformRepositorySource, AiScheduleCreateNotebookExecutionJobRequestNotebookExecutionJobDataformRepositorySourceArgs
- Commit
Sha string - The commit SHA to read repository with. If unset, the file will be read at HEAD.
- Dataform
Repository stringResource Name - The resource name of the Dataform Repository. Format:
projects/{project_id}/locations/{location}/repositories/{repository_id}
- Commit
Sha string - The commit SHA to read repository with. If unset, the file will be read at HEAD.
- Dataform
Repository stringResource Name - The resource name of the Dataform Repository. Format:
projects/{project_id}/locations/{location}/repositories/{repository_id}
- commit_
sha string - The commit SHA to read repository with. If unset, the file will be read at HEAD.
- dataform_
repository_ stringresource_ name - The resource name of the Dataform Repository. Format:
projects/{project_id}/locations/{location}/repositories/{repository_id}
- commit
Sha String - The commit SHA to read repository with. If unset, the file will be read at HEAD.
- dataform
Repository StringResource Name - The resource name of the Dataform Repository. Format:
projects/{project_id}/locations/{location}/repositories/{repository_id}
- commit
Sha string - The commit SHA to read repository with. If unset, the file will be read at HEAD.
- dataform
Repository stringResource Name - The resource name of the Dataform Repository. Format:
projects/{project_id}/locations/{location}/repositories/{repository_id}
- commit_
sha str - The commit SHA to read repository with. If unset, the file will be read at HEAD.
- dataform_
repository_ strresource_ name - The resource name of the Dataform Repository. Format:
projects/{project_id}/locations/{location}/repositories/{repository_id}
- commit
Sha String - The commit SHA to read repository with. If unset, the file will be read at HEAD.
- dataform
Repository StringResource Name - The resource name of the Dataform Repository. Format:
projects/{project_id}/locations/{location}/repositories/{repository_id}
AiScheduleCreateNotebookExecutionJobRequestNotebookExecutionJobDirectNotebookSource, AiScheduleCreateNotebookExecutionJobRequestNotebookExecutionJobDirectNotebookSourceArgs
- Content string
- The base64-encoded contents of the input notebook file.
- Content string
- The base64-encoded contents of the input notebook file.
- content string
- The base64-encoded contents of the input notebook file.
- content String
- The base64-encoded contents of the input notebook file.
- content string
- The base64-encoded contents of the input notebook file.
- content str
- The base64-encoded contents of the input notebook file.
- content String
- The base64-encoded contents of the input notebook file.
AiScheduleCreateNotebookExecutionJobRequestNotebookExecutionJobEncryptionSpec, AiScheduleCreateNotebookExecutionJobRequestNotebookExecutionJobEncryptionSpecArgs
- Kms
Key stringName - 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}.
- Kms
Key stringName - 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}.
- kms_
key_ stringname - 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}.
- kms
Key StringName - 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}.
- kms
Key stringName - 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}.
- kms_
key_ strname - 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}.
- kms
Key StringName - 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}.
AiScheduleCreateNotebookExecutionJobRequestNotebookExecutionJobGcsNotebookSource, AiScheduleCreateNotebookExecutionJobRequestNotebookExecutionJobGcsNotebookSourceArgs
- 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
- 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
- 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
- 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
- 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
- generation str
- 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 str
- The Cloud Storage uri pointing to the ipynb file. Format:
gs://bucket/notebook_file.ipynb
- 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
AiScheduleCreatePipelineJobRequest, AiScheduleCreatePipelineJobRequestArgs
- Parent string
- The resource name of the Location to create the PipelineJob in. Format:
projects/{project}/locations/{location} - Pipeline
Job AiSchedule Create Pipeline Job Request Pipeline Job - An instance of a machine learning PipelineJob. Structure is documented below.
- Pipeline
Job stringId - 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-/.
- Parent string
- The resource name of the Location to create the PipelineJob in. Format:
projects/{project}/locations/{location} - Pipeline
Job AiSchedule Create Pipeline Job Request Pipeline Job - An instance of a machine learning PipelineJob. Structure is documented below.
- Pipeline
Job stringId - 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-/.
- parent string
- The resource name of the Location to create the PipelineJob in. Format:
projects/{project}/locations/{location} - pipeline_
job object - An instance of a machine learning PipelineJob. Structure is documented below.
- pipeline_
job_ stringid - 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-/.
- parent String
- The resource name of the Location to create the PipelineJob in. Format:
projects/{project}/locations/{location} - pipeline
Job AiSchedule Create Pipeline Job Request Pipeline Job - An instance of a machine learning PipelineJob. Structure is documented below.
- pipeline
Job StringId - 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-/.
- parent string
- The resource name of the Location to create the PipelineJob in. Format:
projects/{project}/locations/{location} - pipeline
Job AiSchedule Create Pipeline Job Request Pipeline Job - An instance of a machine learning PipelineJob. Structure is documented below.
- pipeline
Job stringId - 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-/.
- parent str
- The resource name of the Location to create the PipelineJob in. Format:
projects/{project}/locations/{location} - pipeline_
job AiSchedule Create Pipeline Job Request Pipeline Job - An instance of a machine learning PipelineJob. Structure is documented below.
- pipeline_
job_ strid - 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-/.
- parent String
- The resource name of the Location to create the PipelineJob in. Format:
projects/{project}/locations/{location} - pipeline
Job Property Map - An instance of a machine learning PipelineJob. Structure is documented below.
- pipeline
Job StringId - 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-/.
AiScheduleCreatePipelineJobRequestPipelineJob, AiScheduleCreatePipelineJobRequestPipelineJobArgs
- Create
Time string - (Output) Pipeline creation time.
- Display
Name string - The display name of the Pipeline. The name can be up to 128 characters long and can consist of any UTF-8 characters.
- Encryption
Spec AiSchedule Create Pipeline Job Request Pipeline Job Encryption Spec - Represents a customer-managed encryption key specification that can be applied to a Vertex AI resource. Structure is documented below.
- End
Time string - (Output) Pipeline end time.
- Labels Dictionary<string, string>
- 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) The resource name of the PipelineJob.
- Network string
- The full name of the Compute Engine network to which the Pipeline Job's workload should be peered. For example,
projects/12345/global/networks/myVPC. Format is of the formprojects/{project}/global/networks/{network}. Where {project} is a project number, as in12345, 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. - Pipeline
Spec string - A compiled definition of a pipeline, represented as a
JSONobject. Defines the structure of the pipeline, including its components, tasks, and parameters. This specification is generated by compiling a pipeline function defined inPythonusing theKubeflow Pipelines SDK. - Preflight
Validations bool - Whether to do component level validations before job creation.
- Psc
Interface AiConfig Schedule Create Pipeline Job Request Pipeline Job Psc Interface Config - Configuration for PSC-I. Structure is documented below.
- Reserved
Ip List<string>Ranges - 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'].
- Runtime
Config AiSchedule Create Pipeline Job Request Pipeline Job Runtime Config - The runtime config of a PipelineJob. Structure is documented below.
- Schedule
Name string - (Output) The schedule resource name. Only returned if the Pipeline is created by Schedule API.
- Service
Account 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.actAspermission on this service account. - Start
Time string - (Output) Pipeline start time.
- State string
- (Output) Possible values: PIPELINE_STATE_QUEUED PIPELINE_STATE_PENDING PIPELINE_STATE_RUNNING PIPELINE_STATE_SUCCEEDED PIPELINE_STATE_FAILED PIPELINE_STATE_CANCELLING PIPELINE_STATE_CANCELLED PIPELINE_STATE_PAUSED
- Template
Metadatas List<AiSchedule Create Pipeline Job Request Pipeline Job Template Metadata> - (Output) Pipeline template metadata if PipelineJob.template_uri is from supported template registry. Currently, the only supported registry is Artifact Registry. Structure is documented below.
- Template
Uri 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.
- Update
Time string - (Output) Timestamp when this PipelineJob was most recently updated.
- Create
Time string - (Output) Pipeline creation time.
- Display
Name string - The display name of the Pipeline. The name can be up to 128 characters long and can consist of any UTF-8 characters.
- Encryption
Spec AiSchedule Create Pipeline Job Request Pipeline Job Encryption Spec - Represents a customer-managed encryption key specification that can be applied to a Vertex AI resource. Structure is documented below.
- End
Time string - (Output) Pipeline end time.
- Labels map[string]string
- 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) The resource name of the PipelineJob.
- Network string
- The full name of the Compute Engine network to which the Pipeline Job's workload should be peered. For example,
projects/12345/global/networks/myVPC. Format is of the formprojects/{project}/global/networks/{network}. Where {project} is a project number, as in12345, 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. - Pipeline
Spec string - A compiled definition of a pipeline, represented as a
JSONobject. Defines the structure of the pipeline, including its components, tasks, and parameters. This specification is generated by compiling a pipeline function defined inPythonusing theKubeflow Pipelines SDK. - Preflight
Validations bool - Whether to do component level validations before job creation.
- Psc
Interface AiConfig Schedule Create Pipeline Job Request Pipeline Job Psc Interface Config - Configuration for PSC-I. Structure is documented below.
- Reserved
Ip []stringRanges - 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'].
- Runtime
Config AiSchedule Create Pipeline Job Request Pipeline Job Runtime Config - The runtime config of a PipelineJob. Structure is documented below.
- Schedule
Name string - (Output) The schedule resource name. Only returned if the Pipeline is created by Schedule API.
- Service
Account 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.actAspermission on this service account. - Start
Time string - (Output) Pipeline start time.
- State string
- (Output) Possible values: PIPELINE_STATE_QUEUED PIPELINE_STATE_PENDING PIPELINE_STATE_RUNNING PIPELINE_STATE_SUCCEEDED PIPELINE_STATE_FAILED PIPELINE_STATE_CANCELLING PIPELINE_STATE_CANCELLED PIPELINE_STATE_PAUSED
- Template
Metadatas []AiSchedule Create Pipeline Job Request Pipeline Job Template Metadata - (Output) Pipeline template metadata if PipelineJob.template_uri is from supported template registry. Currently, the only supported registry is Artifact Registry. Structure is documented below.
- Template
Uri 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.
- Update
Time string - (Output) Timestamp when this PipelineJob was most recently updated.
- create_
time string - (Output) Pipeline creation time.
- display_
name string - The display name of the Pipeline. The name can be up to 128 characters long and can consist of any UTF-8 characters.
- encryption_
spec object - Represents a customer-managed encryption key specification that can be applied to a Vertex AI resource. Structure is documented below.
- end_
time string - (Output) Pipeline end time.
- labels map(string)
- 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) The resource name of the PipelineJob.
- network string
- The full name of the Compute Engine network to which the Pipeline Job's workload should be peered. For example,
projects/12345/global/networks/myVPC. Format is of the formprojects/{project}/global/networks/{network}. Where {project} is a project number, as in12345, 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. - pipeline_
spec string - A compiled definition of a pipeline, represented as a
JSONobject. Defines the structure of the pipeline, including its components, tasks, and parameters. This specification is generated by compiling a pipeline function defined inPythonusing theKubeflow Pipelines SDK. - preflight_
validations bool - Whether to do component level validations before job creation.
- psc_
interface_ objectconfig - Configuration for PSC-I. Structure is documented below.
- reserved_
ip_ list(string)ranges - 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'].
- runtime_
config object - The runtime config of a PipelineJob. Structure is documented below.
- schedule_
name string - (Output) The schedule resource name. Only returned if the Pipeline is created by Schedule API.
- service_
account 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.actAspermission on this service account. - start_
time string - (Output) Pipeline start time.
- state string
- (Output) Possible values: PIPELINE_STATE_QUEUED PIPELINE_STATE_PENDING PIPELINE_STATE_RUNNING PIPELINE_STATE_SUCCEEDED PIPELINE_STATE_FAILED PIPELINE_STATE_CANCELLING PIPELINE_STATE_CANCELLED PIPELINE_STATE_PAUSED
- template_
metadatas list(object) - (Output) Pipeline template metadata if PipelineJob.template_uri is from supported template registry. Currently, the only supported registry is Artifact Registry. Structure is documented below.
- template_
uri 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.
- update_
time string - (Output) Timestamp when this PipelineJob was most recently updated.
- create
Time String - (Output) Pipeline creation time.
- display
Name String - The display name of the Pipeline. The name can be up to 128 characters long and can consist of any UTF-8 characters.
- encryption
Spec AiSchedule Create Pipeline Job Request Pipeline Job Encryption Spec - Represents a customer-managed encryption key specification that can be applied to a Vertex AI resource. Structure is documented below.
- end
Time String - (Output) Pipeline end time.
- labels Map<String,String>
- 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) The resource name of the PipelineJob.
- network String
- The full name of the Compute Engine network to which the Pipeline Job's workload should be peered. For example,
projects/12345/global/networks/myVPC. Format is of the formprojects/{project}/global/networks/{network}. Where {project} is a project number, as in12345, 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. - pipeline
Spec String - A compiled definition of a pipeline, represented as a
JSONobject. Defines the structure of the pipeline, including its components, tasks, and parameters. This specification is generated by compiling a pipeline function defined inPythonusing theKubeflow Pipelines SDK. - preflight
Validations Boolean - Whether to do component level validations before job creation.
- psc
Interface AiConfig Schedule Create Pipeline Job Request Pipeline Job Psc Interface Config - Configuration for PSC-I. Structure is documented below.
- reserved
Ip List<String>Ranges - 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'].
- runtime
Config AiSchedule Create Pipeline Job Request Pipeline Job Runtime Config - The runtime config of a PipelineJob. Structure is documented below.
- schedule
Name String - (Output) The schedule resource name. Only returned if the Pipeline is created by Schedule API.
- service
Account 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.actAspermission on this service account. - start
Time String - (Output) Pipeline start time.
- state String
- (Output) Possible values: PIPELINE_STATE_QUEUED PIPELINE_STATE_PENDING PIPELINE_STATE_RUNNING PIPELINE_STATE_SUCCEEDED PIPELINE_STATE_FAILED PIPELINE_STATE_CANCELLING PIPELINE_STATE_CANCELLED PIPELINE_STATE_PAUSED
- template
Metadatas List<AiSchedule Create Pipeline Job Request Pipeline Job Template Metadata> - (Output) Pipeline template metadata if PipelineJob.template_uri is from supported template registry. Currently, the only supported registry is Artifact Registry. Structure is documented below.
- template
Uri 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.
- update
Time String - (Output) Timestamp when this PipelineJob was most recently updated.
- create
Time string - (Output) Pipeline creation time.
- display
Name string - The display name of the Pipeline. The name can be up to 128 characters long and can consist of any UTF-8 characters.
- encryption
Spec AiSchedule Create Pipeline Job Request Pipeline Job Encryption Spec - Represents a customer-managed encryption key specification that can be applied to a Vertex AI resource. Structure is documented below.
- end
Time string - (Output) Pipeline end time.
- labels {[key: string]: string}
- 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) The resource name of the PipelineJob.
- network string
- The full name of the Compute Engine network to which the Pipeline Job's workload should be peered. For example,
projects/12345/global/networks/myVPC. Format is of the formprojects/{project}/global/networks/{network}. Where {project} is a project number, as in12345, 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. - pipeline
Spec string - A compiled definition of a pipeline, represented as a
JSONobject. Defines the structure of the pipeline, including its components, tasks, and parameters. This specification is generated by compiling a pipeline function defined inPythonusing theKubeflow Pipelines SDK. - preflight
Validations boolean - Whether to do component level validations before job creation.
- psc
Interface AiConfig Schedule Create Pipeline Job Request Pipeline Job Psc Interface Config - Configuration for PSC-I. Structure is documented below.
- reserved
Ip string[]Ranges - 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'].
- runtime
Config AiSchedule Create Pipeline Job Request Pipeline Job Runtime Config - The runtime config of a PipelineJob. Structure is documented below.
- schedule
Name string - (Output) The schedule resource name. Only returned if the Pipeline is created by Schedule API.
- service
Account 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.actAspermission on this service account. - start
Time string - (Output) Pipeline start time.
- state string
- (Output) Possible values: PIPELINE_STATE_QUEUED PIPELINE_STATE_PENDING PIPELINE_STATE_RUNNING PIPELINE_STATE_SUCCEEDED PIPELINE_STATE_FAILED PIPELINE_STATE_CANCELLING PIPELINE_STATE_CANCELLED PIPELINE_STATE_PAUSED
- template
Metadatas AiSchedule Create Pipeline Job Request Pipeline Job Template Metadata[] - (Output) Pipeline template metadata if PipelineJob.template_uri is from supported template registry. Currently, the only supported registry is Artifact Registry. Structure is documented below.
- template
Uri 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.
- update
Time string - (Output) Timestamp when this PipelineJob was most recently updated.
- create_
time str - (Output) Pipeline creation time.
- display_
name str - The display name of the Pipeline. The name can be up to 128 characters long and can consist of any UTF-8 characters.
- encryption_
spec AiSchedule Create Pipeline Job Request Pipeline Job Encryption Spec - Represents a customer-managed encryption key specification that can be applied to a Vertex AI resource. Structure is documented below.
- end_
time str - (Output) Pipeline end time.
- labels Mapping[str, str]
- 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 str
- (Output) The resource name of the PipelineJob.
- network str
- The full name of the Compute Engine network to which the Pipeline Job's workload should be peered. For example,
projects/12345/global/networks/myVPC. Format is of the formprojects/{project}/global/networks/{network}. Where {project} is a project number, as in12345, 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. - pipeline_
spec str - A compiled definition of a pipeline, represented as a
JSONobject. Defines the structure of the pipeline, including its components, tasks, and parameters. This specification is generated by compiling a pipeline function defined inPythonusing theKubeflow Pipelines SDK. - preflight_
validations bool - Whether to do component level validations before job creation.
- psc_
interface_ Aiconfig Schedule Create Pipeline Job Request Pipeline Job Psc Interface Config - Configuration for PSC-I. Structure is documented below.
- reserved_
ip_ Sequence[str]ranges - 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'].
- runtime_
config AiSchedule Create Pipeline Job Request Pipeline Job Runtime Config - The runtime config of a PipelineJob. Structure is documented below.
- schedule_
name str - (Output) The schedule resource name. Only returned if the Pipeline is created by Schedule API.
- service_
account str - 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.actAspermission on this service account. - start_
time str - (Output) Pipeline start time.
- state str
- (Output) Possible values: PIPELINE_STATE_QUEUED PIPELINE_STATE_PENDING PIPELINE_STATE_RUNNING PIPELINE_STATE_SUCCEEDED PIPELINE_STATE_FAILED PIPELINE_STATE_CANCELLING PIPELINE_STATE_CANCELLED PIPELINE_STATE_PAUSED
- template_
metadatas Sequence[AiSchedule Create Pipeline Job Request Pipeline Job Template Metadata] - (Output) Pipeline template metadata if PipelineJob.template_uri is from supported template registry. Currently, the only supported registry is Artifact Registry. Structure is documented below.
- template_
uri str - 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.
- update_
time str - (Output) Timestamp when this PipelineJob was most recently updated.
- create
Time String - (Output) Pipeline creation time.
- display
Name String - The display name of the Pipeline. The name can be up to 128 characters long and can consist of any UTF-8 characters.
- encryption
Spec Property Map - Represents a customer-managed encryption key specification that can be applied to a Vertex AI resource. Structure is documented below.
- end
Time String - (Output) Pipeline end time.
- labels Map<String>
- 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) The resource name of the PipelineJob.
- network String
- The full name of the Compute Engine network to which the Pipeline Job's workload should be peered. For example,
projects/12345/global/networks/myVPC. Format is of the formprojects/{project}/global/networks/{network}. Where {project} is a project number, as in12345, 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. - pipeline
Spec String - A compiled definition of a pipeline, represented as a
JSONobject. Defines the structure of the pipeline, including its components, tasks, and parameters. This specification is generated by compiling a pipeline function defined inPythonusing theKubeflow Pipelines SDK. - preflight
Validations Boolean - Whether to do component level validations before job creation.
- psc
Interface Property MapConfig - Configuration for PSC-I. Structure is documented below.
- reserved
Ip List<String>Ranges - 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'].
- runtime
Config Property Map - The runtime config of a PipelineJob. Structure is documented below.
- schedule
Name String - (Output) The schedule resource name. Only returned if the Pipeline is created by Schedule API.
- service
Account 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.actAspermission on this service account. - start
Time String - (Output) Pipeline start time.
- state String
- (Output) Possible values: PIPELINE_STATE_QUEUED PIPELINE_STATE_PENDING PIPELINE_STATE_RUNNING PIPELINE_STATE_SUCCEEDED PIPELINE_STATE_FAILED PIPELINE_STATE_CANCELLING PIPELINE_STATE_CANCELLED PIPELINE_STATE_PAUSED
- template
Metadatas List<Property Map> - (Output) Pipeline template metadata if PipelineJob.template_uri is from supported template registry. Currently, the only supported registry is Artifact Registry. Structure is documented below.
- template
Uri 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.
- update
Time String - (Output) Timestamp when this PipelineJob was most recently updated.
AiScheduleCreatePipelineJobRequestPipelineJobEncryptionSpec, AiScheduleCreatePipelineJobRequestPipelineJobEncryptionSpecArgs
- Kms
Key stringName - 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}.
- Kms
Key stringName - 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}.
- kms_
key_ stringname - 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}.
- kms
Key StringName - 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}.
- kms
Key stringName - 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}.
- kms_
key_ strname - 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}.
- kms
Key StringName - 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}.
AiScheduleCreatePipelineJobRequestPipelineJobPscInterfaceConfig, AiScheduleCreatePipelineJobRequestPipelineJobPscInterfaceConfigArgs
- Dns
Peering List<AiConfigs Schedule Create Pipeline Job Request Pipeline Job Psc Interface Config Dns Peering Config> - 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. Structure is documented below.
- Network
Attachment string - The name of the Compute Engine network attachment 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.
- Dns
Peering []AiConfigs Schedule Create Pipeline Job Request Pipeline Job Psc Interface Config Dns Peering Config - 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. Structure is documented below.
- Network
Attachment string - The name of the Compute Engine network attachment 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.
- dns_
peering_ list(object)configs - 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. Structure is documented below.
- network_
attachment string - The name of the Compute Engine network attachment 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.
- dns
Peering List<AiConfigs Schedule Create Pipeline Job Request Pipeline Job Psc Interface Config Dns Peering Config> - 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. Structure is documented below.
- network
Attachment String - The name of the Compute Engine network attachment 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.
- dns
Peering AiConfigs Schedule Create Pipeline Job Request Pipeline Job Psc Interface Config Dns Peering Config[] - 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. Structure is documented below.
- network
Attachment string - The name of the Compute Engine network attachment 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.
- dns_
peering_ Sequence[Aiconfigs Schedule Create Pipeline Job Request Pipeline Job Psc Interface Config Dns Peering Config] - 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. Structure is documented below.
- network_
attachment str - The name of the Compute Engine network attachment 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.
- dns
Peering List<Property Map>Configs - 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. Structure is documented below.
- network
Attachment String - The name of the Compute Engine network attachment 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.
AiScheduleCreatePipelineJobRequestPipelineJobPscInterfaceConfigDnsPeeringConfig, AiScheduleCreatePipelineJobRequestPipelineJobPscInterfaceConfigDnsPeeringConfigArgs
- Domain string
- The DNS name suffix of the zone being peered to, e.g., "my-internal-domain.corp.". Must end with a dot.
- Target
Network string - The VPC network name in the targetProject where the DNS zone specified by 'domain' is visible.
- Target
Project string - 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.
- Domain string
- The DNS name suffix of the zone being peered to, e.g., "my-internal-domain.corp.". Must end with a dot.
- Target
Network string - The VPC network name in the targetProject where the DNS zone specified by 'domain' is visible.
- Target
Project string - 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.
- domain string
- The DNS name suffix of the zone being peered to, e.g., "my-internal-domain.corp.". Must end with a dot.
- target_
network string - The VPC network name in the targetProject where the DNS zone specified by 'domain' is visible.
- target_
project string - 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.
- domain String
- The DNS name suffix of the zone being peered to, e.g., "my-internal-domain.corp.". Must end with a dot.
- target
Network String - The VPC network name in the targetProject where the DNS zone specified by 'domain' is visible.
- target
Project String - 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.
- domain string
- The DNS name suffix of the zone being peered to, e.g., "my-internal-domain.corp.". Must end with a dot.
- target
Network string - The VPC network name in the targetProject where the DNS zone specified by 'domain' is visible.
- target
Project string - 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.
- domain str
- The DNS name suffix of the zone being peered to, e.g., "my-internal-domain.corp.". Must end with a dot.
- target_
network str - The VPC network name in the targetProject where the DNS zone specified by 'domain' is visible.
- target_
project str - 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.
- domain String
- The DNS name suffix of the zone being peered to, e.g., "my-internal-domain.corp.". Must end with a dot.
- target
Network String - The VPC network name in the targetProject where the DNS zone specified by 'domain' is visible.
- target
Project String - 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.
AiScheduleCreatePipelineJobRequestPipelineJobRuntimeConfig, AiScheduleCreatePipelineJobRequestPipelineJobRuntimeConfigArgs
- Gcs
Output stringDirectory - 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 thestorage.objects.getandstorage.objects.createpermissions for this bucket. - Failure
Policy string - Possible values: PIPELINE_FAILURE_POLICY_FAIL_SLOW PIPELINE_FAILURE_POLICY_FAIL_FAST
- Input
Artifacts Dictionary<string, string> - The runtime artifacts of the PipelineJob. The key will be the input artifact name and the value would be one of the InputArtifact.
- Parameter
Values Dictionary<string, string> 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_version2.1.0, such as pipelines built using Kubeflow Pipelines SDK 1.9 or higher and the v2 DSL.The
templateMetadatablock contains:
- Gcs
Output stringDirectory - 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 thestorage.objects.getandstorage.objects.createpermissions for this bucket. - Failure
Policy string - Possible values: PIPELINE_FAILURE_POLICY_FAIL_SLOW PIPELINE_FAILURE_POLICY_FAIL_FAST
- Input
Artifacts map[string]string - The runtime artifacts of the PipelineJob. The key will be the input artifact name and the value would be one of the InputArtifact.
- Parameter
Values map[string]string 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_version2.1.0, such as pipelines built using Kubeflow Pipelines SDK 1.9 or higher and the v2 DSL.The
templateMetadatablock contains:
- gcs_
output_ stringdirectory - 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 thestorage.objects.getandstorage.objects.createpermissions for this bucket. - failure_
policy string - Possible values: PIPELINE_FAILURE_POLICY_FAIL_SLOW PIPELINE_FAILURE_POLICY_FAIL_FAST
- input_
artifacts map(string) - The runtime artifacts of the PipelineJob. The key will be the input artifact name and the value would be one of the InputArtifact.
- parameter_
values map(string) 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_version2.1.0, such as pipelines built using Kubeflow Pipelines SDK 1.9 or higher and the v2 DSL.The
templateMetadatablock contains:
- gcs
Output StringDirectory - 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 thestorage.objects.getandstorage.objects.createpermissions for this bucket. - failure
Policy String - Possible values: PIPELINE_FAILURE_POLICY_FAIL_SLOW PIPELINE_FAILURE_POLICY_FAIL_FAST
- input
Artifacts Map<String,String> - The runtime artifacts of the PipelineJob. The key will be the input artifact name and the value would be one of the InputArtifact.
- parameter
Values Map<String,String> 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_version2.1.0, such as pipelines built using Kubeflow Pipelines SDK 1.9 or higher and the v2 DSL.The
templateMetadatablock contains:
- gcs
Output stringDirectory - 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 thestorage.objects.getandstorage.objects.createpermissions for this bucket. - failure
Policy string - Possible values: PIPELINE_FAILURE_POLICY_FAIL_SLOW PIPELINE_FAILURE_POLICY_FAIL_FAST
- input
Artifacts {[key: string]: string} - The runtime artifacts of the PipelineJob. The key will be the input artifact name and the value would be one of the InputArtifact.
- parameter
Values {[key: string]: string} 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_version2.1.0, such as pipelines built using Kubeflow Pipelines SDK 1.9 or higher and the v2 DSL.The
templateMetadatablock contains:
- gcs_
output_ strdirectory - 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 thestorage.objects.getandstorage.objects.createpermissions for this bucket. - failure_
policy str - Possible values: PIPELINE_FAILURE_POLICY_FAIL_SLOW PIPELINE_FAILURE_POLICY_FAIL_FAST
- input_
artifacts Mapping[str, str] - The runtime artifacts of the PipelineJob. The key will be the input artifact name and the value would be one of the InputArtifact.
- parameter_
values Mapping[str, str] 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_version2.1.0, such as pipelines built using Kubeflow Pipelines SDK 1.9 or higher and the v2 DSL.The
templateMetadatablock contains:
- gcs
Output StringDirectory - 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 thestorage.objects.getandstorage.objects.createpermissions for this bucket. - failure
Policy String - Possible values: PIPELINE_FAILURE_POLICY_FAIL_SLOW PIPELINE_FAILURE_POLICY_FAIL_FAST
- input
Artifacts Map<String> - The runtime artifacts of the PipelineJob. The key will be the input artifact name and the value would be one of the InputArtifact.
- parameter
Values Map<String> 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_version2.1.0, such as pipelines built using Kubeflow Pipelines SDK 1.9 or higher and the v2 DSL.The
templateMetadatablock contains:
AiScheduleCreatePipelineJobRequestPipelineJobTemplateMetadata, AiScheduleCreatePipelineJobRequestPipelineJobTemplateMetadataArgs
- Version string
- The versionName in artifact registry. Will always be presented in output if the PipelineJob.template_uri is from supported template registry. Format is "sha256:abcdef123456...".
- Version string
- The versionName in artifact registry. Will always be presented in output if the PipelineJob.template_uri is from supported template registry. Format is "sha256:abcdef123456...".
- version string
- The versionName in artifact registry. Will always be presented in output if the PipelineJob.template_uri is from supported template registry. Format is "sha256:abcdef123456...".
- version String
- The versionName in artifact registry. Will always be presented in output if the PipelineJob.template_uri is from supported template registry. Format is "sha256:abcdef123456...".
- version string
- The versionName in artifact registry. Will always be presented in output if the PipelineJob.template_uri is from supported template registry. Format is "sha256:abcdef123456...".
- version str
- The versionName in artifact registry. Will always be presented in output if the PipelineJob.template_uri is from supported template registry. Format is "sha256:abcdef123456...".
- version String
- The versionName in artifact registry. Will always be presented in output if the PipelineJob.template_uri is from supported template registry. Format is "sha256:abcdef123456...".
AiScheduleLastScheduledRunResponse, AiScheduleLastScheduledRunResponseArgs
- Run
Response string - (Output) The response of the scheduled run.
- Scheduled
Run stringTime - (Output) The scheduled run time based on the user-specified schedule.
- Run
Response string - (Output) The response of the scheduled run.
- Scheduled
Run stringTime - (Output) The scheduled run time based on the user-specified schedule.
- run_
response string - (Output) The response of the scheduled run.
- scheduled_
run_ stringtime - (Output) The scheduled run time based on the user-specified schedule.
- run
Response String - (Output) The response of the scheduled run.
- scheduled
Run StringTime - (Output) The scheduled run time based on the user-specified schedule.
- run
Response string - (Output) The response of the scheduled run.
- scheduled
Run stringTime - (Output) The scheduled run time based on the user-specified schedule.
- run_
response str - (Output) The response of the scheduled run.
- scheduled_
run_ strtime - (Output) The scheduled run time based on the user-specified schedule.
- run
Response String - (Output) The response of the scheduled run.
- scheduled
Run StringTime - (Output) The scheduled run time based on the user-specified schedule.
Import
Schedule can be imported using any of these accepted formats:
projects/{{project}}/locations/{{location}}/schedules/{{name}}{{project}}/{{location}}/{{name}}{{location}}/{{name}}
When using the pulumi import command, Schedule can be imported using one of the formats above. For example:
$ pulumi import gcp:vertex/aiSchedule:AiSchedule default projects/{{project}}/locations/{{location}}/schedules/{{name}}
$ pulumi import gcp:vertex/aiSchedule:AiSchedule default {{project}}/{{location}}/{{name}}
$ pulumi import gcp:vertex/aiSchedule:AiSchedule default {{location}}/{{name}}
To learn more about importing existing cloud resources, see Importing resources.
Package Details
- Repository
- Google Cloud (GCP) Classic pulumi/pulumi-gcp
- License
- Apache-2.0
- Notes
- This Pulumi package is based on the
google-betaTerraform Provider.
Viewing docs for Google Cloud v9.29.0
published on Wednesday, Jun 24, 2026 by Pulumi
published on Wednesday, Jun 24, 2026 by Pulumi