Ai Feature Group
Vertex AI Feature Group. To get more information about FeatureGroup, see:
How-to Guides
Example Usage
Vertex Ai Feature Group
import * as pulumi from "@pulumi/pulumi";
import * as gcp from "@pulumi/gcp";
const sampleDataset = new gcp.bigquery.Dataset("sample_dataset", {
datasetId: "job_load_dataset",
friendlyName: "test",
description: "This is a test description",
location: "US",
});
const sampleTable = new gcp.bigquery.Table("sample_table", {
deletionProtection: false,
datasetId: sampleDataset.datasetId,
tableId: "job_load_table",
schema: `[
{
"name": "feature_id",
"type": "STRING",
"mode": "NULLABLE"
},
{
"name": "feature_timestamp",
"type": "TIMESTAMP",
"mode": "NULLABLE"
}
]
`,
});
const featureGroup = new gcp.vertex.AiFeatureGroup("feature_group", {
name: "example_feature_group",
description: "A sample feature group",
region: "us-central1",
labels: {
"label-one": "value-one",
},
bigQuery: {
bigQuerySource: {
inputUri: pulumi.interpolate`bq://${sampleTable.project}.${sampleTable.datasetId}.${sampleTable.tableId}`,
},
entityIdColumns: ["feature_id"],
},
});
import pulumi
import pulumi_gcp as gcp
sample_dataset = gcp.bigquery.Dataset("sample_dataset",
dataset_id="job_load_dataset",
friendly_name="test",
description="This is a test description",
location="US")
sample_table = gcp.bigquery.Table("sample_table",
deletion_protection=False,
dataset_id=sample_dataset.dataset_id,
table_id="job_load_table",
schema="""[
{
"name": "feature_id",
"type": "STRING",
"mode": "NULLABLE"
},
{
"name": "feature_timestamp",
"type": "TIMESTAMP",
"mode": "NULLABLE"
}
]
""")
feature_group = gcp.vertex.AiFeatureGroup("feature_group",
name="example_feature_group",
description="A sample feature group",
region="us-central1",
labels={
"label-one": "value-one",
},
big_query={
"big_query_source": {
"input_uri": pulumi.Output.all(
project=sample_table.project,
dataset_id=sample_table.dataset_id,
table_id=sample_table.table_id
).apply(lambda resolved_outputs: f"bq://{resolved_outputs['project']}.{resolved_outputs['dataset_id']}.{resolved_outputs['table_id']}")
,
},
"entity_id_columns": ["feature_id"],
})
using System.Collections.Generic;
using System.Linq;
using Pulumi;
using Gcp = Pulumi.Gcp;
return await Deployment.RunAsync(() =>
{
var sampleDataset = new Gcp.BigQuery.Dataset("sample_dataset", new()
{
DatasetId = "job_load_dataset",
FriendlyName = "test",
Description = "This is a test description",
Location = "US",
});
var sampleTable = new Gcp.BigQuery.Table("sample_table", new()
{
DeletionProtection = false,
DatasetId = sampleDataset.DatasetId,
TableId = "job_load_table",
Schema = @"[
{
""name"": ""feature_id"",
""type"": ""STRING"",
""mode"": ""NULLABLE""
},
{
""name"": ""feature_timestamp"",
""type"": ""TIMESTAMP"",
""mode"": ""NULLABLE""
}
]
",
});
var featureGroup = new Gcp.Vertex.AiFeatureGroup("feature_group", new()
{
Name = "example_feature_group",
Description = "A sample feature group",
Region = "us-central1",
Labels =
{
{ "label-one", "value-one" },
},
BigQuery = new Gcp.Vertex.Inputs.AiFeatureGroupBigQueryArgs
{
BigQuerySource = new Gcp.Vertex.Inputs.AiFeatureGroupBigQueryBigQuerySourceArgs
{
InputUri = Output.Tuple(sampleTable.Project, sampleTable.DatasetId, sampleTable.TableId).Apply(values =>
{
var project = values.Item1;
var datasetId = values.Item2;
var tableId = values.Item3;
return $"bq://{project}.{datasetId}.{tableId}";
}),
},
EntityIdColumns = new[]
{
"feature_id",
},
},
});
});
package main
import (
"fmt"
"github.com/pulumi/pulumi-gcp/sdk/v7/go/gcp/bigquery"
"github.com/pulumi/pulumi-gcp/sdk/v7/go/gcp/vertex"
"github.com/pulumi/pulumi/sdk/v3/go/pulumi"
)
func main() {
pulumi.Run(func(ctx *pulumi.Context) error {
sampleDataset, err := bigquery.NewDataset(ctx, "sample_dataset", &bigquery.DatasetArgs{
DatasetId: pulumi.String("job_load_dataset"),
FriendlyName: pulumi.String("test"),
Description: pulumi.String("This is a test description"),
Location: pulumi.String("US"),
})
if err != nil {
return err
}
sampleTable, err := bigquery.NewTable(ctx, "sample_table", &bigquery.TableArgs{
DeletionProtection: pulumi.Bool(false),
DatasetId: sampleDataset.DatasetId,
TableId: pulumi.String("job_load_table"),
Schema: pulumi.String(`[
{
"name": "feature_id",
"type": "STRING",
"mode": "NULLABLE"
},
{
"name": "feature_timestamp",
"type": "TIMESTAMP",
"mode": "NULLABLE"
}
]
`),
})
if err != nil {
return err
}
_, err = vertex.NewAiFeatureGroup(ctx, "feature_group", &vertex.AiFeatureGroupArgs{
Name: pulumi.String("example_feature_group"),
Description: pulumi.String("A sample feature group"),
Region: pulumi.String("us-central1"),
Labels: pulumi.StringMap{
"label-one": pulumi.String("value-one"),
},
BigQuery: &vertex.AiFeatureGroupBigQueryArgs{
BigQuerySource: &vertex.AiFeatureGroupBigQueryBigQuerySourceArgs{
InputUri: pulumi.All(sampleTable.Project, sampleTable.DatasetId, sampleTable.TableId).ApplyT(func(_args []interface{}) (string, error) {
project := _args[0].(string)
datasetId := _args[1].(string)
tableId := _args[2].(string)
return fmt.Sprintf("bq://%v.%v.%v", project, datasetId, tableId), nil
}).(pulumi.StringOutput),
},
EntityIdColumns: pulumi.StringArray{
pulumi.String("feature_id"),
},
},
})
if err != nil {
return err
}
return nil
})
}
package generated_program;
import com.pulumi.Context;
import com.pulumi.Pulumi;
import com.pulumi.core.Output;
import com.pulumi.gcp.bigquery.Dataset;
import com.pulumi.gcp.bigquery.DatasetArgs;
import com.pulumi.gcp.bigquery.Table;
import com.pulumi.gcp.bigquery.TableArgs;
import com.pulumi.gcp.vertex.AiFeatureGroup;
import com.pulumi.gcp.vertex.AiFeatureGroupArgs;
import com.pulumi.gcp.vertex.inputs.AiFeatureGroupBigQueryArgs;
import com.pulumi.gcp.vertex.inputs.AiFeatureGroupBigQueryBigQuerySourceArgs;
import java.util.List;
import java.util.ArrayList;
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) {
var sampleDataset = new Dataset("sampleDataset", DatasetArgs.builder()
.datasetId("job_load_dataset")
.friendlyName("test")
.description("This is a test description")
.location("US")
.build());
var sampleTable = new Table("sampleTable", TableArgs.builder()
.deletionProtection(false)
.datasetId(sampleDataset.datasetId())
.tableId("job_load_table")
.schema("""
[
{
"name": "feature_id",
"type": "STRING",
"mode": "NULLABLE"
},
{
"name": "feature_timestamp",
"type": "TIMESTAMP",
"mode": "NULLABLE"
}
]
""")
.build());
var featureGroup = new AiFeatureGroup("featureGroup", AiFeatureGroupArgs.builder()
.name("example_feature_group")
.description("A sample feature group")
.region("us-central1")
.labels(Map.of("label-one", "value-one"))
.bigQuery(AiFeatureGroupBigQueryArgs.builder()
.bigQuerySource(AiFeatureGroupBigQueryBigQuerySourceArgs.builder()
.inputUri(Output.tuple(sampleTable.project(), sampleTable.datasetId(), sampleTable.tableId()).applyValue(values -> {
var project = values.t1;
var datasetId = values.t2;
var tableId = values.t3;
return String.format("bq://%s.%s.%s", project,datasetId,tableId);
}))
.build())
.entityIdColumns("feature_id")
.build())
.build());
}
}
resources:
featureGroup:
type: gcp:vertex:AiFeatureGroup
name: feature_group
properties:
name: example_feature_group
description: A sample feature group
region: us-central1
labels:
label-one: value-one
bigQuery:
bigQuerySource:
inputUri: bq://${sampleTable.project}.${sampleTable.datasetId}.${sampleTable.tableId}
entityIdColumns:
- feature_id
sampleDataset:
type: gcp:bigquery:Dataset
name: sample_dataset
properties:
datasetId: job_load_dataset
friendlyName: test
description: This is a test description
location: US
sampleTable:
type: gcp:bigquery:Table
name: sample_table
properties:
deletionProtection: false
datasetId: ${sampleDataset.datasetId}
tableId: job_load_table
schema: |
[
{
"name": "feature_id",
"type": "STRING",
"mode": "NULLABLE"
},
{
"name": "feature_timestamp",
"type": "TIMESTAMP",
"mode": "NULLABLE"
}
]
Import
FeatureGroup can be imported using any of these accepted formats:
projects/{{project}}/locations/{{region}}/featureGroups/{{name}}
{{project}}/{{region}}/{{name}}
{{region}}/{{name}}
{{name}}
When using thepulumi import
command, FeatureGroup can be imported using one of the formats above. For example:
$ pulumi import gcp:vertex/aiFeatureGroup:AiFeatureGroup default projects/{{project}}/locations/{{region}}/featureGroups/{{name}}
$ pulumi import gcp:vertex/aiFeatureGroup:AiFeatureGroup default {{project}}/{{region}}/{{name}}
$ pulumi import gcp:vertex/aiFeatureGroup:AiFeatureGroup default {{region}}/{{name}}
$ pulumi import gcp:vertex/aiFeatureGroup:AiFeatureGroup default {{name}}
Properties
Indicates that features for this group come from BigQuery Table/View. By default treats the source as a sparse time series source, which is required to have an entityId and a feature_timestamp column in the source. Structure is documented below.
The timestamp of when the FeatureGroup was created in RFC3339 UTC "Zulu" format, with nanosecond resolution and up to nine fractional digits.
The description of the FeatureGroup.
All of labels (key/value pairs) present on the resource in GCP, including the labels configured through Pulumi, other clients and services.
The labels with user-defined metadata to organize your FeatureGroup. Note: This field is non-authoritative, and will only manage the labels present in your configuration. Please refer to the field effective_labels
for all of the labels present on the resource.
The combination of labels configured directly on the resource and default labels configured on the provider.
The timestamp of when the FeatureGroup was last updated in RFC3339 UTC "Zulu" format, with nanosecond resolution and up to nine fractional digits.