databricks.getMlflowModel

import * as pulumi from "@pulumi/pulumi";
import * as databricks from "@pulumi/databricks";

const thisMlflowModel = new databricks.MlflowModel("this", {
    name: "My MLflow Model",
    description: "My MLflow model description",
    tags: [
        {
            key: "key1",
            value: "value1",
        },
        {
            key: "key2",
            value: "value2",
        },
    ],
});
const this = databricks.getMlflowModel({
    name: "My MLflow Model",
});
export const model = _this;

import pulumi
import pulumi_databricks as databricks

this_mlflow_model = databricks.MlflowModel("this",
    name="My MLflow Model",
    description="My MLflow model description",
    tags=[
        databricks.MlflowModelTagArgs(
            key="key1",
            value="value1",
        ),
        databricks.MlflowModelTagArgs(
            key="key2",
            value="value2",
        ),
    ])
this = databricks.get_mlflow_model(name="My MLflow Model")
pulumi.export("model", this)

package main

import (
	"github.com/pulumi/pulumi-databricks/sdk/go/databricks"
	"github.com/pulumi/pulumi/sdk/v3/go/pulumi"
)

func main() {
	pulumi.Run(func(ctx *pulumi.Context) error {
		_, err := databricks.NewMlflowModel(ctx, "this", &databricks.MlflowModelArgs{
			Name:        pulumi.String("My MLflow Model"),
			Description: pulumi.String("My MLflow model description"),
			Tags: databricks.MlflowModelTagArray{
				&databricks.MlflowModelTagArgs{
					Key:   pulumi.String("key1"),
					Value: pulumi.String("value1"),
				},
				&databricks.MlflowModelTagArgs{
					Key:   pulumi.String("key2"),
					Value: pulumi.String("value2"),
				},
			},
		})
		if err != nil {
			return err
		}
		this, err := databricks.LookupMlflowModel(ctx, &databricks.LookupMlflowModelArgs{
			Name: "My MLflow Model",
		}, nil)
		if err != nil {
			return err
		}
		ctx.Export("model", this)
		return nil
	})
}

using System.Collections.Generic;
using System.Linq;
using Pulumi;
using Databricks = Pulumi.Databricks;

return await Deployment.RunAsync(() => 
{
    var thisMlflowModel = new Databricks.MlflowModel("this", new()
    {
        Name = "My MLflow Model",
        Description = "My MLflow model description",
        Tags = new[]
        {
            new Databricks.Inputs.MlflowModelTagArgs
            {
                Key = "key1",
                Value = "value1",
            },
            new Databricks.Inputs.MlflowModelTagArgs
            {
                Key = "key2",
                Value = "value2",
            },
        },
    });

    var @this = Databricks.GetMlflowModel.Invoke(new()
    {
        Name = "My MLflow Model",
    });

    return new Dictionary<string, object?>
    {
        ["model"] = @this,
    };
});

package generated_program;

import com.pulumi.Context;
import com.pulumi.Pulumi;
import com.pulumi.core.Output;
import com.pulumi.databricks.MlflowModel;
import com.pulumi.databricks.MlflowModelArgs;
import com.pulumi.databricks.inputs.MlflowModelTagArgs;
import com.pulumi.databricks.DatabricksFunctions;
import com.pulumi.databricks.inputs.GetMlflowModelArgs;
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 thisMlflowModel = new MlflowModel("thisMlflowModel", MlflowModelArgs.builder()        
            .name("My MLflow Model")
            .description("My MLflow model description")
            .tags(            
                MlflowModelTagArgs.builder()
                    .key("key1")
                    .value("value1")
                    .build(),
                MlflowModelTagArgs.builder()
                    .key("key2")
                    .value("value2")
                    .build())
            .build());

        final var this = DatabricksFunctions.getMlflowModel(GetMlflowModelArgs.builder()
            .name("My MLflow Model")
            .build());

        ctx.export("model", this_);
    }
}

resources:
  thisMlflowModel:
    type: databricks:MlflowModel
    name: this
    properties:
      name: My MLflow Model
      description: My MLflow model description
      tags:
        - key: key1
          value: value1
        - key: key2
          value: value2
variables:
  this:
    fn::invoke:
      Function: databricks:getMlflowModel
      Arguments:
        name: My MLflow Model
outputs:
  model: ${this}

import * as pulumi from "@pulumi/pulumi";
import * as databricks from "@pulumi/databricks";

const this = databricks.getMlflowModel({
    name: "My MLflow Model with multiple versions",
});
const thisModelServing = new databricks.ModelServing("this", {
    name: "model-serving-endpoint",
    config: {
        servedModels: [{
            name: "model_serving_prod",
            modelName: _this.then(_this => _this.name),
            modelVersion: _this.then(_this => _this.latestVersions?.[0]?.version),
            workloadSize: "Small",
            scaleToZeroEnabled: true,
        }],
    },
});

import pulumi
import pulumi_databricks as databricks

this = databricks.get_mlflow_model(name="My MLflow Model with multiple versions")
this_model_serving = databricks.ModelServing("this",
    name="model-serving-endpoint",
    config=databricks.ModelServingConfigArgs(
        served_models=[databricks.ModelServingConfigServedModelArgs(
            name="model_serving_prod",
            model_name=this.name,
            model_version=this.latest_versions[0].version,
            workload_size="Small",
            scale_to_zero_enabled=True,
        )],
    ))

package main

import (
	"github.com/pulumi/pulumi-databricks/sdk/go/databricks"
	"github.com/pulumi/pulumi/sdk/v3/go/pulumi"
)

func main() {
	pulumi.Run(func(ctx *pulumi.Context) error {
		this, err := databricks.LookupMlflowModel(ctx, &databricks.LookupMlflowModelArgs{
			Name: "My MLflow Model with multiple versions",
		}, nil)
		if err != nil {
			return err
		}
		_, err = databricks.NewModelServing(ctx, "this", &databricks.ModelServingArgs{
			Name: pulumi.String("model-serving-endpoint"),
			Config: &databricks.ModelServingConfigArgs{
				ServedModels: databricks.ModelServingConfigServedModelArray{
					&databricks.ModelServingConfigServedModelArgs{
						Name:               pulumi.String("model_serving_prod"),
						ModelName:          pulumi.String(this.Name),
						ModelVersion:       pulumi.String(this.LatestVersions[0].Version),
						WorkloadSize:       pulumi.String("Small"),
						ScaleToZeroEnabled: pulumi.Bool(true),
					},
				},
			},
		})
		if err != nil {
			return err
		}
		return nil
	})
}

using System.Collections.Generic;
using System.Linq;
using Pulumi;
using Databricks = Pulumi.Databricks;

return await Deployment.RunAsync(() => 
{
    var @this = Databricks.GetMlflowModel.Invoke(new()
    {
        Name = "My MLflow Model with multiple versions",
    });

    var thisModelServing = new Databricks.ModelServing("this", new()
    {
        Name = "model-serving-endpoint",
        Config = new Databricks.Inputs.ModelServingConfigArgs
        {
            ServedModels = new[]
            {
                new Databricks.Inputs.ModelServingConfigServedModelArgs
                {
                    Name = "model_serving_prod",
                    ModelName = @this.Apply(@this => @this.Apply(getMlflowModelResult => getMlflowModelResult.Name)),
                    ModelVersion = @this.Apply(@this => @this.Apply(getMlflowModelResult => getMlflowModelResult.LatestVersions[0]?.Version)),
                    WorkloadSize = "Small",
                    ScaleToZeroEnabled = true,
                },
            },
        },
    });

});

package generated_program;

import com.pulumi.Context;
import com.pulumi.Pulumi;
import com.pulumi.core.Output;
import com.pulumi.databricks.DatabricksFunctions;
import com.pulumi.databricks.inputs.GetMlflowModelArgs;
import com.pulumi.databricks.ModelServing;
import com.pulumi.databricks.ModelServingArgs;
import com.pulumi.databricks.inputs.ModelServingConfigArgs;
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) {
        final var this = DatabricksFunctions.getMlflowModel(GetMlflowModelArgs.builder()
            .name("My MLflow Model with multiple versions")
            .build());

        var thisModelServing = new ModelServing("thisModelServing", ModelServingArgs.builder()        
            .name("model-serving-endpoint")
            .config(ModelServingConfigArgs.builder()
                .servedModels(ModelServingConfigServedModelArgs.builder()
                    .name("model_serving_prod")
                    .modelName(this_.name())
                    .modelVersion(this_.latestVersions()[0].version())
                    .workloadSize("Small")
                    .scaleToZeroEnabled(true)
                    .build())
                .build())
            .build());

    }
}

resources:
  thisModelServing:
    type: databricks:ModelServing
    name: this
    properties:
      name: model-serving-endpoint
      config:
        servedModels:
          - name: model_serving_prod
            modelName: ${this.name}
            modelVersion: ${this.latestVersions[0].version}
            workloadSize: Small
            scaleToZeroEnabled: true
variables:
  this:
    fn::invoke:
      Function: databricks:getMlflowModel
      Arguments:
        name: My MLflow Model with multiple versions

function getMlflowModel(args: GetMlflowModelArgs, opts?: InvokeOptions): Promise<GetMlflowModelResult>
function getMlflowModelOutput(args: GetMlflowModelOutputArgs, opts?: InvokeOptions): Output<GetMlflowModelResult>

def get_mlflow_model(description: Optional[str] = None,
                     latest_versions: Optional[Sequence[GetMlflowModelLatestVersion]] = None,
                     name: Optional[str] = None,
                     permission_level: Optional[str] = None,
                     tags: Optional[Sequence[GetMlflowModelTag]] = None,
                     user_id: Optional[str] = None,
                     opts: Optional[InvokeOptions] = None) -> GetMlflowModelResult
def get_mlflow_model_output(description: Optional[pulumi.Input[str]] = None,
                     latest_versions: Optional[pulumi.Input[Sequence[pulumi.Input[GetMlflowModelLatestVersionArgs]]]] = None,
                     name: Optional[pulumi.Input[str]] = None,
                     permission_level: Optional[pulumi.Input[str]] = None,
                     tags: Optional[pulumi.Input[Sequence[pulumi.Input[GetMlflowModelTagArgs]]]] = None,
                     user_id: Optional[pulumi.Input[str]] = None,
                     opts: Optional[InvokeOptions] = None) -> Output[GetMlflowModelResult]

func LookupMlflowModel(ctx *Context, args *LookupMlflowModelArgs, opts ...InvokeOption) (*LookupMlflowModelResult, error)
func LookupMlflowModelOutput(ctx *Context, args *LookupMlflowModelOutputArgs, opts ...InvokeOption) LookupMlflowModelResultOutput

public static class GetMlflowModel 
{
    public static Task<GetMlflowModelResult> InvokeAsync(GetMlflowModelArgs args, InvokeOptions? opts = null)
    public static Output<GetMlflowModelResult> Invoke(GetMlflowModelInvokeArgs args, InvokeOptions? opts = null)
}

public static CompletableFuture<GetMlflowModelResult> getMlflowModel(GetMlflowModelArgs args, InvokeOptions options)
// Output-based functions aren't available in Java yet

fn::invoke:
  function: databricks:index/getMlflowModel:getMlflowModel
  arguments:
    # arguments dictionary