Docs Packages
  1. Packages
  2. Databricks
  3. API Docs
  4. MlflowModel
Databricks v1.35.0 published on Friday, Mar 29, 2024 by Pulumi
pulumi/pulumi-databricks

databricks.MlflowModel

Explore with Pulumi AI

databricks logo
Databricks v1.35.0 published on Friday, Mar 29, 2024 by Pulumi
pulumi/pulumi-databricks

    This resource allows you to create MLflow models in Databricks.

    Note This documentation covers the Workspace Model Registry. Databricks recommends using Models in Unity Catalog. Models in Unity Catalog provides centralized model governance, cross-workspace access, lineage, and deployment.

    Example Usage

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

const test = new databricks.MlflowModel("test", {
    description: "My MLflow model description",
    tags: [
        {
            key: "key1",
            value: "value1",
        },
        {
            key: "key2",
            value: "value2",
        },
    ],
});

    import pulumi
import pulumi_databricks as databricks

test = databricks.MlflowModel("test",
    description="My MLflow model description",
    tags=[
        databricks.MlflowModelTagArgs(
            key="key1",
            value="value1",
        ),
        databricks.MlflowModelTagArgs(
            key="key2",
            value="value2",
        ),
    ])

    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, "test", &databricks.MlflowModelArgs{
			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
		}
		return nil
	})
}

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

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

});

    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 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 test = new MlflowModel("test", MlflowModelArgs.builder()        
            .description("My MLflow model description")
            .tags(            
                MlflowModelTagArgs.builder()
                    .key("key1")
                    .value("value1")
                    .build(),
                MlflowModelTagArgs.builder()
                    .key("key2")
                    .value("value2")
                    .build())
            .build());

    }
}

    resources:
  test:
    type: databricks:MlflowModel
    properties:
      description: My MLflow model description
      tags:
        - key: key1
          value: value1
        - key: key2
          value: value2

    Access Control

    • databricks.Permissions can control which groups or individual users can Read, Edit, Manage Staging Versions, Manage Production Versions, and Manage individual models.

    The following resources are often used in the same context:

    • databricks.RegisteredModel to create Models in Unity Catalog in Databricks.
    • End to end workspace management guide.
    • databricks.ModelServing to serve this model on a Databricks serving endpoint.
    • databricks.Directory to manage directories in Databricks Workspace.
    • databricks.MlflowExperiment to manage MLflow experiments in Databricks.
    • databricks.Notebook to manage Databricks Notebooks.
    • databricks.Notebook data to export a notebook from Databricks Workspace.
    • databricks.Repo to manage Databricks Repos.

    Create MlflowModel Resource

    Resources are created with functions called constructors. To learn more about declaring and configuring resources, see Resources.

    Constructor syntax

    new MlflowModel(name: string, args?: MlflowModelArgs, opts?: CustomResourceOptions);
    @overload
def MlflowModel(resource_name: str,
                args: Optional[MlflowModelArgs] = None,
                opts: Optional[ResourceOptions] = None)

@overload
def MlflowModel(resource_name: str,
                opts: Optional[ResourceOptions] = None,
                description: Optional[str] = None,
                name: Optional[str] = None,
                tags: Optional[Sequence[MlflowModelTagArgs]] = None)
    func NewMlflowModel(ctx *Context, name string, args *MlflowModelArgs, opts ...ResourceOption) (*MlflowModel, error)
    public MlflowModel(string name, MlflowModelArgs? args = null, CustomResourceOptions? opts = null)
    public MlflowModel(String name, MlflowModelArgs args)
public MlflowModel(String name, MlflowModelArgs args, CustomResourceOptions options)

    type: databricks:MlflowModel
properties: # The arguments to resource properties.
options: # Bag of options to control resource's behavior.

    Parameters

    name string
    The unique name of the resource.
    args MlflowModelArgs
    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 MlflowModelArgs
    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 MlflowModelArgs
    The arguments to resource properties.
    opts ResourceOption
    Bag of options to control resource's behavior.
    name string
    The unique name of the resource.
    args MlflowModelArgs
    The arguments to resource properties.
    opts CustomResourceOptions
    Bag of options to control resource's behavior.
    name String
    The unique name of the resource.
    args MlflowModelArgs
    The arguments to resource properties.
    options CustomResourceOptions
    Bag of options to control resource's behavior.

    Example

    The following reference example uses placeholder values for all input properties.

    var mlflowModelResource = new Databricks.MlflowModel("mlflowModelResource", new()
{
    Description = "string",
    Name = "string",
    Tags = new[]
    {
        new Databricks.Inputs.MlflowModelTagArgs
        {
            Key = "string",
            Value = "string",
        },
    },
});

    example, err := databricks.NewMlflowModel(ctx, "mlflowModelResource", &databricks.MlflowModelArgs{
	Description: pulumi.String("string"),
	Name:        pulumi.String("string"),
	Tags: databricks.MlflowModelTagArray{
		&databricks.MlflowModelTagArgs{
			Key:   pulumi.String("string"),
			Value: pulumi.String("string"),
		},
	},
})

    var mlflowModelResource = new MlflowModel("mlflowModelResource", MlflowModelArgs.builder()        
    .description("string")
    .name("string")
    .tags(MlflowModelTagArgs.builder()
        .key("string")
        .value("string")
        .build())
    .build());

    mlflow_model_resource = databricks.MlflowModel("mlflowModelResource",
    description="string",
    name="string",
    tags=[databricks.MlflowModelTagArgs(
        key="string",
        value="string",
    )])

    const mlflowModelResource = new databricks.MlflowModel("mlflowModelResource", {
    description: "string",
    name: "string",
    tags: [{
        key: "string",
        value: "string",
    }],
});

    type: databricks:MlflowModel
properties:
    description: string
    name: string
    tags:
        - key: string
          value: string

    MlflowModel Resource Properties

    To learn more about resource properties and how to use them, see Inputs and Outputs in the Architecture and Concepts docs.

    Inputs

    The MlflowModel resource accepts the following input properties:

    Description string
    The description of the MLflow model.
    Name string
    Name of MLflow model. Change of name triggers new resource.
    Tags List<MlflowModelTag>
    Tags for the MLflow model.
    Description string
    The description of the MLflow model.
    Name string
    Name of MLflow model. Change of name triggers new resource.
    Tags []MlflowModelTagArgs
    Tags for the MLflow model.
    description String
    The description of the MLflow model.
    name String
    Name of MLflow model. Change of name triggers new resource.
    tags List<MlflowModelTag>
    Tags for the MLflow model.
    description string
    The description of the MLflow model.
    name string
    Name of MLflow model. Change of name triggers new resource.
    tags MlflowModelTag[]
    Tags for the MLflow model.
    description str
    The description of the MLflow model.
    name str
    Name of MLflow model. Change of name triggers new resource.
    tags Sequence[MlflowModelTagArgs]
    Tags for the MLflow model.
    description String
    The description of the MLflow model.
    name String
    Name of MLflow model. Change of name triggers new resource.
    tags List<Property Map>
    Tags for the MLflow model.

    Outputs

    All input properties are implicitly available as output properties. Additionally, the MlflowModel resource produces the following output properties:

    Id string
    The provider-assigned unique ID for this managed resource.
    RegisteredModelId string
    Id string
    The provider-assigned unique ID for this managed resource.
    RegisteredModelId string
    id String
    The provider-assigned unique ID for this managed resource.
    registeredModelId String
    id string
    The provider-assigned unique ID for this managed resource.
    registeredModelId string
    id str
    The provider-assigned unique ID for this managed resource.
    registered_model_id str
    id String
    The provider-assigned unique ID for this managed resource.
    registeredModelId String

    Look up Existing MlflowModel Resource

    Get an existing MlflowModel 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?: MlflowModelState, opts?: CustomResourceOptions): MlflowModel
    @staticmethod
def get(resource_name: str,
        id: str,
        opts: Optional[ResourceOptions] = None,
        description: Optional[str] = None,
        name: Optional[str] = None,
        registered_model_id: Optional[str] = None,
        tags: Optional[Sequence[MlflowModelTagArgs]] = None) -> MlflowModel
    func GetMlflowModel(ctx *Context, name string, id IDInput, state *MlflowModelState, opts ...ResourceOption) (*MlflowModel, error)
    public static MlflowModel Get(string name, Input<string> id, MlflowModelState? state, CustomResourceOptions? opts = null)
    public static MlflowModel get(String name, Output<String> id, MlflowModelState state, CustomResourceOptions options)
    Resource lookup is not supported in YAML
    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.
    The following state arguments are supported:
    Description string
    The description of the MLflow model.
    Name string
    Name of MLflow model. Change of name triggers new resource.
    RegisteredModelId string
    Tags List<MlflowModelTag>
    Tags for the MLflow model.
    Description string
    The description of the MLflow model.
    Name string
    Name of MLflow model. Change of name triggers new resource.
    RegisteredModelId string
    Tags []MlflowModelTagArgs
    Tags for the MLflow model.
    description String
    The description of the MLflow model.
    name String
    Name of MLflow model. Change of name triggers new resource.
    registeredModelId String
    tags List<MlflowModelTag>
    Tags for the MLflow model.
    description string
    The description of the MLflow model.
    name string
    Name of MLflow model. Change of name triggers new resource.
    registeredModelId string
    tags MlflowModelTag[]
    Tags for the MLflow model.
    description str
    The description of the MLflow model.
    name str
    Name of MLflow model. Change of name triggers new resource.
    registered_model_id str
    tags Sequence[MlflowModelTagArgs]
    Tags for the MLflow model.
    description String
    The description of the MLflow model.
    name String
    Name of MLflow model. Change of name triggers new resource.
    registeredModelId String
    tags List<Property Map>
    Tags for the MLflow model.

    Supporting Types

    MlflowModelTag, MlflowModelTagArgs

    Key string
    Value string
    Key string
    Value string
    key String
    value String
    key string
    value string
    key str
    value str
    key String
    value String

    Import

    The model resource can be imported using the name

    bash

    $ pulumi import databricks:index/mlflowModel:MlflowModel this <name>

    To learn more about importing existing cloud resources, see Importing resources.

    Package Details

    Repository
    databricks pulumi/pulumi-databricks
    License
    Apache-2.0
    Notes
    This Pulumi package is based on the databricks Terraform Provider.
    databricks logo
    Databricks v1.35.0 published on Friday, Mar 29, 2024 by Pulumi
    pulumi/pulumi-databricks