Databricks v1.56.0, Nov 12 24

Databricks v1.56.0 published on Tuesday, Nov 12, 2024 by Pulumi

databricks.MlflowModel

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Databricks v1.56.0 published on Tuesday, Nov 12, 2024 by Pulumi

Example Usage

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

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

});

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()
            .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());

    }
}

resources:
  test:
    type: databricks:MlflowModel
    properties:
      name: My MLflow Model
      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.

Constructor 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=[{
        "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

In Python, inputs that are objects can be passed either as argument classes or as dictionary literals.

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 v1.56.0 published on Tuesday, Nov 12, 2024 by Pulumi

pulumi/pulumi-databricks

databricks.MlflowModel

On this page

On this page

Example Usage

Access Control

Create MlflowModel Resource

Constructor syntax

Parameters

Constructor example

MlflowModel Resource Properties

Inputs

Outputs

Look up Existing MlflowModel Resource

Supporting Types

MlflowModelTag, MlflowModelTagArgs

Import

Package Details

On this page

On this page

databricks.MlflowModel

On this page

On this page

Example Usage

Access Control

Related Resources

Create MlflowModel Resource

Constructor syntax

Parameters

Constructor example

MlflowModel Resource Properties

Inputs

Outputs

Look up Existing MlflowModel Resource

Supporting Types

MlflowModelTag, MlflowModelTagArgs

Import

Package Details

On this page

On this page