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Databricks v1.56.0 published on Tuesday, Nov 12, 2024 by Pulumi
pulumi/pulumi-databricks

databricks.MlflowExperiment

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Databricks v1.56.0 published on Tuesday, Nov 12, 2024 by Pulumi
pulumi/pulumi-databricks

    This resource allows you to manage MLflow experiments in Databricks.

    Example Usage

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

const me = databricks.getCurrentUser({});
const _this = new databricks.MlflowExperiment("this", {
    name: me.then(me => `${me.home}/Sample`),
    artifactLocation: "dbfs:/tmp/my-experiment",
    description: "My MLflow experiment description",
});

    import pulumi
import pulumi_databricks as databricks

me = databricks.get_current_user()
this = databricks.MlflowExperiment("this",
    name=f"{me.home}/Sample",
    artifact_location="dbfs:/tmp/my-experiment",
    description="My MLflow experiment description")

    package main

import (
	"fmt"

	"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 {
		me, err := databricks.GetCurrentUser(ctx, map[string]interface{}{}, nil)
		if err != nil {
			return err
		}
		_, err = databricks.NewMlflowExperiment(ctx, "this", &databricks.MlflowExperimentArgs{
			Name:             pulumi.Sprintf("%v/Sample", me.Home),
			ArtifactLocation: pulumi.String("dbfs:/tmp/my-experiment"),
			Description:      pulumi.String("My MLflow experiment description"),
		})
		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 me = Databricks.GetCurrentUser.Invoke();

    var @this = new Databricks.MlflowExperiment("this", new()
    {
        Name = $"{me.Apply(getCurrentUserResult => getCurrentUserResult.Home)}/Sample",
        ArtifactLocation = "dbfs:/tmp/my-experiment",
        Description = "My MLflow experiment description",
    });

});

    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.MlflowExperiment;
import com.pulumi.databricks.MlflowExperimentArgs;
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 me = DatabricksFunctions.getCurrentUser();

        var this_ = new MlflowExperiment("this", MlflowExperimentArgs.builder()
            .name(String.format("%s/Sample", me.applyValue(getCurrentUserResult -> getCurrentUserResult.home())))
            .artifactLocation("dbfs:/tmp/my-experiment")
            .description("My MLflow experiment description")
            .build());

    }
}

    resources:
  this:
    type: databricks:MlflowExperiment
    properties:
      name: ${me.home}/Sample
      artifactLocation: dbfs:/tmp/my-experiment
      description: My MLflow experiment description
variables:
  me:
    fn::invoke:
      Function: databricks:getCurrentUser
      Arguments: {}

    Access Control

    • databricks.Permissions can control which groups or individual users can Read, Edit, or Manage individual experiments.

    The following resources are often used in the same context:

    Create MlflowExperiment Resource

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

    Constructor syntax

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

@overload
def MlflowExperiment(resource_name: str,
                     opts: Optional[ResourceOptions] = None,
                     artifact_location: Optional[str] = None,
                     creation_time: Optional[int] = None,
                     description: Optional[str] = None,
                     experiment_id: Optional[str] = None,
                     last_update_time: Optional[int] = None,
                     lifecycle_stage: Optional[str] = None,
                     name: Optional[str] = None)
    func NewMlflowExperiment(ctx *Context, name string, args *MlflowExperimentArgs, opts ...ResourceOption) (*MlflowExperiment, error)
    public MlflowExperiment(string name, MlflowExperimentArgs? args = null, CustomResourceOptions? opts = null)
    public MlflowExperiment(String name, MlflowExperimentArgs args)
public MlflowExperiment(String name, MlflowExperimentArgs args, CustomResourceOptions options)

    type: databricks:MlflowExperiment
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 MlflowExperimentArgs
    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 MlflowExperimentArgs
    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 MlflowExperimentArgs
    The arguments to resource properties.
    opts ResourceOption
    Bag of options to control resource's behavior.
    name string
    The unique name of the resource.
    args MlflowExperimentArgs
    The arguments to resource properties.
    opts CustomResourceOptions
    Bag of options to control resource's behavior.
    name String
    The unique name of the resource.
    args MlflowExperimentArgs
    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 mlflowExperimentResource = new Databricks.MlflowExperiment("mlflowExperimentResource", new()
{
    ArtifactLocation = "string",
    CreationTime = 0,
    Description = "string",
    ExperimentId = "string",
    LastUpdateTime = 0,
    LifecycleStage = "string",
    Name = "string",
});

    example, err := databricks.NewMlflowExperiment(ctx, "mlflowExperimentResource", &databricks.MlflowExperimentArgs{
	ArtifactLocation: pulumi.String("string"),
	CreationTime:     pulumi.Int(0),
	Description:      pulumi.String("string"),
	ExperimentId:     pulumi.String("string"),
	LastUpdateTime:   pulumi.Int(0),
	LifecycleStage:   pulumi.String("string"),
	Name:             pulumi.String("string"),
})

    var mlflowExperimentResource = new MlflowExperiment("mlflowExperimentResource", MlflowExperimentArgs.builder()
    .artifactLocation("string")
    .creationTime(0)
    .description("string")
    .experimentId("string")
    .lastUpdateTime(0)
    .lifecycleStage("string")
    .name("string")
    .build());

    mlflow_experiment_resource = databricks.MlflowExperiment("mlflowExperimentResource",
    artifact_location="string",
    creation_time=0,
    description="string",
    experiment_id="string",
    last_update_time=0,
    lifecycle_stage="string",
    name="string")

    const mlflowExperimentResource = new databricks.MlflowExperiment("mlflowExperimentResource", {
    artifactLocation: "string",
    creationTime: 0,
    description: "string",
    experimentId: "string",
    lastUpdateTime: 0,
    lifecycleStage: "string",
    name: "string",
});

    type: databricks:MlflowExperiment
properties:
    artifactLocation: string
    creationTime: 0
    description: string
    experimentId: string
    lastUpdateTime: 0
    lifecycleStage: string
    name: string

    MlflowExperiment 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 MlflowExperiment resource accepts the following input properties:

    ArtifactLocation string
    Path to dbfs:/ or s3:// artifact location of the MLflow experiment.
    CreationTime int
    Description string
    The description of the MLflow experiment.
    ExperimentId string
    LastUpdateTime int
    LifecycleStage string
    Name string
    Name of MLflow experiment. It must be an absolute path within the Databricks workspace, e.g. /Users/<some-username>/my-experiment. For more information about changes to experiment naming conventions, see mlflow docs.
    ArtifactLocation string
    Path to dbfs:/ or s3:// artifact location of the MLflow experiment.
    CreationTime int
    Description string
    The description of the MLflow experiment.
    ExperimentId string
    LastUpdateTime int
    LifecycleStage string
    Name string
    Name of MLflow experiment. It must be an absolute path within the Databricks workspace, e.g. /Users/<some-username>/my-experiment. For more information about changes to experiment naming conventions, see mlflow docs.
    artifactLocation String
    Path to dbfs:/ or s3:// artifact location of the MLflow experiment.
    creationTime Integer
    description String
    The description of the MLflow experiment.
    experimentId String
    lastUpdateTime Integer
    lifecycleStage String
    name String
    Name of MLflow experiment. It must be an absolute path within the Databricks workspace, e.g. /Users/<some-username>/my-experiment. For more information about changes to experiment naming conventions, see mlflow docs.
    artifactLocation string
    Path to dbfs:/ or s3:// artifact location of the MLflow experiment.
    creationTime number
    description string
    The description of the MLflow experiment.
    experimentId string
    lastUpdateTime number
    lifecycleStage string
    name string
    Name of MLflow experiment. It must be an absolute path within the Databricks workspace, e.g. /Users/<some-username>/my-experiment. For more information about changes to experiment naming conventions, see mlflow docs.
    artifact_location str
    Path to dbfs:/ or s3:// artifact location of the MLflow experiment.
    creation_time int
    description str
    The description of the MLflow experiment.
    experiment_id str
    last_update_time int
    lifecycle_stage str
    name str
    Name of MLflow experiment. It must be an absolute path within the Databricks workspace, e.g. /Users/<some-username>/my-experiment. For more information about changes to experiment naming conventions, see mlflow docs.
    artifactLocation String
    Path to dbfs:/ or s3:// artifact location of the MLflow experiment.
    creationTime Number
    description String
    The description of the MLflow experiment.
    experimentId String
    lastUpdateTime Number
    lifecycleStage String
    name String
    Name of MLflow experiment. It must be an absolute path within the Databricks workspace, e.g. /Users/<some-username>/my-experiment. For more information about changes to experiment naming conventions, see mlflow docs.

    Outputs

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

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

    Look up Existing MlflowExperiment Resource

    Get an existing MlflowExperiment 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?: MlflowExperimentState, opts?: CustomResourceOptions): MlflowExperiment
    @staticmethod
def get(resource_name: str,
        id: str,
        opts: Optional[ResourceOptions] = None,
        artifact_location: Optional[str] = None,
        creation_time: Optional[int] = None,
        description: Optional[str] = None,
        experiment_id: Optional[str] = None,
        last_update_time: Optional[int] = None,
        lifecycle_stage: Optional[str] = None,
        name: Optional[str] = None) -> MlflowExperiment
    func GetMlflowExperiment(ctx *Context, name string, id IDInput, state *MlflowExperimentState, opts ...ResourceOption) (*MlflowExperiment, error)
    public static MlflowExperiment Get(string name, Input<string> id, MlflowExperimentState? state, CustomResourceOptions? opts = null)
    public static MlflowExperiment get(String name, Output<String> id, MlflowExperimentState 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:
    ArtifactLocation string
    Path to dbfs:/ or s3:// artifact location of the MLflow experiment.
    CreationTime int
    Description string
    The description of the MLflow experiment.
    ExperimentId string
    LastUpdateTime int
    LifecycleStage string
    Name string
    Name of MLflow experiment. It must be an absolute path within the Databricks workspace, e.g. /Users/<some-username>/my-experiment. For more information about changes to experiment naming conventions, see mlflow docs.
    ArtifactLocation string
    Path to dbfs:/ or s3:// artifact location of the MLflow experiment.
    CreationTime int
    Description string
    The description of the MLflow experiment.
    ExperimentId string
    LastUpdateTime int
    LifecycleStage string
    Name string
    Name of MLflow experiment. It must be an absolute path within the Databricks workspace, e.g. /Users/<some-username>/my-experiment. For more information about changes to experiment naming conventions, see mlflow docs.
    artifactLocation String
    Path to dbfs:/ or s3:// artifact location of the MLflow experiment.
    creationTime Integer
    description String
    The description of the MLflow experiment.
    experimentId String
    lastUpdateTime Integer
    lifecycleStage String
    name String
    Name of MLflow experiment. It must be an absolute path within the Databricks workspace, e.g. /Users/<some-username>/my-experiment. For more information about changes to experiment naming conventions, see mlflow docs.
    artifactLocation string
    Path to dbfs:/ or s3:// artifact location of the MLflow experiment.
    creationTime number
    description string
    The description of the MLflow experiment.
    experimentId string
    lastUpdateTime number
    lifecycleStage string
    name string
    Name of MLflow experiment. It must be an absolute path within the Databricks workspace, e.g. /Users/<some-username>/my-experiment. For more information about changes to experiment naming conventions, see mlflow docs.
    artifact_location str
    Path to dbfs:/ or s3:// artifact location of the MLflow experiment.
    creation_time int
    description str
    The description of the MLflow experiment.
    experiment_id str
    last_update_time int
    lifecycle_stage str
    name str
    Name of MLflow experiment. It must be an absolute path within the Databricks workspace, e.g. /Users/<some-username>/my-experiment. For more information about changes to experiment naming conventions, see mlflow docs.
    artifactLocation String
    Path to dbfs:/ or s3:// artifact location of the MLflow experiment.
    creationTime Number
    description String
    The description of the MLflow experiment.
    experimentId String
    lastUpdateTime Number
    lifecycleStage String
    name String
    Name of MLflow experiment. It must be an absolute path within the Databricks workspace, e.g. /Users/<some-username>/my-experiment. For more information about changes to experiment naming conventions, see mlflow docs.

    Import

    The experiment resource can be imported using the id of the experiment

    bash

    $ pulumi import databricks:index/mlflowExperiment:MlflowExperiment this <experiment-id>

    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 v1.56.0 published on Tuesday, Nov 12, 2024 by Pulumi
    pulumi/pulumi-databricks