MlflowTrackingServer class

Provides a SageMaker AI MLFlow Tracking Server resource.

Example Usage

Cognito Usage

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

const example = new aws.sagemaker.MlflowTrackingServer("example", {
    trackingServerName: "example",
    roleArn: exampleAwsIamRole.arn,
    artifactStoreUri: `s3://${exampleAwsS3Bucket.bucket}/path`,
});

import pulumi
import pulumi_aws as aws

example = aws.sagemaker.MlflowTrackingServer("example",
    tracking_server_name="example",
    role_arn=example_aws_iam_role["arn"],
    artifact_store_uri=f"s3://{example_aws_s3_bucket['bucket']}/path")

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

return await Deployment.RunAsync(() =>
{
    var example = new Aws.Sagemaker.MlflowTrackingServer("example", new()
    {
        TrackingServerName = "example",
        RoleArn = exampleAwsIamRole.Arn,
        ArtifactStoreUri = $"s3://{exampleAwsS3Bucket.Bucket}/path",
    });

});

package main

import (
	"github.com/pulumi/pulumi-aws/sdk/v7/go/aws/sagemaker"
	"github.com/pulumi/pulumi/sdk/v3/go/pulumi"
)

func main() {
	pulumi.Run(func(ctx *pulumi.Context) error {
		_, err := sagemaker.NewMlflowTrackingServer(ctx, "example", &sagemaker.MlflowTrackingServerArgs{
			TrackingServerName: pulumi.String("example"),
			RoleArn:            pulumi.Any(exampleAwsIamRole.Arn),
			ArtifactStoreUri:   pulumi.Sprintf("s3://%v/path", exampleAwsS3Bucket.Bucket),
		})
		if err != nil {
			return err
		}
		return nil
	})
}

pulumi {
  required_providers {
    aws = {
      source = "pulumi/aws"
    }
  }
}

resource "aws_sagemaker_mlflowtrackingserver" "example" {
  tracking_server_name = "example"
  role_arn             = exampleAwsIamRole.arn
  artifact_store_uri   ="s3://${exampleAwsS3Bucket.bucket}/path"
}

package generated_program;

import com.pulumi.Context;
import com.pulumi.Pulumi;
import com.pulumi.core.Output;
import com.pulumi.aws.sagemaker.MlflowTrackingServer;
import com.pulumi.aws.sagemaker.MlflowTrackingServerArgs;
import java.util.ArrayList;
import java.util.Arrays;
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 example = new MlflowTrackingServer("example", MlflowTrackingServerArgs.builder()
            .trackingServerName("example")
            .roleArn(exampleAwsIamRole.arn())
            .artifactStoreUri(String.format("s3://%s/path", exampleAwsS3Bucket.bucket()))
            .build());

    }
}

resources:
  example:
    type: aws:sagemaker:MlflowTrackingServer
    properties:
      trackingServerName: example
      roleArn: ${exampleAwsIamRole.arn}
      artifactStoreUri: s3://${exampleAwsS3Bucket.bucket}/path

Import

Using pulumi import, import SageMaker AI MLFlow Tracking Servers using the workteamName. For example:

$ pulumi import aws:sagemaker/mlflowTrackingServer:MlflowTrackingServer example example