MlflowTrackingServer

class MlflowTrackingServer : KotlinCustomResource

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 (
"fmt"
"github.com/pulumi/pulumi-aws/sdk/v6/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
})
}
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.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 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 workteam_name. For example:

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

Properties

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val arn: Output<String>

The Amazon Resource Name (ARN) assigned by AWS to this MLFlow Tracking Server.

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The S3 URI for a general purpose bucket to use as the MLflow Tracking Server artifact store.

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A list of Member Definitions that contains objects that identify the workers that make up the work team.

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val id: Output<String>
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val mlflowVersion: Output<String>

The version of MLflow that the tracking server uses. To see which MLflow versions are available to use, see How it works.

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val pulumiChildResources: Set<KotlinResource>
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val roleArn: Output<String>

The Amazon Resource Name (ARN) for an IAM role in your account that the MLflow Tracking Server uses to access the artifact store in Amazon S3. The role should have AmazonS3FullAccess permissions. For more information on IAM permissions for tracking server creation, see Set up IAM permissions for MLflow.

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val tags: Output<Map<String, String>>?

A map of tags to assign to the resource. If configured with a provider default_tags configuration block present, tags with matching keys will overwrite those defined at the provider-level.

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val tagsAll: Output<Map<String, String>>

A map of tags assigned to the resource, including those inherited from the provider default_tags configuration block.

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A unique string identifying the tracking server name. This string is part of the tracking server ARN.

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The size of the tracking server you want to create. You can choose between "Small", "Medium", and "Large". The default MLflow Tracking Server configuration size is "Small". You can choose a size depending on the projected use of the tracking server such as the volume of data logged, number of users, and frequency of use.

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The URL to connect to the MLflow user interface for the described tracking server.

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val urn: Output<String>
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The day and time of the week in Coordinated Universal Time (UTC) 24-hour standard time that weekly maintenance updates are scheduled. For example: TUE:03:30.