mlflow_tracking_servers

Creates, updates, deletes or gets a mlflow_tracking_server resource or lists mlflow_tracking_servers in a region

Overview

Name	mlflow_tracking_servers
Type	Resource
Description	Resource Type definition for AWS::SageMaker::MlflowTrackingServer
Id	awscc.sagemaker.mlflow_tracking_servers

Fields

get (all properties)

Name	Datatype	Description
tracking_server_name	string	The name of the MLFlow Tracking Server.
tracking_server_arn	string	The Amazon Resource Name (ARN) of the MLFlow Tracking Server.
tracking_server_size	string	The size of the MLFlow Tracking Server.
mlflow_version	string	The MLFlow Version used on the MLFlow Tracking Server.
role_arn	string	The Amazon Resource Name (ARN) of an IAM role that enables Amazon SageMaker to perform tasks on behalf of the customer.
artifact_store_uri	string	The Amazon S3 URI for MLFlow Tracking Server artifacts.
automatic_model_registration	boolean	A flag to enable Automatic SageMaker Model Registration.
weekly_maintenance_window_start	string	The start of the time window for maintenance of the MLFlow Tracking Server in UTC time.
▶tags	array	An array of key-value pairs to apply to this resource.
region	string	AWS region.

list (identifiers only)

Name	Datatype	Description
tracking_server_name	string	The name of the MLFlow Tracking Server.
region	string	AWS region.

For more information, see AWS::SageMaker::MlflowTrackingServer.

Methods

Name	Resource	Accessible by	Required Params
create_resource	mlflow_tracking_servers	INSERT	TrackingServerName, ArtifactStoreUri, RoleArn, region
delete_resource	mlflow_tracking_servers	DELETE	Identifier, region
update_resource	mlflow_tracking_servers	UPDATE	Identifier, PatchDocument, region
list_resources	mlflow_tracking_servers_list_only	SELECT	region
get_resource	mlflow_tracking_servers	SELECT	Identifier, region

SELECT examples

get (all properties)

Gets all properties from an individual mlflow_tracking_server.

SELECT
  region,
  tracking_server_name,
  tracking_server_arn,
  tracking_server_size,
  mlflow_version,
  role_arn,
  artifact_store_uri,
  automatic_model_registration,
  weekly_maintenance_window_start,
  tags
FROM awscc.sagemaker.mlflow_tracking_servers
WHERE
  region = 'us-east-1' AND
  Identifier = '{{ tracking_server_name }}';

list (identifiers only)

Lists all mlflow_tracking_servers in a region.

SELECT
  region,
  tracking_server_name
FROM awscc.sagemaker.mlflow_tracking_servers_list_only
WHERE
  region = 'us-east-1';

INSERT example

Use the following StackQL query and manifest file to create a new mlflow_tracking_server resource, using stack-deploy.

Required Properties

/*+ create */
INSERT INTO awscc.sagemaker.mlflow_tracking_servers (
  TrackingServerName,
  RoleArn,
  ArtifactStoreUri,
  region
)
SELECT
  '{{ tracking_server_name }}',
  '{{ role_arn }}',
  '{{ artifact_store_uri }}',
  '{{ region }}';

All Properties

/*+ create */
INSERT INTO awscc.sagemaker.mlflow_tracking_servers (
  TrackingServerName,
  TrackingServerSize,
  MlflowVersion,
  RoleArn,
  ArtifactStoreUri,
  AutomaticModelRegistration,
  WeeklyMaintenanceWindowStart,
  Tags,
  region
)
SELECT
  '{{ tracking_server_name }}',
  '{{ tracking_server_size }}',
  '{{ mlflow_version }}',
  '{{ role_arn }}',
  '{{ artifact_store_uri }}',
  '{{ automatic_model_registration }}',
  '{{ weekly_maintenance_window_start }}',
  '{{ tags }}',
  '{{ region }}';

Manifest

version: 1
name: stack name
description: stack description
providers:
  - aws
globals:
  - name: region
    value: '{{ vars.AWS_REGION }}'
resources:
  - name: mlflow_tracking_server
    props:
      - name: tracking_server_name
        value: '{{ tracking_server_name }}'
      - name: tracking_server_size
        value: '{{ tracking_server_size }}'
      - name: mlflow_version
        value: '{{ mlflow_version }}'
      - name: role_arn
        value: '{{ role_arn }}'
      - name: artifact_store_uri
        value: '{{ artifact_store_uri }}'
      - name: automatic_model_registration
        value: '{{ automatic_model_registration }}'
      - name: weekly_maintenance_window_start
        value: '{{ weekly_maintenance_window_start }}'
      - name: tags
        value:
          - value: '{{ value }}'
            key: '{{ key }}'

UPDATE example

Use the following StackQL query and manifest file to update a mlflow_tracking_server resource, using stack-deploy.

/*+ update */
UPDATE awscc.sagemaker.mlflow_tracking_servers
SET PatchDocument = string('{{ {
    "TrackingServerSize": tracking_server_size,
    "MlflowVersion": mlflow_version,
    "RoleArn": role_arn,
    "ArtifactStoreUri": artifact_store_uri,
    "AutomaticModelRegistration": automatic_model_registration,
    "WeeklyMaintenanceWindowStart": weekly_maintenance_window_start,
    "Tags": tags
} | generate_patch_document }}')
WHERE
  region = '{{ region }}' AND
  Identifier = '{{ tracking_server_name }}';

DELETE example

/*+ delete */
DELETE FROM awscc.sagemaker.mlflow_tracking_servers
WHERE
  Identifier = '{{ tracking_server_name }}' AND
  region = 'us-east-1';

Permissions

To operate on the mlflow_tracking_servers resource, the following permissions are required:

Create

sagemaker:CreateMlflowTrackingServer,
sagemaker:DescribeMlflowTrackingServer,
sagemaker:AddTags,
sagemaker:ListTags,
iam:PassRole

Read

sagemaker:DescribeMlflowTrackingServer,
sagemaker:ListTags

Update

sagemaker:UpdateMlflowTrackingServer,
sagemaker:DescribeMlflowTrackingServer,
sagemaker:ListTags,
sagemaker:AddTags,
sagemaker:DeleteTags,
iam:PassRole

Delete

sagemaker:DeleteMlflowTrackingServer,
sagemaker:DescribeMlflowTrackingServer

List

sagemaker:ListMlflowTrackingServers