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 = '{{ region }}' 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 = '{{ region }}';

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 }}'
RETURNING
  ErrorCode,
  EventTime,
  Identifier,
  Operation,
  OperationStatus,
  RequestToken,
  ResourceModel,
  RetryAfter,
  StatusMessage,
  TypeName
;

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 }}'
RETURNING
  ErrorCode,
  EventTime,
  Identifier,
  Operation,
  OperationStatus,
  RequestToken,
  ResourceModel,
  RetryAfter,
  StatusMessage,
  TypeName
;

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 }}'
RETURNING
  ErrorCode,
  EventTime,
  Identifier,
  Operation,
  OperationStatus,
  RequestToken,
  ResourceModel,
  RetryAfter,
  StatusMessage,
  TypeName
;

DELETE example

/*+ delete */
DELETE FROM awscc.sagemaker.mlflow_tracking_servers
WHERE
  Identifier = '{{ tracking_server_name }}' AND
  region = '{{ region }}'
RETURNING
  ErrorCode,
  EventTime,
  Identifier,
  Operation,
  OperationStatus,
  RequestToken,
  ResourceModel,
  RetryAfter,
  StatusMessage,
  TypeName
;

Additional Parameters

Mutable resources in the Cloud Control provider support additional optional parameters which can be supplied with INSERT, UPDATE, or DELETE operations. These include:

Parameter	Description
ClientToken	A unique identifier to ensure the idempotency of the resource request. This allows the provider to accurately distinguish between retries and new requests. A client token is valid for 36 hours once used. After that, a resource request with the same client token is treated as a new request. If you do not specify a client token, one is generated for inclusion in the request.
RoleArn	The ARN of the IAM role used to perform this resource operation. The role specified must have the permissions required for this operation. If you do not specify a role, a temporary session is created using your AWS user credentials.
TypeVersionId	For private resource types, the type version to use in this resource operation. If you do not specify a resource version, the default version is used.

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