The desired number of streaming instances.
" + }, + "DesiredSessions":{ + "shape":"Integer", + "documentation":"The desired number of user sessions for a multi-session fleet. This is not allowed for single-session fleets.
When you create a fleet, you must set either the DesiredSessions or DesiredInstances attribute, based on the type of fleet you create. You can’t define both attributes or leave both attributes blank.
" } }, "documentation":"Describes the capacity for a fleet.
" @@ -1810,6 +1813,22 @@ "Available":{ "shape":"Integer", "documentation":"The number of currently available instances that can be used to stream sessions.
" + }, + "DesiredUserSessions":{ + "shape":"Integer", + "documentation":"The total number of sessions slots that are either running or pending. This represents the total number of concurrent streaming sessions your fleet can support in a steady state.
DesiredUserSessionCapacity = ActualUserSessionCapacity + PendingUserSessionCapacity
This only applies to multi-session fleets.
" + }, + "AvailableUserSessions":{ + "shape":"Integer", + "documentation":"The number of idle session slots currently available for user sessions.
AvailableUserSessionCapacity = ActualUserSessionCapacity - ActiveUserSessions
This only applies to multi-session fleets.
" + }, + "ActiveUserSessions":{ + "shape":"Integer", + "documentation":"The number of user sessions currently being used for streaming sessions. This only applies to multi-session fleets.
" + }, + "ActualUserSessions":{ + "shape":"Integer", + "documentation":"The total number of session slots that are available for streaming or are currently streaming.
ActualUserSessionCapacity = AvailableUserSessionCapacity + ActiveUserSessions
This only applies to multi-session fleets.
" } }, "documentation":"Describes the capacity status for a fleet.
" @@ -2165,7 +2184,7 @@ }, "MaxUserDurationInSeconds":{ "shape":"Integer", - "documentation":"The maximum amount of time that a streaming session can remain active, in seconds. If users are still connected to a streaming instance five minutes before this limit is reached, they are prompted to save any open documents before being disconnected. After this time elapses, the instance is terminated and replaced by a new instance.
Specify a value between 600 and 360000.
" + "documentation":"The maximum amount of time that a streaming session can remain active, in seconds. If users are still connected to a streaming instance five minutes before this limit is reached, they are prompted to save any open documents before being disconnected. After this time elapses, the instance is terminated and replaced by a new instance.
Specify a value between 600 and 432000.
" }, "DisconnectTimeoutInSeconds":{ "shape":"Integer", @@ -2218,6 +2237,10 @@ "SessionScriptS3Location":{ "shape":"S3Location", "documentation":"The S3 location of the session scripts configuration zip file. This only applies to Elastic fleets.
" + }, + "MaxSessionsPerInstance":{ + "shape":"Integer", + "documentation":"The maximum number of user sessions on an instance. This only applies to multi-session fleets.
" } } }, @@ -3106,11 +3129,11 @@ ], "members":{ "StackName":{ - "shape":"String", + "shape":"Name", "documentation":"The name of the stack. This value is case-sensitive.
" }, "FleetName":{ - "shape":"String", + "shape":"Name", "documentation":"The name of the fleet. This value is case-sensitive.
" }, "UserId":{ @@ -3128,6 +3151,10 @@ "AuthenticationType":{ "shape":"AuthenticationType", "documentation":"The authentication method. Specify API for a user authenticated using a streaming URL or SAML for a SAML federated user. The default is to authenticate users using a streaming URL.
The identifier for the instance hosting the session.
" } } }, @@ -3727,6 +3754,10 @@ "SessionScriptS3Location":{ "shape":"S3Location", "documentation":"The S3 location of the session scripts configuration zip file. This only applies to Elastic fleets.
" + }, + "MaxSessionsPerInstance":{ + "shape":"Integer", + "documentation":"The maximum number of user sessions on an instance. This only applies to multi-session fleets.
" } }, "documentation":"Describes a fleet.
" @@ -3740,7 +3771,8 @@ "DOMAIN_JOIN_INFO", "IAM_ROLE_ARN", "USB_DEVICE_FILTER_STRINGS", - "SESSION_SCRIPT_S3_LOCATION" + "SESSION_SCRIPT_S3_LOCATION", + "MAX_SESSIONS_PER_INSTANCE" ] }, "FleetAttributes":{ @@ -4528,6 +4560,10 @@ "NetworkAccessConfiguration":{ "shape":"NetworkAccessConfiguration", "documentation":"The network details for the streaming session.
" + }, + "InstanceId":{ + "shape":"String", + "documentation":"The identifier for the instance hosting the session.
" } }, "documentation":"Describes a streaming session.
" @@ -5118,7 +5154,7 @@ "documentation":"The ARN of the public, private, or shared image to use.
" }, "Name":{ - "shape":"String", + "shape":"Name", "documentation":"A unique name for the fleet.
" }, "InstanceType":{ @@ -5193,6 +5229,10 @@ "SessionScriptS3Location":{ "shape":"S3Location", "documentation":"The S3 location of the session scripts configuration zip file. This only applies to Elastic fleets.
" + }, + "MaxSessionsPerInstance":{ + "shape":"Integer", + "documentation":"The maximum number of user sessions on an instance. This only applies to multi-session fleets.
" } } }, diff --git a/botocore/data/appstream/2016-12-01/waiters-2.json b/botocore/data/appstream/2016-12-01/waiters-2.json index 1c8dea0ded..f53f609cb7 100644 --- a/botocore/data/appstream/2016-12-01/waiters-2.json +++ b/botocore/data/appstream/2016-12-01/waiters-2.json @@ -10,19 +10,19 @@ "state": "success", "matcher": "pathAll", "argument": "Fleets[].State", - "expected": "RUNNING" + "expected": "ACTIVE" }, { "state": "failure", "matcher": "pathAny", "argument": "Fleets[].State", - "expected": "STOPPING" + "expected": "PENDING_DEACTIVATE" }, { "state": "failure", "matcher": "pathAny", "argument": "Fleets[].State", - "expected": "STOPPED" + "expected": "INACTIVE" } ] }, @@ -35,19 +35,19 @@ "state": "success", "matcher": "pathAll", "argument": "Fleets[].State", - "expected": "STOPPED" + "expected": "INACTIVE" }, { "state": "failure", "matcher": "pathAny", "argument": "Fleets[].State", - "expected": "STARTING" + "expected": "PENDING_ACTIVATE" }, { "state": "failure", "matcher": "pathAny", "argument": "Fleets[].State", - "expected": "RUNNING" + "expected": "ACTIVE" } ] } diff --git a/botocore/data/cloudformation/2010-05-15/service-2.json b/botocore/data/cloudformation/2010-05-15/service-2.json index 7fe1b64932..df22b12194 100644 --- a/botocore/data/cloudformation/2010-05-15/service-2.json +++ b/botocore/data/cloudformation/2010-05-15/service-2.json @@ -1862,7 +1862,7 @@ }, "RetainExceptOnCreate":{ "shape":"RetainExceptOnCreate", - "documentation":"This deletion policy deletes newly created resources, but retains existing resources, when a stack operation is rolled back. This ensures new, empty, and unused resources are deleted, while critical resources and their data are retained. RetainExceptOnCreate can be specified for any resource that supports the DeletionPolicy attribute.
When set to true, newly created resources are deleted when the operation rolls back. This includes newly created resources marked with a deletion policy of Retain.
Default: false
The input for CreateStack action.
" @@ -1958,7 +1958,7 @@ }, "Capabilities":{ "shape":"Capabilities", - "documentation":"In some cases, you must explicitly acknowledge that your stack set template contains certain capabilities in order for CloudFormation to create the stack set and related stack instances.
CAPABILITY_IAM and CAPABILITY_NAMED_IAM
Some stack templates might include resources that can affect permissions in your Amazon Web Services account; for example, by creating new Identity and Access Management (IAM) users. For those stack sets, you must explicitly acknowledge this by specifying one of these capabilities.
The following IAM resources require you to specify either the CAPABILITY_IAM or CAPABILITY_NAMED_IAM capability.
If you have IAM resources, you can specify either capability.
If you have IAM resources with custom names, you must specify CAPABILITY_NAMED_IAM.
If you don't specify either of these capabilities, CloudFormation returns an InsufficientCapabilities error.
If your stack template contains these resources, we recommend that you review all permissions associated with them and edit their permissions if necessary.
For more information, see Acknowledging IAM Resources in CloudFormation Templates.
CAPABILITY_AUTO_EXPAND
Some templates reference macros. If your stack set template references one or more macros, you must create the stack set directly from the processed template, without first reviewing the resulting changes in a change set. To create the stack set directly, you must acknowledge this capability. For more information, see Using CloudFormation Macros to Perform Custom Processing on Templates.
Stack sets with service-managed permissions don't currently support the use of macros in templates. (This includes the AWS::Include and AWS::Serverless transforms, which are macros hosted by CloudFormation.) Even if you specify this capability for a stack set with service-managed permissions, if you reference a macro in your template the stack set operation will fail.
In some cases, you must explicitly acknowledge that your stack set template contains certain capabilities in order for CloudFormation to create the stack set and related stack instances.
CAPABILITY_IAM and CAPABILITY_NAMED_IAM
Some stack templates might include resources that can affect permissions in your Amazon Web Services account; for example, by creating new Identity and Access Management (IAM) users. For those stack sets, you must explicitly acknowledge this by specifying one of these capabilities.
The following IAM resources require you to specify either the CAPABILITY_IAM or CAPABILITY_NAMED_IAM capability.
If you have IAM resources, you can specify either capability.
If you have IAM resources with custom names, you must specify CAPABILITY_NAMED_IAM.
If you don't specify either of these capabilities, CloudFormation returns an InsufficientCapabilities error.
If your stack template contains these resources, we recommend that you review all permissions associated with them and edit their permissions if necessary.
For more information, see Acknowledging IAM Resources in CloudFormation Templates.
CAPABILITY_AUTO_EXPAND
Some templates reference macros. If your stack set template references one or more macros, you must create the stack set directly from the processed template, without first reviewing the resulting changes in a change set. To create the stack set directly, you must acknowledge this capability. For more information, see Using CloudFormation Macros to Perform Custom Processing on Templates.
Stack sets with service-managed permissions don't currently support the use of macros in templates. (This includes the AWS::Include and AWS::Serverless transforms, which are macros hosted by CloudFormation.) Even if you specify this capability for a stack set with service-managed permissions, if you reference a macro in your template the stack set operation will fail.
The Amazon Resource Name (ARN) of the IAM role to use to create this stack set.
Specify an IAM role only if you are using customized administrator roles to control which users or groups can manage specific stack sets within the same administrator account. For more information, see Prerequisites: Granting Permissions for Stack Set Operations in the CloudFormation User Guide.
" + "documentation":"The Amazon Resource Name (ARN) of the IAM role to use to create this stack set.
Specify an IAM role only if you are using customized administrator roles to control which users or groups can manage specific stack sets within the same administrator account. For more information, see Prerequisites: Granting Permissions for Stack Set Operations in the CloudFormation User Guide.
" }, "ExecutionRoleName":{ "shape":"ExecutionRoleName", @@ -2714,7 +2714,7 @@ "members":{ "StackName":{ "shape":"StackName", - "documentation":"If you don't pass a parameter to StackName, the API returns a response that describes all resources in the account. This requires ListStacks and DescribeStacks permissions.
The IAM policy below can be added to IAM policies when you want to limit resource-level permissions and avoid returning a response when no parameter is sent in the request:
{ \"Version\": \"2012-10-17\", \"Statement\": [{ \"Effect\": \"Deny\", \"Action\": \"cloudformation:DescribeStacks\", \"NotResource\": \"arn:aws:cloudformation:*:*:stack/*/*\" }] }
The name or the unique stack ID that's associated with the stack, which aren't always interchangeable:
Running stacks: You can specify either the stack's name or its unique stack ID.
Deleted stacks: You must specify the unique stack ID.
Default: There is no default value.
" + "documentation":"If you don't pass a parameter to StackName, the API returns a response that describes all resources in the account, which can impact performance. This requires ListStacks and DescribeStacks permissions.
Consider using the ListStacks API if you're not passing a parameter to StackName.
The IAM policy below can be added to IAM policies when you want to limit resource-level permissions and avoid returning a response when no parameter is sent in the request:
{ \"Version\": \"2012-10-17\", \"Statement\": [{ \"Effect\": \"Deny\", \"Action\": \"cloudformation:DescribeStacks\", \"NotResource\": \"arn:aws:cloudformation:*:*:stack/*/*\" }] }
The name or the unique stack ID that's associated with the stack, which aren't always interchangeable:
Running stacks: You can specify either the stack's name or its unique stack ID.
Deleted stacks: You must specify the unique stack ID.
Default: There is no default value.
" }, "NextToken":{ "shape":"NextToken", @@ -3080,7 +3080,7 @@ }, "RetainExceptOnCreate":{ "shape":"RetainExceptOnCreate", - "documentation":"This deletion policy deletes newly created resources, but retains existing resources, when a stack operation is rolled back. This ensures new, empty, and unused resources are deleted, while critical resources and their data are retained. RetainExceptOnCreate can be specified for any resource that supports the DeletionPolicy attribute.
When set to true, newly created resources are deleted when the operation rolls back. This includes newly created resources marked with a deletion policy of Retain.
Default: false
The input for the ExecuteChangeSet action.
" @@ -4925,7 +4925,7 @@ }, "RetainExceptOnCreate":{ "shape":"RetainExceptOnCreate", - "documentation":"This deletion policy deletes newly created resources, but retains existing resources, when a stack operation is rolled back. This ensures new, empty, and unused resources are deleted, while critical resources and their data are retained. RetainExceptOnCreate can be specified for any resource that supports the DeletionPolicy attribute.
When set to true, newly created resources are deleted when the operation rolls back. This includes newly created resources marked with a deletion policy of Retain.
Default: false
This deletion policy deletes newly created resources, but retains existing resources, when a stack operation is rolled back. This ensures new, empty, and unused resources are deleted, while critical resources and their data are retained. RetainExceptOnCreate can be specified for any resource that supports the DeletionPolicy attribute.
When set to true, newly created resources are deleted when the operation rolls back. This includes newly created resources marked with a deletion policy of Retain.
Default: false
The Stack data type.
" @@ -5916,7 +5916,7 @@ }, "AdministrationRoleARN":{ "shape":"RoleARN", - "documentation":"The Amazon Resource Name (ARN) of the IAM role used to create or update the stack set.
Use customized administrator roles to control which users or groups can manage specific stack sets within the same administrator account. For more information, see Prerequisites: Granting Permissions for Stack Set Operations in the CloudFormation User Guide.
" + "documentation":"The Amazon Resource Name (ARN) of the IAM role used to create or update the stack set.
Use customized administrator roles to control which users or groups can manage specific stack sets within the same administrator account. For more information, see Prerequisites: Granting Permissions for Stack Set Operations in the CloudFormation User Guide.
" }, "ExecutionRoleName":{ "shape":"ExecutionRoleName", @@ -6065,7 +6065,7 @@ }, "AdministrationRoleARN":{ "shape":"RoleARN", - "documentation":"The Amazon Resource Name (ARN) of the IAM role used to perform this stack set operation.
Use customized administrator roles to control which users or groups can manage specific stack sets within the same administrator account. For more information, see Define Permissions for Multiple Administrators in the CloudFormation User Guide.
" + "documentation":"The Amazon Resource Name (ARN) of the IAM role used to perform this stack set operation.
Use customized administrator roles to control which users or groups can manage specific stack sets within the same administrator account. For more information, see Define Permissions for Multiple Administrators in the CloudFormation User Guide.
" }, "ExecutionRoleName":{ "shape":"ExecutionRoleName", @@ -6964,7 +6964,7 @@ }, "RetainExceptOnCreate":{ "shape":"RetainExceptOnCreate", - "documentation":"This deletion policy deletes newly created resources, but retains existing resources, when a stack operation is rolled back. This ensures new, empty, and unused resources are deleted, while critical resources and their data are retained. RetainExceptOnCreate can be specified for any resource that supports the DeletionPolicy attribute.
When set to true, newly created resources are deleted when the operation rolls back. This includes newly created resources marked with a deletion policy of Retain.
Default: false
The input for an UpdateStack action.
" @@ -7060,7 +7060,7 @@ }, "Capabilities":{ "shape":"Capabilities", - "documentation":"In some cases, you must explicitly acknowledge that your stack template contains certain capabilities in order for CloudFormation to update the stack set and its associated stack instances.
CAPABILITY_IAM and CAPABILITY_NAMED_IAM
Some stack templates might include resources that can affect permissions in your Amazon Web Services account; for example, by creating new Identity and Access Management (IAM) users. For those stacks sets, you must explicitly acknowledge this by specifying one of these capabilities.
The following IAM resources require you to specify either the CAPABILITY_IAM or CAPABILITY_NAMED_IAM capability.
If you have IAM resources, you can specify either capability.
If you have IAM resources with custom names, you must specify CAPABILITY_NAMED_IAM.
If you don't specify either of these capabilities, CloudFormation returns an InsufficientCapabilities error.
If your stack template contains these resources, we recommend that you review all permissions associated with them and edit their permissions if necessary.
For more information, see Acknowledging IAM Resources in CloudFormation Templates.
CAPABILITY_AUTO_EXPAND
Some templates reference macros. If your stack set template references one or more macros, you must update the stack set directly from the processed template, without first reviewing the resulting changes in a change set. To update the stack set directly, you must acknowledge this capability. For more information, see Using CloudFormation Macros to Perform Custom Processing on Templates.
Stack sets with service-managed permissions do not currently support the use of macros in templates. (This includes the AWS::Include and AWS::Serverless transforms, which are macros hosted by CloudFormation.) Even if you specify this capability for a stack set with service-managed permissions, if you reference a macro in your template the stack set operation will fail.
In some cases, you must explicitly acknowledge that your stack template contains certain capabilities in order for CloudFormation to update the stack set and its associated stack instances.
CAPABILITY_IAM and CAPABILITY_NAMED_IAM
Some stack templates might include resources that can affect permissions in your Amazon Web Services account; for example, by creating new Identity and Access Management (IAM) users. For those stacks sets, you must explicitly acknowledge this by specifying one of these capabilities.
The following IAM resources require you to specify either the CAPABILITY_IAM or CAPABILITY_NAMED_IAM capability.
If you have IAM resources, you can specify either capability.
If you have IAM resources with custom names, you must specify CAPABILITY_NAMED_IAM.
If you don't specify either of these capabilities, CloudFormation returns an InsufficientCapabilities error.
If your stack template contains these resources, we recommend that you review all permissions associated with them and edit their permissions if necessary.
For more information, see Acknowledging IAM Resources in CloudFormation Templates.
CAPABILITY_AUTO_EXPAND
Some templates reference macros. If your stack set template references one or more macros, you must update the stack set directly from the processed template, without first reviewing the resulting changes in a change set. To update the stack set directly, you must acknowledge this capability. For more information, see Using CloudFormation Macros to Perform Custom Processing on Templates.
Stack sets with service-managed permissions do not currently support the use of macros in templates. (This includes the AWS::Include and AWS::Serverless transforms, which are macros hosted by CloudFormation.) Even if you specify this capability for a stack set with service-managed permissions, if you reference a macro in your template the stack set operation will fail.
The Amazon Resource Name (ARN) of the IAM role to use to update this stack set.
Specify an IAM role only if you are using customized administrator roles to control which users or groups can manage specific stack sets within the same administrator account. For more information, see Granting Permissions for Stack Set Operations in the CloudFormation User Guide.
If you specified a customized administrator role when you created the stack set, you must specify a customized administrator role, even if it is the same customized administrator role used with this stack set previously.
" + "documentation":"The Amazon Resource Name (ARN) of the IAM role to use to update this stack set.
Specify an IAM role only if you are using customized administrator roles to control which users or groups can manage specific stack sets within the same administrator account. For more information, see Granting Permissions for Stack Set Operations in the CloudFormation User Guide.
If you specified a customized administrator role when you created the stack set, you must specify a customized administrator role, even if it is the same customized administrator role used with this stack set previously.
" }, "ExecutionRoleName":{ "shape":"ExecutionRoleName", diff --git a/botocore/data/entityresolution/2018-05-10/service-2.json b/botocore/data/entityresolution/2018-05-10/service-2.json index 2603a3901e..c2e1ae1434 100644 --- a/botocore/data/entityresolution/2018-05-10/service-2.json +++ b/botocore/data/entityresolution/2018-05-10/service-2.json @@ -193,7 +193,7 @@ {"shape":"AccessDeniedException"}, {"shape":"ValidationException"} ], - "documentation":"Returns a list of all the MatchingWorkflows that have been created for an AWS account.
Returns a list of all the MatchingWorkflows that have been created for an Amazon Web Services account.
Returns a list of all the SchemaMappings that have been created for an AWS account.
Returns a list of all the SchemaMappings that have been created for an Amazon Web Services account.
Displays the tags associated with an AWS Entity Resolution resource. In Entity Resolution, SchemaMapping, and MatchingWorkflow can be tagged.
Displays the tags associated with an Entity Resolution resource. In Entity Resolution, SchemaMapping, and MatchingWorkflow can be tagged.
Assigns one or more tags (key-value pairs) to the specified AWS Entity Resolution resource. Tags can help you organize and categorize your resources. You can also use them to scope user permissions by granting a user permission to access or change only resources with certain tag values. In Entity Resolution, SchemaMapping, and MatchingWorkflow can be tagged. Tags don't have any semantic meaning to AWS and are interpreted strictly as strings of characters. You can use the TagResource action with a resource that already has tags. If you specify a new tag key, this tag is appended to the list of tags associated with the resource. If you specify a tag key that is already associated with the resource, the new tag value that you specify replaces the previous value for that tag.
Assigns one or more tags (key-value pairs) to the specified Entity Resolution resource. Tags can help you organize and categorize your resources. You can also use them to scope user permissions by granting a user permission to access or change only resources with certain tag values. In Entity Resolution, SchemaMapping and MatchingWorkflow can be tagged. Tags don't have any semantic meaning to Amazon Web Services and are interpreted strictly as strings of characters. You can use the TagResource action with a resource that already has tags. If you specify a new tag key, this tag is appended to the list of tags associated with the resource. If you specify a tag key that is already associated with the resource, the new tag value that you specify replaces the previous value for that tag.
Removes one or more tags from the specified AWS Entity Resolution resource. In Entity Resolution, SchemaMapping, and MatchingWorkflow can be tagged.
Removes one or more tags from the specified Entity Resolution resource. In Entity Resolution, SchemaMapping, and MatchingWorkflow can be tagged.
An object which defines the resolutionType and the ruleBasedProperties
An object which defines the resolutionType and the ruleBasedProperties.
The Amazon Resource Name (ARN) of the IAM role. AWS Entity Resolution assumes this role to create resources on your behalf as part of workflow execution.
" + "documentation":"The Amazon Resource Name (ARN) of the IAM role. Entity Resolution assumes this role to create resources on your behalf as part of workflow execution.
" }, "tags":{ "shape":"TagMap", @@ -415,11 +415,11 @@ }, "resolutionTechniques":{ "shape":"ResolutionTechniques", - "documentation":"An object which defines the resolutionType and the ruleBasedProperties
An object which defines the resolutionType and the ruleBasedProperties.
The Amazon Resource Name (ARN) of the IAM role. AWS Entity Resolution assumes this role to create resources on your behalf as part of workflow execution.
" + "documentation":"The Amazon Resource Name (ARN) of the IAM role. Entity Resolution assumes this role to create resources on your behalf as part of workflow execution.
" }, "workflowArn":{ "shape":"MatchingWorkflowArn", @@ -433,7 +433,10 @@ }, "CreateSchemaMappingInput":{ "type":"structure", - "required":["schemaName"], + "required":[ + "mappedInputFields", + "schemaName" + ], "members":{ "description":{ "shape":"Description", @@ -553,9 +556,17 @@ "ExceedsLimitException":{ "type":"structure", "members":{ - "message":{"shape":"ErrorMessage"} + "message":{"shape":"ErrorMessage"}, + "quotaName":{ + "shape":"String", + "documentation":"The name of the quota that has been breached.
" + }, + "quotaValue":{ + "shape":"Integer", + "documentation":"The current quota value for the customers.
" + } }, - "documentation":"The request was rejected because it attempted to create resources beyond the current AWS Entity Resolution account limits. The error message describes the limit exceeded. HTTP Status Code: 402
The request was rejected because it attempted to create resources beyond the current Entity Resolution account limits. The error message describes the limit exceeded. HTTP Status Code: 402
The current status of the job. Either running, succeeded, queued, or failed.
The current status of the job.
" } } }, @@ -692,11 +703,11 @@ }, "resolutionTechniques":{ "shape":"ResolutionTechniques", - "documentation":"An object which defines the resolutionType and the ruleBasedProperties
An object which defines the resolutionType and the ruleBasedProperties.
The Amazon Resource Name (ARN) of the IAM role. AWS Entity Resolution assumes this role to access resources on your behalf.
" + "documentation":"The Amazon Resource Name (ARN) of the IAM role. Entity Resolution assumes this role to access resources on your behalf.
" }, "tags":{ "shape":"TagMap", @@ -823,7 +834,7 @@ "members":{ "message":{"shape":"ErrorMessage"} }, - "documentation":"This exception occurs when there is an internal failure in the AWS Entity Resolution service. HTTP Status Code: 500
This exception occurs when there is an internal failure in the Entity Resolution service. HTTP Status Code: 500
The total number of records that did not get processed,
" + "documentation":"The total number of records that did not get processed.
" }, "totalRecordsProcessed":{ "shape":"Integer", @@ -890,7 +901,7 @@ }, "status":{ "shape":"JobStatus", - "documentation":"The current status of the job. Either running, succeeded, queued, or failed.
The current status of the job.
" } }, "documentation":"An object containing the JobId, Status, StartTime, and EndTime of a job.
A list of JobSummary objects, each of which contain the ID, status, start time, and end time of a job.
" + "documentation":"A list of JobSummary objects, each of which contain the ID, status, start time, and end time of a job.
A name of a column to be written to the output. This must be an InputField name in the schema mapping.
A list of OutputAttribute objects, each of which have the fields Name and Hashed. Each of these objects selects a column to be included in the output table, and whether the values of the column should be hashed.
A list of OutputAttribute objects, each of which have the fields Name and Hashed. Each of these objects selects a column to be included in the output table, and whether the values of the column should be hashed.
A list of OutputAttribute objects, each of which have the fields Name and Hashed. Each of these objects selects a column to be included in the output table, and whether the values of the column should be hashed.
A list of OutputAttribute objects, each of which have the fields Name and Hashed. Each of these objects selects a column to be included in the output table, and whether the values of the column should be hashed.
The S3 path to which Entity Resolution will write the output table.
" } }, - "documentation":"A list of OutputAttribute objects, each of which have the fields Name and Hashed. Each of these objects selects a column to be included in the output table, and whether the values of the column should be hashed.
A list of OutputAttribute objects, each of which have the fields Name and Hashed. Each of these objects selects a column to be included in the output table, and whether the values of the column should be hashed.
There are two types of matching, RULE_MATCHING and ML_MATCHING
The type of matching. There are two types of matching: RULE_MATCHING and ML_MATCHING.
An object which defines the list of matching rules to run and has a field Rules, which is a list of rule objects.
An object which defines the resolutionType and the ruleBasedProperties
An object which defines the resolutionType and the ruleBasedProperties.
You can either choose ONE_TO_ONE or MANY_TO_MANY as the AttributeMatchingModel. When choosing MANY_TO_MANY, the system can match attribute across the sub-types of an attribute type. For example, if the value of the Email field of Profile A and the value of BusinessEmail field of Profile B matches, the two profiles are matched on the Email type. When choosing ONE_TO_ONE the system can only match if the sub-types are exact matches. For example, only when the value of the Email field of Profile A and the value of the Email field of Profile B matches, the two profiles are matched on the Email type.
The comparison type. You can either choose ONE_TO_ONE or MANY_TO_MANY as the AttributeMatchingModel. When choosing MANY_TO_MANY, the system can match attributes across the sub-types of an attribute type. For example, if the value of the Email field of Profile A and the value of BusinessEmail field of Profile B matches, the two profiles are matched on the Email type. When choosing ONE_TO_ONE ,the system can only match if the sub-types are exact matches. For example, only when the value of the Email field of Profile A and the value of the Email field of Profile B matches, the two profiles are matched on the Email type.
A list of Rule objects, each of which have fields RuleName and MatchingKeys.
A list of Rule objects, each of which have fields RuleName and MatchingKeys.
An object which defines the list of matching rules to run and has a field Rules, which is a list of rule objects.
A key that allows grouping of multiple input attributes into a unified matching group. For example, let's consider a scenario where the source table contains various addresses, such as business_address and shipping_address. By assigning the MatchKey Address' to both attributes, Entity Resolution will match records across these fields to create a consolidated matching group. If no MatchKey is specified for a column, it won't be utilized for matching purposes but will still be included in the output table.
A key that allows grouping of multiple input attributes into a unified matching group. For example, let's consider a scenario where the source table contains various addresses, such as business_address and shipping_address. By assigning the MatchKey Address to both attributes, Entity Resolution will match records across these fields to create a consolidated matching group. If no MatchKey is specified for a column, it won't be utilized for matching purposes but will still be included in the output table.
An object which defines the resolutionType and the ruleBasedProperties
An object which defines the resolutionType and the ruleBasedProperties.
The Amazon Resource Name (ARN) of the IAM role. AWS Entity Resolution assumes this role to create resources on your behalf as part of workflow execution.
" + "documentation":"The Amazon Resource Name (ARN) of the IAM role. Entity Resolution assumes this role to create resources on your behalf as part of workflow execution.
" }, "workflowName":{ "shape":"EntityName", @@ -1514,7 +1526,7 @@ }, "roleArn":{ "shape":"String", - "documentation":"The Amazon Resource Name (ARN) of the IAM role. AWS Entity Resolution assumes this role to create resources on your behalf as part of workflow execution.
" + "documentation":"The Amazon Resource Name (ARN) of the IAM role. Entity Resolution assumes this role to create resources on your behalf as part of workflow execution.
" }, "workflowName":{ "shape":"EntityName", @@ -1527,7 +1539,7 @@ "members":{ "message":{"shape":"ErrorMessage"} }, - "documentation":"The input fails to satisfy the constraints specified by AWS Entity Resolution. HTTP Status Code: 400
The input fails to satisfy the constraints specified by Entity Resolution. HTTP Status Code: 400
Welcome to the AWS Entity Resolution API Reference.
AWS Entity Resolution is an AWS service that provides pre-configured entity resolution capabilities that enable developers and analysts at advertising and marketing companies to build an accurate and complete view of their consumers.
With AWS Entity Resolution, you have the ability to match source records containing consumer identifiers, such as name, email address, and phone number. This holds true even when these records have incomplete or conflicting identifiers. For example, AWS Entity Resolution can effectively match a source record from a customer relationship management (CRM) system, which includes account information like first name, last name, postal address, phone number, and email address, with a source record from a marketing system containing campaign information, such as username and email address.
To learn more about AWS Entity Resolution concepts, procedures, and best practices, see the AWS Entity Resolution User Guide.
" + "documentation":"Welcome to the Entity Resolution API Reference.
Entity Resolution is an Amazon Web Services service that provides pre-configured entity resolution capabilities that enable developers and analysts at advertising and marketing companies to build an accurate and complete view of their consumers.
With Entity Resolution, you can match source records containing consumer identifiers, such as name, email address, and phone number. This is true even when these records have incomplete or conflicting identifiers. For example, Entity Resolution can effectively match a source record from a customer relationship management (CRM) system with a source record from a marketing system containing campaign information.
To learn more about Entity Resolution concepts, procedures, and best practices, see the Entity Resolution User Guide.
" } diff --git a/botocore/data/lookoutequipment/2020-12-15/service-2.json b/botocore/data/lookoutequipment/2020-12-15/service-2.json index e6a07920ea..185149e6b3 100644 --- a/botocore/data/lookoutequipment/2020-12-15/service-2.json +++ b/botocore/data/lookoutequipment/2020-12-15/service-2.json @@ -104,7 +104,25 @@ {"shape":"ResourceNotFoundException"}, {"shape":"AccessDeniedException"} ], - "documentation":"Creates an ML model for data inference.
A machine-learning (ML) model is a mathematical model that finds patterns in your data. In Amazon Lookout for Equipment, the model learns the patterns of normal behavior and detects abnormal behavior that could be potential equipment failure (or maintenance events). The models are made by analyzing normal data and abnormalities in machine behavior that have already occurred.
Your model is trained using a portion of the data from your dataset and uses that data to learn patterns of normal behavior and abnormal patterns that lead to equipment failure. Another portion of the data is used to evaluate the model's accuracy.
" + "documentation":"Creates a machine learning model for data inference.
A machine-learning (ML) model is a mathematical model that finds patterns in your data. In Amazon Lookout for Equipment, the model learns the patterns of normal behavior and detects abnormal behavior that could be potential equipment failure (or maintenance events). The models are made by analyzing normal data and abnormalities in machine behavior that have already occurred.
Your model is trained using a portion of the data from your dataset and uses that data to learn patterns of normal behavior and abnormal patterns that lead to equipment failure. Another portion of the data is used to evaluate the model's accuracy.
" + }, + "CreateRetrainingScheduler":{ + "name":"CreateRetrainingScheduler", + "http":{ + "method":"POST", + "requestUri":"/" + }, + "input":{"shape":"CreateRetrainingSchedulerRequest"}, + "output":{"shape":"CreateRetrainingSchedulerResponse"}, + "errors":[ + {"shape":"ValidationException"}, + {"shape":"ResourceNotFoundException"}, + {"shape":"ConflictException"}, + {"shape":"ThrottlingException"}, + {"shape":"AccessDeniedException"}, + {"shape":"InternalServerException"} + ], + "documentation":"Creates a retraining scheduler on the specified model.
" }, "DeleteDataset":{ "name":"DeleteDataset", @@ -138,7 +156,7 @@ {"shape":"AccessDeniedException"}, {"shape":"InternalServerException"} ], - "documentation":"Deletes an inference scheduler that has been set up. Already processed output results are not affected.
" + "documentation":"Deletes an inference scheduler that has been set up. Prior inference results will not be deleted.
" }, "DeleteLabel":{ "name":"DeleteLabel", @@ -189,7 +207,7 @@ {"shape":"AccessDeniedException"}, {"shape":"ValidationException"} ], - "documentation":"Deletes an ML model currently available for Amazon Lookout for Equipment. This will prevent it from being used with an inference scheduler, even one that is already set up.
" + "documentation":"Deletes a machine learning model currently available for Amazon Lookout for Equipment. This will prevent it from being used with an inference scheduler, even one that is already set up.
" }, "DeleteResourcePolicy":{ "name":"DeleteResourcePolicy", @@ -208,6 +226,23 @@ ], "documentation":"Deletes the resource policy attached to the resource.
" }, + "DeleteRetrainingScheduler":{ + "name":"DeleteRetrainingScheduler", + "http":{ + "method":"POST", + "requestUri":"/" + }, + "input":{"shape":"DeleteRetrainingSchedulerRequest"}, + "errors":[ + {"shape":"ValidationException"}, + {"shape":"ResourceNotFoundException"}, + {"shape":"ConflictException"}, + {"shape":"ThrottlingException"}, + {"shape":"AccessDeniedException"}, + {"shape":"InternalServerException"} + ], + "documentation":"Deletes a retraining scheduler from a model. The retraining scheduler must be in the STOPPED status.
Provides a JSON containing the overall information about a specific ML model, including model name and ARN, dataset, training and evaluation information, status, and so on.
" + "documentation":"Provides a JSON containing the overall information about a specific machine learning model, including model name and ARN, dataset, training and evaluation information, status, and so on.
" }, "DescribeModelVersion":{ "name":"DescribeModelVersion", @@ -344,6 +379,23 @@ ], "documentation":"Provides the details of a resource policy attached to a resource.
" }, + "DescribeRetrainingScheduler":{ + "name":"DescribeRetrainingScheduler", + "http":{ + "method":"POST", + "requestUri":"/" + }, + "input":{"shape":"DescribeRetrainingSchedulerRequest"}, + "output":{"shape":"DescribeRetrainingSchedulerResponse"}, + "errors":[ + {"shape":"ValidationException"}, + {"shape":"ResourceNotFoundException"}, + {"shape":"ThrottlingException"}, + {"shape":"AccessDeniedException"}, + {"shape":"InternalServerException"} + ], + "documentation":"Provides a description of the retraining scheduler, including information such as the model name and retraining parameters.
" + }, "ImportDataset":{ "name":"ImportDataset", "http":{ @@ -529,6 +581,22 @@ ], "documentation":"Generates a list of all models in the account, including model name and ARN, dataset, and status.
" }, + "ListRetrainingSchedulers":{ + "name":"ListRetrainingSchedulers", + "http":{ + "method":"POST", + "requestUri":"/" + }, + "input":{"shape":"ListRetrainingSchedulersRequest"}, + "output":{"shape":"ListRetrainingSchedulersResponse"}, + "errors":[ + {"shape":"ValidationException"}, + {"shape":"ThrottlingException"}, + {"shape":"AccessDeniedException"}, + {"shape":"InternalServerException"} + ], + "documentation":"Lists all retraining schedulers in your account, filtering by model name prefix and status.
" + }, "ListSensorStatistics":{ "name":"ListSensorStatistics", "http":{ @@ -619,6 +687,24 @@ ], "documentation":"Starts an inference scheduler.
" }, + "StartRetrainingScheduler":{ + "name":"StartRetrainingScheduler", + "http":{ + "method":"POST", + "requestUri":"/" + }, + "input":{"shape":"StartRetrainingSchedulerRequest"}, + "output":{"shape":"StartRetrainingSchedulerResponse"}, + "errors":[ + {"shape":"ValidationException"}, + {"shape":"ResourceNotFoundException"}, + {"shape":"ConflictException"}, + {"shape":"ThrottlingException"}, + {"shape":"AccessDeniedException"}, + {"shape":"InternalServerException"} + ], + "documentation":"Starts a retraining scheduler.
" + }, "StopInferenceScheduler":{ "name":"StopInferenceScheduler", "http":{ @@ -637,6 +723,24 @@ ], "documentation":"Stops an inference scheduler.
" }, + "StopRetrainingScheduler":{ + "name":"StopRetrainingScheduler", + "http":{ + "method":"POST", + "requestUri":"/" + }, + "input":{"shape":"StopRetrainingSchedulerRequest"}, + "output":{"shape":"StopRetrainingSchedulerResponse"}, + "errors":[ + {"shape":"ValidationException"}, + {"shape":"ResourceNotFoundException"}, + {"shape":"ConflictException"}, + {"shape":"ThrottlingException"}, + {"shape":"AccessDeniedException"}, + {"shape":"InternalServerException"} + ], + "documentation":"Stops a retraining scheduler.
" + }, "TagResource":{ "name":"TagResource", "http":{ @@ -723,6 +827,40 @@ {"shape":"InternalServerException"} ], "documentation":"Updates the label group.
" + }, + "UpdateModel":{ + "name":"UpdateModel", + "http":{ + "method":"POST", + "requestUri":"/" + }, + "input":{"shape":"UpdateModelRequest"}, + "errors":[ + {"shape":"ConflictException"}, + {"shape":"ResourceNotFoundException"}, + {"shape":"ValidationException"}, + {"shape":"ThrottlingException"}, + {"shape":"AccessDeniedException"}, + {"shape":"InternalServerException"} + ], + "documentation":"Updates a model in the account.
" + }, + "UpdateRetrainingScheduler":{ + "name":"UpdateRetrainingScheduler", + "http":{ + "method":"POST", + "requestUri":"/" + }, + "input":{"shape":"UpdateRetrainingSchedulerRequest"}, + "errors":[ + {"shape":"ValidationException"}, + {"shape":"ResourceNotFoundException"}, + {"shape":"ConflictException"}, + {"shape":"ThrottlingException"}, + {"shape":"AccessDeniedException"}, + {"shape":"InternalServerException"} + ], + "documentation":"Updates a retraining scheduler.
" } }, "shapes":{ @@ -740,6 +878,21 @@ "max":1011, "min":1 }, + "AutoPromotionResult":{ + "type":"string", + "enum":[ + "MODEL_PROMOTED", + "MODEL_NOT_PROMOTED", + "RETRAINING_INTERNAL_ERROR", + "RETRAINING_CUSTOMER_ERROR", + "RETRAINING_CANCELLED" + ] + }, + "AutoPromotionResultReason":{ + "type":"string", + "max":256, + "min":1 + }, "Boolean":{"type":"boolean"}, "BoundedLengthString":{ "type":"string", @@ -868,7 +1021,7 @@ "members":{ "ModelName":{ "shape":"ModelName", - "documentation":"The name of the previously trained ML model being used to create the inference scheduler.
" + "documentation":"The name of the previously trained machine learning model being used to create the inference scheduler.
" }, "InferenceSchedulerName":{ "shape":"InferenceSchedulerName", @@ -1029,19 +1182,19 @@ "members":{ "ModelName":{ "shape":"ModelName", - "documentation":"The name for the ML model to be created.
" + "documentation":"The name for the machine learning model to be created.
" }, "DatasetName":{ "shape":"DatasetIdentifier", - "documentation":"The name of the dataset for the ML model being created.
" + "documentation":"The name of the dataset for the machine learning model being created.
" }, "DatasetSchema":{ "shape":"DatasetSchema", - "documentation":"The data schema for the ML model being created.
" + "documentation":"The data schema for the machine learning model being created.
" }, "LabelsInputConfiguration":{ "shape":"LabelsInputConfiguration", - "documentation":"The input configuration for the labels being used for the ML model that's being created.
" + "documentation":"The input configuration for the labels being used for the machine learning model that's being created.
" }, "ClientToken":{ "shape":"IdempotenceToken", @@ -1050,23 +1203,23 @@ }, "TrainingDataStartTime":{ "shape":"Timestamp", - "documentation":"Indicates the time reference in the dataset that should be used to begin the subset of training data for the ML model.
" + "documentation":"Indicates the time reference in the dataset that should be used to begin the subset of training data for the machine learning model.
" }, "TrainingDataEndTime":{ "shape":"Timestamp", - "documentation":"Indicates the time reference in the dataset that should be used to end the subset of training data for the ML model.
" + "documentation":"Indicates the time reference in the dataset that should be used to end the subset of training data for the machine learning model.
" }, "EvaluationDataStartTime":{ "shape":"Timestamp", - "documentation":"Indicates the time reference in the dataset that should be used to begin the subset of evaluation data for the ML model.
" + "documentation":"Indicates the time reference in the dataset that should be used to begin the subset of evaluation data for the machine learning model.
" }, "EvaluationDataEndTime":{ "shape":"Timestamp", - "documentation":"Indicates the time reference in the dataset that should be used to end the subset of evaluation data for the ML model.
" + "documentation":"Indicates the time reference in the dataset that should be used to end the subset of evaluation data for the machine learning model.
" }, "RoleArn":{ "shape":"IamRoleArn", - "documentation":"The Amazon Resource Name (ARN) of a role with permission to access the data source being used to create the ML model.
" + "documentation":"The Amazon Resource Name (ARN) of a role with permission to access the data source being used to create the machine learning model.
" }, "DataPreProcessingConfiguration":{ "shape":"DataPreProcessingConfiguration", @@ -1078,7 +1231,7 @@ }, "Tags":{ "shape":"TagList", - "documentation":"Any tags associated with the ML model being created.
" + "documentation":"Any tags associated with the machine learning model being created.
" }, "OffCondition":{ "shape":"OffCondition", @@ -1099,6 +1252,59 @@ } } }, + "CreateRetrainingSchedulerRequest":{ + "type":"structure", + "required":[ + "ModelName", + "RetrainingFrequency", + "LookbackWindow", + "ClientToken" + ], + "members":{ + "ModelName":{ + "shape":"ModelName", + "documentation":"The name of the model to add the retraining scheduler to.
" + }, + "RetrainingStartDate":{ + "shape":"Timestamp", + "documentation":"The start date for the retraining scheduler. Lookout for Equipment truncates the time you provide to the nearest UTC day.
" + }, + "RetrainingFrequency":{ + "shape":"RetrainingFrequency", + "documentation":"This parameter uses the ISO 8601 standard to set the frequency at which you want retraining to occur in terms of Years, Months, and/or Days (note: other parameters like Time are not currently supported). The minimum value is 30 days (P30D) and the maximum value is 1 year (P1Y). For example, the following values are valid:
P3M15D – Every 3 months and 15 days
P2M – Every 2 months
P150D – Every 150 days
The number of past days of data that will be used for retraining.
" + }, + "PromoteMode":{ + "shape":"ModelPromoteMode", + "documentation":"Indicates how the service will use new models. In MANAGED mode, new models will automatically be used for inference if they have better performance than the current model. In MANUAL mode, the new models will not be used until they are manually activated.
A unique identifier for the request. If you do not set the client request token, Amazon Lookout for Equipment generates one.
", + "idempotencyToken":true + } + } + }, + "CreateRetrainingSchedulerResponse":{ + "type":"structure", + "members":{ + "ModelName":{ + "shape":"ModelName", + "documentation":"The name of the model that you added the retraining scheduler to.
" + }, + "ModelArn":{ + "shape":"ModelArn", + "documentation":"The ARN of the model that you added the retraining scheduler to.
" + }, + "Status":{ + "shape":"RetrainingSchedulerStatus", + "documentation":"The status of the retraining scheduler.
" + } + } + }, "DataDelayOffsetInMinutes":{ "type":"long", "max":60, @@ -1214,7 +1420,7 @@ "members":{ "InlineDataSchema":{ "shape":"InlineDataSchema", - "documentation":"", + "documentation":"
The data schema used within the given dataset.
", "jsonvalue":true } }, @@ -1308,7 +1514,7 @@ "members":{ "ModelName":{ "shape":"ModelName", - "documentation":"The name of the ML model to be deleted.
" + "documentation":"The name of the machine learning model to be deleted.
" } } }, @@ -1322,6 +1528,16 @@ } } }, + "DeleteRetrainingSchedulerRequest":{ + "type":"structure", + "required":["ModelName"], + "members":{ + "ModelName":{ + "shape":"ModelName", + "documentation":"The name of the model whose retraining scheduler you want to delete.
" + } + } + }, "DescribeDataIngestionJobRequest":{ "type":"structure", "required":["JobId"], @@ -1477,11 +1693,11 @@ "members":{ "ModelArn":{ "shape":"ModelArn", - "documentation":"The Amazon Resource Name (ARN) of the ML model of the inference scheduler being described.
" + "documentation":"The Amazon Resource Name (ARN) of the machine learning model of the inference scheduler being described.
" }, "ModelName":{ "shape":"ModelName", - "documentation":"The name of the ML model of the inference scheduler being described.
" + "documentation":"The name of the machine learning model of the inference scheduler being described.
" }, "InferenceSchedulerName":{ "shape":"InferenceSchedulerName", @@ -1636,7 +1852,7 @@ "members":{ "ModelName":{ "shape":"ModelName", - "documentation":"The name of the ML model to be described.
" + "documentation":"The name of the machine learning model to be described.
" } } }, @@ -1645,19 +1861,19 @@ "members":{ "ModelName":{ "shape":"ModelName", - "documentation":"The name of the ML model being described.
" + "documentation":"The name of the machine learning model being described.
" }, "ModelArn":{ "shape":"ModelArn", - "documentation":"The Amazon Resource Name (ARN) of the ML model being described.
" + "documentation":"The Amazon Resource Name (ARN) of the machine learning model being described.
" }, "DatasetName":{ "shape":"DatasetName", - "documentation":"The name of the dataset being used by the ML being described.
" + "documentation":"The name of the dataset being used by the machine learning being described.
" }, "DatasetArn":{ "shape":"DatasetArn", - "documentation":"The Amazon Resouce Name (ARN) of the dataset used to create the ML model being described.
" + "documentation":"The Amazon Resouce Name (ARN) of the dataset used to create the machine learning model being described.
" }, "Schema":{ "shape":"InlineDataSchema", @@ -1670,23 +1886,23 @@ }, "TrainingDataStartTime":{ "shape":"Timestamp", - "documentation":"Indicates the time reference in the dataset that was used to begin the subset of training data for the ML model.
" + "documentation":"Indicates the time reference in the dataset that was used to begin the subset of training data for the machine learning model.
" }, "TrainingDataEndTime":{ "shape":"Timestamp", - "documentation":"Indicates the time reference in the dataset that was used to end the subset of training data for the ML model.
" + "documentation":"Indicates the time reference in the dataset that was used to end the subset of training data for the machine learning model.
" }, "EvaluationDataStartTime":{ "shape":"Timestamp", - "documentation":"Indicates the time reference in the dataset that was used to begin the subset of evaluation data for the ML model.
" + "documentation":"Indicates the time reference in the dataset that was used to begin the subset of evaluation data for the machine learning model.
" }, "EvaluationDataEndTime":{ "shape":"Timestamp", - "documentation":"Indicates the time reference in the dataset that was used to end the subset of evaluation data for the ML model.
" + "documentation":"Indicates the time reference in the dataset that was used to end the subset of evaluation data for the machine learning model.
" }, "RoleArn":{ "shape":"IamRoleArn", - "documentation":"The Amazon Resource Name (ARN) of a role with permission to access the data source for the ML model being described.
" + "documentation":"The Amazon Resource Name (ARN) of a role with permission to access the data source for the machine learning model being described.
" }, "DataPreProcessingConfiguration":{ "shape":"DataPreProcessingConfiguration", @@ -1698,15 +1914,15 @@ }, "TrainingExecutionStartTime":{ "shape":"Timestamp", - "documentation":"Indicates the time at which the training of the ML model began.
" + "documentation":"Indicates the time at which the training of the machine learning model began.
" }, "TrainingExecutionEndTime":{ "shape":"Timestamp", - "documentation":"Indicates the time at which the training of the ML model was completed.
" + "documentation":"Indicates the time at which the training of the machine learning model was completed.
" }, "FailedReason":{ "shape":"BoundedLengthString", - "documentation":"If the training of the ML model failed, this indicates the reason for that failure.
" + "documentation":"If the training of the machine learning model failed, this indicates the reason for that failure.
" }, "ModelMetrics":{ "shape":"ModelMetrics", @@ -1715,11 +1931,11 @@ }, "LastUpdatedTime":{ "shape":"Timestamp", - "documentation":"Indicates the last time the ML model was updated. The type of update is not specified.
" + "documentation":"Indicates the last time the machine learning model was updated. The type of update is not specified.
" }, "CreatedAt":{ "shape":"Timestamp", - "documentation":"Indicates the time and date at which the ML model was created.
" + "documentation":"Indicates the time and date at which the machine learning model was created.
" }, "ServerSideKmsKeyId":{ "shape":"KmsKeyArn", @@ -1764,6 +1980,47 @@ "PreviousModelVersionActivatedAt":{ "shape":"Timestamp", "documentation":"The date and time when the previous active model version was activated.
" + }, + "PriorModelMetrics":{ + "shape":"ModelMetrics", + "documentation":"If the model version was retrained, this field shows a summary of the performance of the prior model on the new training range. You can use the information in this JSON-formatted object to compare the new model version and the prior model version.
", + "jsonvalue":true + }, + "LatestScheduledRetrainingFailedReason":{ + "shape":"BoundedLengthString", + "documentation":"If the model version was generated by retraining and the training failed, this indicates the reason for that failure.
" + }, + "LatestScheduledRetrainingStatus":{ + "shape":"ModelVersionStatus", + "documentation":"Indicates the status of the most recent scheduled retraining run.
" + }, + "LatestScheduledRetrainingModelVersion":{ + "shape":"ModelVersion", + "documentation":"Indicates the most recent model version that was generated by retraining.
" + }, + "LatestScheduledRetrainingStartTime":{ + "shape":"Timestamp", + "documentation":"Indicates the start time of the most recent scheduled retraining run.
" + }, + "LatestScheduledRetrainingAvailableDataInDays":{ + "shape":"Integer", + "documentation":"Indicates the number of days of data used in the most recent scheduled retraining run.
" + }, + "NextScheduledRetrainingStartDate":{ + "shape":"Timestamp", + "documentation":"Indicates the date and time that the next scheduled retraining run will start on. Lookout for Equipment truncates the time you provide to the nearest UTC day.
" + }, + "AccumulatedInferenceDataStartTime":{ + "shape":"Timestamp", + "documentation":"Indicates the start time of the inference data that has been accumulated.
" + }, + "AccumulatedInferenceDataEndTime":{ + "shape":"Timestamp", + "documentation":"Indicates the end time of the inference data that has been accumulated.
" + }, + "RetrainingSchedulerStatus":{ + "shape":"RetrainingSchedulerStatus", + "documentation":"Indicates the status of the retraining scheduler.
" } } }, @@ -1892,6 +2149,22 @@ "ImportedDataSizeInBytes":{ "shape":"DataSizeInBytes", "documentation":"The size in bytes of the imported data. This field appears if the model version was imported.
" + }, + "PriorModelMetrics":{ + "shape":"ModelMetrics", + "documentation":"If the model version was retrained, this field shows a summary of the performance of the prior model on the new training range. You can use the information in this JSON-formatted object to compare the new model version and the prior model version.
" + }, + "RetrainingAvailableDataInDays":{ + "shape":"Integer", + "documentation":"Indicates the number of days of data used in the most recent scheduled retraining run.
" + }, + "AutoPromotionResult":{ + "shape":"AutoPromotionResult", + "documentation":"Indicates whether the model version was promoted to be the active version after retraining or if there was an error with or cancellation of the retraining.
" + }, + "AutoPromotionResultReason":{ + "shape":"AutoPromotionResultReason", + "documentation":"Indicates the reason for the AutoPromotionResult. For example, a model might not be promoted if its performance was worse than the active version, if there was an error during training, or if the retraining scheduler was using MANUAL promote mode. The model will be promoted in MANAGED promote mode if the performance is better than the previous model.
The name of the model that the retraining scheduler is attached to.
" + } + } + }, + "DescribeRetrainingSchedulerResponse":{ + "type":"structure", + "members":{ + "ModelName":{ + "shape":"ModelName", + "documentation":"The name of the model that the retraining scheduler is attached to.
" + }, + "ModelArn":{ + "shape":"ModelArn", + "documentation":"The ARN of the model that the retraining scheduler is attached to.
" + }, + "RetrainingStartDate":{ + "shape":"Timestamp", + "documentation":"The start date for the retraining scheduler. Lookout for Equipment truncates the time you provide to the nearest UTC day.
" + }, + "RetrainingFrequency":{ + "shape":"RetrainingFrequency", + "documentation":"The frequency at which the model retraining is set. This follows the ISO 8601 guidelines.
" + }, + "LookbackWindow":{ + "shape":"LookbackWindow", + "documentation":"The number of past days of data used for retraining.
" + }, + "Status":{ + "shape":"RetrainingSchedulerStatus", + "documentation":"The status of the retraining scheduler.
" + }, + "PromoteMode":{ + "shape":"ModelPromoteMode", + "documentation":"Indicates how the service uses new models. In MANAGED mode, new models are used for inference if they have better performance than the current model. In MANUAL mode, the new models are not used until they are manually activated.
Indicates the time and date at which the retraining scheduler was created.
" + }, + "UpdatedAt":{ + "shape":"Timestamp", + "documentation":"Indicates the time and date at which the retraining scheduler was updated.
" + } + } + }, "DuplicateTimestamps":{ "type":"structure", "required":["TotalNumberOfDuplicateTimestamps"], @@ -2064,6 +2388,10 @@ "Tags":{ "shape":"TagList", "documentation":"The tags associated with the machine learning model to be created.
" + }, + "InferenceDataImportStrategy":{ + "shape":"InferenceDataImportStrategy", + "documentation":"Indicates how to import the accumulated inference data when a model version is imported. The possible values are as follows:
NO_IMPORT – Don't import the data.
ADD_WHEN_EMPTY – Only import the data from the source model if there is no existing data in the target model.
OVERWRITE – Import the data from the source model and overwrite the existing data in the target model.
The name of the ML model being used for the inference execution.
" + "documentation":"The name of the machine learning model being used for the inference execution.
" }, "ModelArn":{ "shape":"ModelArn", - "documentation":"The Amazon Resource Name (ARN) of the ML model used for the inference execution.
" + "documentation":"The Amazon Resource Name (ARN) of the machine learning model used for the inference execution.
" }, "InferenceSchedulerName":{ "shape":"InferenceSchedulerName", @@ -2179,7 +2515,7 @@ }, "CustomerResultObject":{ "shape":"S3Object", - "documentation":"" + "documentation":"
The S3 object that the inference execution results were uploaded to.
" }, "Status":{ "shape":"InferenceExecutionStatus", @@ -2188,6 +2524,14 @@ "FailedReason":{ "shape":"BoundedLengthString", "documentation":"Specifies the reason for failure when an inference execution has failed.
" + }, + "ModelVersion":{ + "shape":"ModelVersion", + "documentation":"The model version used for the inference execution.
" + }, + "ModelVersionArn":{ + "shape":"ModelVersionArn", + "documentation":"The Amazon Resource Number (ARN) of the model version used for the inference execution.
" } }, "documentation":"Contains information about the specific inference execution, including input and output data configuration, inference scheduling information, status, and so on.
" @@ -2305,11 +2649,11 @@ "members":{ "ModelName":{ "shape":"ModelName", - "documentation":"The name of the ML model used for the inference scheduler.
" + "documentation":"The name of the machine learning model used for the inference scheduler.
" }, "ModelArn":{ "shape":"ModelArn", - "documentation":"The Amazon Resource Name (ARN) of the ML model used by the inference scheduler.
" + "documentation":"The Amazon Resource Name (ARN) of the machine learning model used by the inference scheduler.
" }, "InferenceSchedulerName":{ "shape":"InferenceSchedulerName", @@ -2786,7 +3130,7 @@ }, "ModelName":{ "shape":"ModelName", - "documentation":"The name of the ML model used by the inference scheduler to be listed.
" + "documentation":"The name of the machine learning model used by the inference scheduler to be listed.
" }, "Status":{ "shape":"InferenceSchedulerStatus", @@ -2944,23 +3288,23 @@ "members":{ "NextToken":{ "shape":"NextToken", - "documentation":"An opaque pagination token indicating where to continue the listing of ML models.
" + "documentation":"An opaque pagination token indicating where to continue the listing of machine learning models.
" }, "MaxResults":{ "shape":"MaxResults", - "documentation":"Specifies the maximum number of ML models to list.
" + "documentation":"Specifies the maximum number of machine learning models to list.
" }, "Status":{ "shape":"ModelStatus", - "documentation":"The status of the ML model.
" + "documentation":"The status of the machine learning model.
" }, "ModelNameBeginsWith":{ "shape":"ModelName", - "documentation":"The beginning of the name of the ML models being listed.
" + "documentation":"The beginning of the name of the machine learning models being listed.
" }, "DatasetNameBeginsWith":{ "shape":"DatasetName", - "documentation":"The beginning of the name of the dataset of the ML models to be listed.
" + "documentation":"The beginning of the name of the dataset of the machine learning models to be listed.
" } } }, @@ -2969,7 +3313,7 @@ "members":{ "NextToken":{ "shape":"NextToken", - "documentation":"An opaque pagination token indicating where to continue the listing of ML models.
" + "documentation":"An opaque pagination token indicating where to continue the listing of machine learning models.
" }, "ModelSummaries":{ "shape":"ModelSummaries", @@ -2982,6 +3326,40 @@ "member":{"shape":"S3Object"}, "min":0 }, + "ListRetrainingSchedulersRequest":{ + "type":"structure", + "members":{ + "ModelNameBeginsWith":{ + "shape":"ModelName", + "documentation":"Specify this field to only list retraining schedulers whose machine learning models begin with the value you specify.
" + }, + "Status":{ + "shape":"RetrainingSchedulerStatus", + "documentation":"Specify this field to only list retraining schedulers whose status matches the value you specify.
" + }, + "NextToken":{ + "shape":"NextToken", + "documentation":"If the number of results exceeds the maximum, a pagination token is returned. Use the token in the request to show the next page of retraining schedulers.
" + }, + "MaxResults":{ + "shape":"MaxResults", + "documentation":"Specifies the maximum number of retraining schedulers to list.
" + } + } + }, + "ListRetrainingSchedulersResponse":{ + "type":"structure", + "members":{ + "RetrainingSchedulerSummaries":{ + "shape":"RetrainingSchedulerSummaries", + "documentation":"Provides information on the specified retraining scheduler, including the model name, model ARN, status, and start date.
" + }, + "NextToken":{ + "shape":"NextToken", + "documentation":"If the number of results exceeds the maximum, this pagination token is returned. Use this token in the request to show the next page of retraining schedulers.
" + } + } + }, "ListSensorStatisticsRequest":{ "type":"structure", "required":["DatasetName"], @@ -3036,6 +3414,10 @@ } } }, + "LookbackWindow":{ + "type":"string", + "pattern":"^P180D$|^P360D$|^P540D$|^P720D$" + }, "MaxResults":{ "type":"integer", "max":500, @@ -3087,6 +3469,13 @@ "min":1, "pattern":"^[0-9a-zA-Z_-]{1,200}$" }, + "ModelPromoteMode":{ + "type":"string", + "enum":[ + "MANAGED", + "MANUAL" + ] + }, "ModelStatus":{ "type":"string", "enum":[ @@ -3105,15 +3494,15 @@ "members":{ "ModelName":{ "shape":"ModelName", - "documentation":"The name of the ML model.
" + "documentation":"The name of the machine learning model.
" }, "ModelArn":{ "shape":"ModelArn", - "documentation":"The Amazon Resource Name (ARN) of the ML model.
" + "documentation":"The Amazon Resource Name (ARN) of the machine learning model.
" }, "DatasetName":{ "shape":"DatasetName", - "documentation":"The name of the dataset being used for the ML model.
" + "documentation":"The name of the dataset being used for the machine learning model.
" }, "DatasetArn":{ "shape":"DatasetArn", @@ -3121,7 +3510,7 @@ }, "Status":{ "shape":"ModelStatus", - "documentation":"Indicates the status of the ML model.
" + "documentation":"Indicates the status of the machine learning model.
" }, "CreatedAt":{ "shape":"Timestamp", @@ -3134,9 +3523,29 @@ "ActiveModelVersionArn":{ "shape":"ModelVersionArn", "documentation":"The Amazon Resource Name (ARN) of the model version that is set as active. The active model version is the model version that the inference scheduler uses to run an inference execution.
" + }, + "LatestScheduledRetrainingStatus":{ + "shape":"ModelVersionStatus", + "documentation":"Indicates the status of the most recent scheduled retraining run.
" + }, + "LatestScheduledRetrainingModelVersion":{ + "shape":"ModelVersion", + "documentation":"Indicates the most recent model version that was generated by retraining.
" + }, + "LatestScheduledRetrainingStartTime":{ + "shape":"Timestamp", + "documentation":"Indicates the start time of the most recent scheduled retraining run.
" + }, + "NextScheduledRetrainingStartDate":{ + "shape":"Timestamp", + "documentation":"Indicates the date that the next scheduled retraining run will start on. Lookout for Equipment truncates the time you provide to the nearest UTC day.
" + }, + "RetrainingSchedulerStatus":{ + "shape":"RetrainingSchedulerStatus", + "documentation":"Indicates the status of the retraining scheduler.
" } }, - "documentation":"Provides information about the specified ML model, including dataset and model names and ARNs, as well as status.
" + "documentation":"Provides information about the specified machine learning model, including dataset and model names and ARNs, as well as status.
" }, "ModelVersion":{ "type":"long", @@ -3320,6 +3729,55 @@ "documentation":"The resource requested could not be found. Verify the resource ID and retry your request.
", "exception":true }, + "RetrainingFrequency":{ + "type":"string", + "max":10, + "min":1, + "pattern":"^P(\\dY)?(\\d{1,2}M)?(\\d{1,3}D)?$" + }, + "RetrainingSchedulerStatus":{ + "type":"string", + "enum":[ + "PENDING", + "RUNNING", + "STOPPING", + "STOPPED" + ] + }, + "RetrainingSchedulerSummaries":{ + "type":"list", + "member":{"shape":"RetrainingSchedulerSummary"} + }, + "RetrainingSchedulerSummary":{ + "type":"structure", + "members":{ + "ModelName":{ + "shape":"ModelName", + "documentation":"The name of the model that the retraining scheduler is attached to.
" + }, + "ModelArn":{ + "shape":"ModelArn", + "documentation":"The ARN of the model that the retraining scheduler is attached to.
" + }, + "Status":{ + "shape":"RetrainingSchedulerStatus", + "documentation":"The status of the retraining scheduler.
" + }, + "RetrainingStartDate":{ + "shape":"Timestamp", + "documentation":"The start date for the retraining scheduler. Lookout for Equipment truncates the time you provide to the nearest UTC day.
" + }, + "RetrainingFrequency":{ + "shape":"RetrainingFrequency", + "documentation":"The frequency at which the model retraining is set. This follows the ISO 8601 guidelines.
" + }, + "LookbackWindow":{ + "shape":"LookbackWindow", + "documentation":"The number of past days of data used for retraining.
" + } + }, + "documentation":"Provides information about the specified retraining scheduler, including model name, status, start date, frequency, and lookback window.
" + }, "S3Bucket":{ "type":"string", "max":63, @@ -3500,11 +3958,11 @@ "members":{ "ModelArn":{ "shape":"ModelArn", - "documentation":"The Amazon Resource Name (ARN) of the ML model being used by the inference scheduler.
" + "documentation":"The Amazon Resource Name (ARN) of the machine learning model being used by the inference scheduler.
" }, "ModelName":{ "shape":"ModelName", - "documentation":"The name of the ML model being used by the inference scheduler.
" + "documentation":"The name of the machine learning model being used by the inference scheduler.
" }, "InferenceSchedulerName":{ "shape":"InferenceSchedulerName", @@ -3520,6 +3978,33 @@ } } }, + "StartRetrainingSchedulerRequest":{ + "type":"structure", + "required":["ModelName"], + "members":{ + "ModelName":{ + "shape":"ModelName", + "documentation":"The name of the model whose retraining scheduler you want to start.
" + } + } + }, + "StartRetrainingSchedulerResponse":{ + "type":"structure", + "members":{ + "ModelName":{ + "shape":"ModelName", + "documentation":"The name of the model whose retraining scheduler is being started.
" + }, + "ModelArn":{ + "shape":"ModelArn", + "documentation":"The ARN of the model whose retraining scheduler is being started.
" + }, + "Status":{ + "shape":"RetrainingSchedulerStatus", + "documentation":"The status of the retraining scheduler.
" + } + } + }, "StatisticalIssueStatus":{ "type":"string", "enum":[ @@ -3542,11 +4027,11 @@ "members":{ "ModelArn":{ "shape":"ModelArn", - "documentation":"The Amazon Resource Name (ARN) of the ML model used by the inference scheduler being stopped.
" + "documentation":"The Amazon Resource Name (ARN) of the machine learning model used by the inference scheduler being stopped.
" }, "ModelName":{ "shape":"ModelName", - "documentation":"The name of the ML model used by the inference scheduler being stopped.
" + "documentation":"The name of the machine learning model used by the inference scheduler being stopped.
" }, "InferenceSchedulerName":{ "shape":"InferenceSchedulerName", @@ -3562,6 +4047,33 @@ } } }, + "StopRetrainingSchedulerRequest":{ + "type":"structure", + "required":["ModelName"], + "members":{ + "ModelName":{ + "shape":"ModelName", + "documentation":"The name of the model whose retraining scheduler you want to stop.
" + } + } + }, + "StopRetrainingSchedulerResponse":{ + "type":"structure", + "members":{ + "ModelName":{ + "shape":"ModelName", + "documentation":"The name of the model whose retraining scheduler is being stopped.
" + }, + "ModelArn":{ + "shape":"ModelArn", + "documentation":"The ARN of the model whose retraining scheduler is being stopped.
" + }, + "Status":{ + "shape":"RetrainingSchedulerStatus", + "documentation":"The status of the retraining scheduler.
" + } + } + }, "Tag":{ "type":"structure", "required":[ @@ -3779,6 +4291,47 @@ } } }, + "UpdateModelRequest":{ + "type":"structure", + "required":["ModelName"], + "members":{ + "ModelName":{ + "shape":"ModelName", + "documentation":"The name of the model to update.
" + }, + "LabelsInputConfiguration":{"shape":"LabelsInputConfiguration"}, + "RoleArn":{ + "shape":"IamRoleArn", + "documentation":"The ARN of the model to update.
" + } + } + }, + "UpdateRetrainingSchedulerRequest":{ + "type":"structure", + "required":["ModelName"], + "members":{ + "ModelName":{ + "shape":"ModelName", + "documentation":"The name of the model whose retraining scheduler you want to update.
" + }, + "RetrainingStartDate":{ + "shape":"Timestamp", + "documentation":"The start date for the retraining scheduler. Lookout for Equipment truncates the time you provide to the nearest UTC day.
" + }, + "RetrainingFrequency":{ + "shape":"RetrainingFrequency", + "documentation":"This parameter uses the ISO 8601 standard to set the frequency at which you want retraining to occur in terms of Years, Months, and/or Days (note: other parameters like Time are not currently supported). The minimum value is 30 days (P30D) and the maximum value is 1 year (P1Y). For example, the following values are valid:
P3M15D – Every 3 months and 15 days
P2M – Every 2 months
P150D – Every 150 days
The number of past days of data that will be used for retraining.
" + }, + "PromoteMode":{ + "shape":"ModelPromoteMode", + "documentation":"Indicates how the service will use new models. In MANAGED mode, new models will automatically be used for inference if they have better performance than the current model. In MANUAL mode, the new models will not be used until they are manually activated.