Amazon SageMaker Canvas adds WorkspeceSettings support for CanvasAppSettings

Author: aws-sdk-dotnet-automation

generator/ServiceModels/sagemaker/sagemaker-2017-07-24.api.json

@@ -4809,7 +4809,8 @@
       "type":"structure",
       "members":{
         "TimeSeriesForecastingSettings":{"shape":"TimeSeriesForecastingSettings"},
-        "ModelRegisterSettings":{"shape":"ModelRegisterSettings"}
+        "ModelRegisterSettings":{"shape":"ModelRegisterSettings"},
+        "WorkspaceSettings":{"shape":"WorkspaceSettings"}
       }
     },
     "CapacitySize":{
@@ -21222,6 +21223,13 @@
       "type":"list",
       "member":{"shape":"Workforce"}
     },
+    "WorkspaceSettings":{
+      "type":"structure",
+      "members":{
+        "S3ArtifactPath":{"shape":"S3Uri"},
+        "S3KmsKeyId":{"shape":"KmsKeyId"}
+      }
+    },
     "Workteam":{
       "type":"structure",
       "required":[

generator/ServiceModels/sagemaker/sagemaker-2017-07-24.docs.json

@@ -1553,8 +1553,8 @@
     "CapacitySize": {
       "base": "<p>Specifies the type and size of the endpoint capacity to activate for a blue/green deployment, a rolling deployment, or a rollback strategy. You can specify your batches as either instance count or the overall percentage or your fleet.</p> <p>For a rollback strategy, if you don't specify the fields in this object, or if you set the <code>Value</code> to 100%, then SageMaker uses a blue/green rollback strategy and rolls all traffic back to the blue fleet.</p>",
       "refs": {
-        "RollingUpdatePolicy$MaximumBatchSize": null,
-        "RollingUpdatePolicy$RollbackMaximumBatchSize": null,
+        "RollingUpdatePolicy$MaximumBatchSize": "<p>Batch size for each rolling step to provision capacity and turn on traffic on the new endpoint fleet, and terminate capacity on the old endpoint fleet. Value must be between 5% to 50% of the variant's total instance count.</p>",
+        "RollingUpdatePolicy$RollbackMaximumBatchSize": "<p>Batch size for rollback to the old endpoint fleet. Each rolling step to provision capacity and turn on traffic on the old endpoint fleet, and terminate capacity on the new endpoint fleet. If this field is absent, the default value will be set to 100% of total capacity which means to bring up the whole capacity of the old fleet at once during rollback.</p>",
         "TrafficRoutingConfig$CanarySize": "<p>Batch size for the first step to turn on traffic on the new endpoint fleet. <code>Value</code> must be less than or equal to 50% of the variant's total instance count.</p>",
         "TrafficRoutingConfig$LinearStepSize": "<p>Batch size for each step to turn on traffic on the new endpoint fleet. <code>Value</code> must be 10-50% of the variant's total instance count.</p>"
       }
@@ -7087,7 +7087,8 @@
         "S3StorageConfig$KmsKeyId": "<p>The Amazon Web Services Key Management Service (KMS) key ARN of the key used to encrypt any objects written into the <code>OfflineStore</code> S3 location.</p> <p>The IAM <code>roleARN</code> that is passed as a parameter to <code>CreateFeatureGroup</code> must have below permissions to the <code>KmsKeyId</code>:</p> <ul> <li> <p> <code>\"kms:GenerateDataKey\"</code> </p> </li> </ul>",
         "SharingSettings$S3KmsKeyId": "<p>When <code>NotebookOutputOption</code> is <code>Allowed</code>, the Amazon Web Services Key Management Service (KMS) encryption key ID used to encrypt the notebook cell output in the Amazon S3 bucket.</p>",
         "TransformOutput$KmsKeyId": "<p>The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption. The <code>KmsKeyId</code> can be any of the following formats: </p> <ul> <li> <p>Key ID: <code>1234abcd-12ab-34cd-56ef-1234567890ab</code> </p> </li> <li> <p>Key ARN: <code>arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab</code> </p> </li> <li> <p>Alias name: <code>alias/ExampleAlias</code> </p> </li> <li> <p>Alias name ARN: <code>arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias</code> </p> </li> </ul> <p>If you don't provide a KMS key ID, Amazon SageMaker uses the default KMS key for Amazon S3 for your role's account. For more information, see <a href=\"https://docs.aws.amazon.com/AmazonS3/latest/dev/UsingKMSEncryption.html\">KMS-Managed Encryption Keys</a> in the <i>Amazon Simple Storage Service Developer Guide.</i> </p> <p>The KMS key policy must grant permission to the IAM role that you specify in your <a href=\"https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateModel.html\">CreateModel</a> request. For more information, see <a href=\"https://docs.aws.amazon.com/kms/latest/developerguide/key-policies.html\">Using Key Policies in Amazon Web Services KMS</a> in the <i>Amazon Web Services Key Management Service Developer Guide</i>.</p>",
-        "TransformResources$VolumeKmsKeyId": "<p>The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt model data on the storage volume attached to the ML compute instance(s) that run the batch transform job.</p> <note> <p>Certain Nitro-based instances include local storage, dependent on the instance type. Local storage volumes are encrypted using a hardware module on the instance. You can't request a <code>VolumeKmsKeyId</code> when using an instance type with local storage.</p> <p>For a list of instance types that support local instance storage, see <a href=\"https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/InstanceStorage.html#instance-store-volumes\">Instance Store Volumes</a>.</p> <p>For more information about local instance storage encryption, see <a href=\"https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/ssd-instance-store.html\">SSD Instance Store Volumes</a>.</p> </note> <p> The <code>VolumeKmsKeyId</code> can be any of the following formats:</p> <ul> <li> <p>Key ID: <code>1234abcd-12ab-34cd-56ef-1234567890ab</code> </p> </li> <li> <p>Key ARN: <code>arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab</code> </p> </li> <li> <p>Alias name: <code>alias/ExampleAlias</code> </p> </li> <li> <p>Alias name ARN: <code>arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias</code> </p> </li> </ul>"
+        "TransformResources$VolumeKmsKeyId": "<p>The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt model data on the storage volume attached to the ML compute instance(s) that run the batch transform job.</p> <note> <p>Certain Nitro-based instances include local storage, dependent on the instance type. Local storage volumes are encrypted using a hardware module on the instance. You can't request a <code>VolumeKmsKeyId</code> when using an instance type with local storage.</p> <p>For a list of instance types that support local instance storage, see <a href=\"https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/InstanceStorage.html#instance-store-volumes\">Instance Store Volumes</a>.</p> <p>For more information about local instance storage encryption, see <a href=\"https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/ssd-instance-store.html\">SSD Instance Store Volumes</a>.</p> </note> <p> The <code>VolumeKmsKeyId</code> can be any of the following formats:</p> <ul> <li> <p>Key ID: <code>1234abcd-12ab-34cd-56ef-1234567890ab</code> </p> </li> <li> <p>Key ARN: <code>arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab</code> </p> </li> <li> <p>Alias name: <code>alias/ExampleAlias</code> </p> </li> <li> <p>Alias name ARN: <code>arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias</code> </p> </li> </ul>",
+        "WorkspaceSettings$S3KmsKeyId": "<p>The Amazon Web Services Key Management Service (KMS) encryption key ID that is used to encrypt artifacts generated by Canvas in the Amazon S3 bucket.</p>"
       }
     },
     "LabelAttributeName": {
@@ -12051,10 +12052,11 @@
         "SharingSettings$S3OutputPath": "<p>When <code>NotebookOutputOption</code> is <code>Allowed</code>, the Amazon S3 bucket used to store the shared notebook snapshots.</p>",
         "TabularJobConfig$FeatureSpecificationS3Uri": "<p>A URL to the Amazon S3 data source containing selected features from the input data source to run an Autopilot job V2. You can input <code>FeatureAttributeNames</code> (optional) in JSON format as shown below: </p> <p> <code>{ \"FeatureAttributeNames\":[\"col1\", \"col2\", ...] }</code>.</p> <p>You can also specify the data type of the feature (optional) in the format shown below:</p> <p> <code>{ \"FeatureDataTypes\":{\"col1\":\"numeric\", \"col2\":\"categorical\" ... } }</code> </p> <note> <p>These column keys may not include the target column.</p> </note> <p>In ensembling mode, Autopilot only supports the following data types: <code>numeric</code>, <code>categorical</code>, <code>text</code>, and <code>datetime</code>. In HPO mode, Autopilot can support <code>numeric</code>, <code>categorical</code>, <code>text</code>, <code>datetime</code>, and <code>sequence</code>.</p> <p>If only <code>FeatureDataTypes</code> is provided, the column keys (<code>col1</code>, <code>col2</code>,..) should be a subset of the column names in the input data. </p> <p>If both <code>FeatureDataTypes</code> and <code>FeatureAttributeNames</code> are provided, then the column keys should be a subset of the column names provided in <code>FeatureAttributeNames</code>. </p> <p>The key name <code>FeatureAttributeNames</code> is fixed. The values listed in <code>[\"col1\", \"col2\", ...]</code> are case sensitive and should be a list of strings containing unique values that are a subset of the column names in the input data. The list of columns provided must not include the target column.</p>",
         "TensorBoardOutputConfig$S3OutputPath": "<p>Path to Amazon S3 storage location for TensorBoard output.</p>",
-        "TimeSeriesForecastingJobConfig$FeatureSpecificationS3Uri": "<p>A URL to the Amazon S3 data source containing additional selected features that complement the target, itemID, timestamp, and grouped columns set in <code>TimeSeriesConfig</code>. When not provided, the AutoML job V2 includes all the columns from the original dataset that are not already declared in <code>TimeSeriesConfig</code>. If provided, the AutoML job V2 only considers these additional columns as a complement to the ones declared in <code>TimeSeriesConfig</code>.</p> <p> You can input <code>FeatureAttributeNames</code> (optional) in JSON format as shown below: </p> <p> <code>{ \"FeatureAttributeNames\":[\"col1\", \"col2\", ...] }</code>.</p> <p>You can also specify the data type of the feature (optional) in the format shown below:</p> <p> <code>{ \"FeatureDataTypes\":{\"col1\":\"numeric\", \"col2\":\"categorical\" ... } }</code> </p> <p>Autopilot supports the following data types: <code>numeric</code>, <code>categorical</code>, <code>text</code>, and <code>datetime</code>.</p> <note> <p>These column keys must not include any column set in <code>TimeSeriesConfig</code>.</p> </note> <p>When not provided, the AutoML job V2 includes all the columns from the original dataset that are not already declared in <code>TimeSeriesConfig</code>. If provided, the AutoML job V2 only considers these additional columns as a complement to the ones declared in <code>TimeSeriesConfig</code>.</p> <p>Autopilot supports the following data types: <code>numeric</code>, <code>categorical</code>, <code>text</code>, and <code>datetime</code>.</p>",
+        "TimeSeriesForecastingJobConfig$FeatureSpecificationS3Uri": "<p>A URL to the Amazon S3 data source containing additional selected features that complement the target, itemID, timestamp, and grouped columns set in <code>TimeSeriesConfig</code>. When not provided, the AutoML job V2 includes all the columns from the original dataset that are not already declared in <code>TimeSeriesConfig</code>. If provided, the AutoML job V2 only considers these additional columns as a complement to the ones declared in <code>TimeSeriesConfig</code>.</p> <p>You can input <code>FeatureAttributeNames</code> (optional) in JSON format as shown below: </p> <p> <code>{ \"FeatureAttributeNames\":[\"col1\", \"col2\", ...] }</code>.</p> <p>You can also specify the data type of the feature (optional) in the format shown below:</p> <p> <code>{ \"FeatureDataTypes\":{\"col1\":\"numeric\", \"col2\":\"categorical\" ... } }</code> </p> <p>Autopilot supports the following data types: <code>numeric</code>, <code>categorical</code>, <code>text</code>, and <code>datetime</code>.</p> <note> <p>These column keys must not include any column set in <code>TimeSeriesConfig</code>.</p> </note>",
         "TransformOutput$S3OutputPath": "<p>The Amazon S3 path where you want Amazon SageMaker to store the results of the transform job. For example, <code>s3://bucket-name/key-name-prefix</code>.</p> <p>For every S3 object used as input for the transform job, batch transform stores the transformed data with an .<code>out</code> suffix in a corresponding subfolder in the location in the output prefix. For example, for the input data stored at <code>s3://bucket-name/input-name-prefix/dataset01/data.csv</code>, batch transform stores the transformed data at <code>s3://bucket-name/output-name-prefix/input-name-prefix/data.csv.out</code>. Batch transform doesn't upload partially processed objects. For an input S3 object that contains multiple records, it creates an .<code>out</code> file only if the transform job succeeds on the entire file. When the input contains multiple S3 objects, the batch transform job processes the listed S3 objects and uploads only the output for successfully processed objects. If any object fails in the transform job batch transform marks the job as failed to prompt investigation.</p>",
         "TransformS3DataSource$S3Uri": "<p>Depending on the value specified for the <code>S3DataType</code>, identifies either a key name prefix or a manifest. For example:</p> <ul> <li> <p> A key name prefix might look like this: <code>s3://bucketname/exampleprefix</code>. </p> </li> <li> <p> A manifest might look like this: <code>s3://bucketname/example.manifest</code> </p> <p> The manifest is an S3 object which is a JSON file with the following format: </p> <p> <code>[ {\"prefix\": \"s3://customer_bucket/some/prefix/\"},</code> </p> <p> <code>\"relative/path/to/custdata-1\",</code> </p> <p> <code>\"relative/path/custdata-2\",</code> </p> <p> <code>...</code> </p> <p> <code>\"relative/path/custdata-N\"</code> </p> <p> <code>]</code> </p> <p> The preceding JSON matches the following <code>S3Uris</code>: </p> <p> <code>s3://customer_bucket/some/prefix/relative/path/to/custdata-1</code> </p> <p> <code>s3://customer_bucket/some/prefix/relative/path/custdata-2</code> </p> <p> <code>...</code> </p> <p> <code>s3://customer_bucket/some/prefix/relative/path/custdata-N</code> </p> <p> The complete set of <code>S3Uris</code> in this manifest constitutes the input data for the channel for this datasource. The object that each <code>S3Uris</code> points to must be readable by the IAM role that Amazon SageMaker uses to perform tasks on your behalf.</p> </li> </ul>",
-        "UiConfig$UiTemplateS3Uri": "<p>The Amazon S3 bucket location of the UI template, or worker task template. This is the template used to render the worker UI and tools for labeling job tasks. For more information about the contents of a UI template, see <a href=\"https://docs.aws.amazon.com/sagemaker/latest/dg/sms-custom-templates-step2.html\"> Creating Your Custom Labeling Task Template</a>.</p>"
+        "UiConfig$UiTemplateS3Uri": "<p>The Amazon S3 bucket location of the UI template, or worker task template. This is the template used to render the worker UI and tools for labeling job tasks. For more information about the contents of a UI template, see <a href=\"https://docs.aws.amazon.com/sagemaker/latest/dg/sms-custom-templates-step2.html\"> Creating Your Custom Labeling Task Template</a>.</p>",
+        "WorkspaceSettings$S3ArtifactPath": "<p>The Amazon S3 bucket used to store artifacts generated by Canvas. Updating the Amazon S3 location impacts existing configuration settings, and Canvas users no longer have access to their artifacts. Canvas users must log out and log back in to apply the new location.</p>"
       }
     },
     "SageMakerImageVersionAlias": {
@@ -15142,6 +15144,12 @@
         "ListWorkforcesResponse$Workforces": "<p>A list containing information about your workforce.</p>"
       }
     },
+    "WorkspaceSettings": {
+      "base": "<p>The workspace settings for the SageMaker Canvas application.</p>",
+      "refs": {
+        "CanvasAppSettings$WorkspaceSettings": "<p>The workspace settings for the SageMaker Canvas application.</p>"
+      }
+    },
     "Workteam": {
       "base": "<p>Provides details about a labeling work team.</p>",
       "refs": {