Updates SDK to v2.921.0

Author: awstools

.changes/2.921.0.json

@@ -0,0 +1,17 @@
+[
+    {
+        "type": "feature",
+        "category": "ForecastService",
+        "description": "Added optional field AutoMLOverrideStrategy to CreatePredictor API that allows users to customize AutoML strategy. If provided in CreatePredictor request, this field is visible in DescribePredictor and GetAccuracyMetrics responses."
+    },
+    {
+        "type": "feature",
+        "category": "S3",
+        "description": "S3 Inventory now supports Bucket Key Status"
+    },
+    {
+        "type": "feature",
+        "category": "S3Control",
+        "description": "Amazon S3 Batch Operations now supports S3 Bucket Keys."
+    }
+]

CHANGELOG.md

@@ -1,7 +1,12 @@
 # Changelog for AWS SDK for JavaScript
-<!--LATEST=2.920.0-->
+<!--LATEST=2.921.0-->
 <!--ENTRYINSERT-->
 
+## 2.921.0
+* feature: ForecastService: Added optional field AutoMLOverrideStrategy to CreatePredictor API that allows users to customize AutoML strategy. If provided in CreatePredictor request, this field is visible in DescribePredictor and GetAccuracyMetrics responses.
+* feature: S3: S3 Inventory now supports Bucket Key Status
+* feature: S3Control: Amazon S3 Batch Operations now supports S3 Bucket Keys.
+
 ## 2.920.0
 * feature: AutoScaling: You can now launch EC2 instances with GP3 volumes when using Auto Scaling groups with Launch Configurations
 * feature: Braket: Introduction of a RETIRED status for devices.

README.md

@@ -29,7 +29,7 @@ For release notes, see the [CHANGELOG](https://github.com/aws/aws-sdk-js/blob/ma
 To use the SDK in the browser, simply add the following script tag to your
 HTML pages:
 
-    <script src="https://sdk.amazonaws.com/js/aws-sdk-2.920.0.min.js"></script>
+    <script src="https://sdk.amazonaws.com/js/aws-sdk-2.921.0.min.js"></script>
 
 You can also build a custom browser SDK with your specified set of AWS services.
 This can allow you to reduce the SDK's size, specify different API versions of

apis/forecast-2018-06-26.min.json

@@ -174,23 +174,24 @@
           "PerformAutoML": {
             "type": "boolean"
           },
+          "AutoMLOverrideStrategy": {},
           "PerformHPO": {
             "type": "boolean"
           },
           "TrainingParameters": {
-            "shape": "S14"
+            "shape": "S15"
           },
           "EvaluationParameters": {
-            "shape": "S17"
+            "shape": "S18"
           },
           "HPOConfig": {
-            "shape": "S18"
+            "shape": "S19"
           },
           "InputDataConfig": {
-            "shape": "S1k"
+            "shape": "S1l"
           },
           "FeaturizationConfig": {
-            "shape": "S1n"
+            "shape": "S1o"
           },
           "EncryptionConfig": {
             "shape": "Sa"
@@ -569,23 +570,24 @@
           "PerformAutoML": {
             "type": "boolean"
           },
+          "AutoMLOverrideStrategy": {},
           "PerformHPO": {
             "type": "boolean"
           },
           "TrainingParameters": {
-            "shape": "S14"
+            "shape": "S15"
           },
           "EvaluationParameters": {
-            "shape": "S17"
+            "shape": "S18"
           },
           "HPOConfig": {
-            "shape": "S18"
+            "shape": "S19"
           },
           "InputDataConfig": {
-            "shape": "S1k"
+            "shape": "S1l"
           },
           "FeaturizationConfig": {
-            "shape": "S1n"
+            "shape": "S1o"
           },
           "EncryptionConfig": {
             "shape": "Sa"
@@ -750,7 +752,8 @@
                 }
               }
             }
-          }
+          },
+          "AutoMLOverrideStrategy": {}
         }
       },
       "idempotent": true
@@ -798,7 +801,7 @@
             "type": "integer"
           },
           "Filters": {
-            "shape": "S3g"
+            "shape": "S3h"
           }
         }
       },
@@ -876,7 +879,7 @@
             "type": "integer"
           },
           "Filters": {
-            "shape": "S3g"
+            "shape": "S3h"
           }
         }
       },
@@ -918,7 +921,7 @@
             "type": "integer"
           },
           "Filters": {
-            "shape": "S3g"
+            "shape": "S3h"
           }
         }
       },
@@ -959,7 +962,7 @@
             "type": "integer"
           },
           "Filters": {
-            "shape": "S3g"
+            "shape": "S3h"
           }
         }
       },
@@ -1001,7 +1004,7 @@
             "type": "integer"
           },
           "Filters": {
-            "shape": "S3g"
+            "shape": "S3h"
           }
         }
       },
@@ -1217,12 +1220,12 @@
         }
       }
     },
-    "S14": {
+    "S15": {
       "type": "map",
       "key": {},
       "value": {}
     },
-    "S17": {
+    "S18": {
       "type": "structure",
       "members": {
         "NumberOfBacktestWindows": {
@@ -1233,7 +1236,7 @@
         }
       }
     },
-    "S18": {
+    "S19": {
       "type": "structure",
       "members": {
         "ParameterRanges": {
@@ -1302,7 +1305,7 @@
         }
       }
     },
-    "S1k": {
+    "S1l": {
       "type": "structure",
       "required": [
         "DatasetGroupArn"
@@ -1325,7 +1328,7 @@
         }
       }
     },
-    "S1n": {
+    "S1o": {
       "type": "structure",
       "required": [
         "ForecastFrequency"
@@ -1367,7 +1370,7 @@
         }
       }
     },
-    "S3g": {
+    "S3h": {
       "type": "list",
       "member": {
         "type": "structure",

apis/forecast-2018-06-26.normal.json

@@ -891,6 +891,12 @@
         "geolocation"
       ]
     },
+    "AutoMLOverrideStrategy": {
+      "type": "string",
+      "enum": [
+        "LatencyOptimized"
+      ]
+    },
     "Boolean": {
       "type": "boolean"
     },
@@ -1220,6 +1226,10 @@
           "shape": "Boolean",
           "documentation": "<p>Whether to perform AutoML. When Amazon Forecast performs AutoML, it evaluates the algorithms it provides and chooses the best algorithm and configuration for your training dataset.</p> <p>The default value is <code>false</code>. In this case, you are required to specify an algorithm.</p> <p>Set <code>PerformAutoML</code> to <code>true</code> to have Amazon Forecast perform AutoML. This is a good option if you aren't sure which algorithm is suitable for your training data. In this case, <code>PerformHPO</code> must be false.</p>"
         },
+        "AutoMLOverrideStrategy": {
+          "shape": "AutoMLOverrideStrategy",
+          "documentation": "<p>Used to overide the default AutoML strategy, which is to optimize predictor accuracy. To apply an AutoML strategy that minimizes training time, use <code>LatencyOptimized</code>.</p> <p>This parameter is only valid for predictors trained using AutoML.</p>"
+        },
         "PerformHPO": {
           "shape": "Boolean",
           "documentation": "<p>Whether to perform hyperparameter optimization (HPO). HPO finds optimal hyperparameter values for your training data. The process of performing HPO is known as running a hyperparameter tuning job.</p> <p>The default value is <code>false</code>. In this case, Amazon Forecast uses default hyperparameter values from the chosen algorithm.</p> <p>To override the default values, set <code>PerformHPO</code> to <code>true</code> and, optionally, supply the <a>HyperParameterTuningJobConfig</a> object. The tuning job specifies a metric to optimize, which hyperparameters participate in tuning, and the valid range for each tunable hyperparameter. In this case, you are required to specify an algorithm and <code>PerformAutoML</code> must be false.</p> <p>The following algorithms support HPO:</p> <ul> <li> <p>DeepAR+</p> </li> <li> <p>CNN-QR</p> </li> </ul>"
@@ -1869,6 +1879,10 @@
           "shape": "Boolean",
           "documentation": "<p>Whether the predictor is set to perform AutoML.</p>"
         },
+        "AutoMLOverrideStrategy": {
+          "shape": "AutoMLOverrideStrategy",
+          "documentation": "<p>The AutoML strategy used to train the predictor. Unless <code>LatencyOptimized</code> is specified, the AutoML strategy optimizes predictor accuracy.</p> <p>This parameter is only valid for predictors trained using AutoML.</p>"
+        },
         "PerformHPO": {
           "shape": "Boolean",
           "documentation": "<p>Whether the predictor is set to perform hyperparameter optimization (HPO).</p>"
@@ -2290,6 +2304,10 @@
         "PredictorEvaluationResults": {
           "shape": "PredictorEvaluationResults",
           "documentation": "<p>An array of results from evaluating the predictor.</p>"
+        },
+        "AutoMLOverrideStrategy": {
+          "shape": "AutoMLOverrideStrategy",
+          "documentation": "<p>The AutoML strategy used to train the predictor. Unless <code>LatencyOptimized</code> is specified, the AutoML strategy optimizes predictor accuracy.</p> <p>This parameter is only valid for predictors trained using AutoML.</p>"
         }
       }
     },