refreshing markdown

doc_source/API_CompilationJobSummary.md

@@ -37,7 +37,7 @@ Required: No
  **CompilationTargetDevice**   <a name="SageMaker-Type-CompilationJobSummary-CompilationTargetDevice"></a>
 The type of device that the model will run on after compilation has completed\.  
 Type: String  
-Valid Values:` lambda | ml_m4 | ml_m5 | ml_c4 | ml_c5 | ml_p2 | ml_p3 | jetson_tx1 | jetson_tx2 | jetson_nano | rasp3b | deeplens | rk3399 | rk3288`   
+Valid Values:` lambda | ml_m4 | ml_m5 | ml_c4 | ml_c5 | ml_p2 | ml_p3 | jetson_tx1 | jetson_tx2 | jetson_nano | rasp3b | deeplens | rk3399 | rk3288 | sbe_c`   
 Required: Yes
 
  **CreationTime**   <a name="SageMaker-Type-CompilationJobSummary-CreationTime"></a>

doc_source/API_ContinuousParameterRange.md

@@ -32,7 +32,7 @@ Amazon SageMaker hyperparameter tuning chooses the best scale for the hyperparam
 Linear  
 Hyperparameter tuning searches the values in the hyperparameter range by using a linear scale\.  
 Logarithmic  
-Hyperparemeter tuning searches the values in the hyperparameter range by using a logarithmic scale\.  
+Hyperparameter tuning searches the values in the hyperparameter range by using a logarithmic scale\.  
 Logarithmic scaling works only for ranges that have only values greater than 0\.  
 ReverseLogarithmic  
 Hyperparemeter tuning searches the values in the hyperparameter range by using a reverse logarithmic scale\.  

doc_source/API_CreateCompilationJob.md

@@ -72,7 +72,7 @@ Pattern: `^arn:aws[a-z\-]*:iam::\d{12}:role/?[a-zA-Z_0-9+=,.@\-_/]+$`
 Required: Yes
 
  ** [StoppingCondition](#API_CreateCompilationJob_RequestSyntax) **   <a name="SageMaker-CreateCompilationJob-request-StoppingCondition"></a>
-The duration allowed for model compilation\.  
+Specifies a limit to how long a model compilation job can run\. When the job reaches the time limit, Amazon SageMaker ends the compilation job\. Use this API to cap model training costs\.  
 Type: [StoppingCondition](API_StoppingCondition.md) object  
 Required: Yes
 

doc_source/API_CreatePresignedNotebookInstanceUrl.md

@@ -2,7 +2,7 @@
 
 Returns a URL that you can use to connect to the Jupyter server from a notebook instance\. In the Amazon SageMaker console, when you choose `Open` next to a notebook instance, Amazon SageMaker opens a new tab showing the Jupyter server home page from the notebook instance\. The console uses this API to get the URL and show the page\.
 
-You can restrict access to this API and to the URL that it returns to a list of IP addresses that you specify\. To restrict access, attach an IAM policy that denies access to this API unless the call comes from an IP address in the specified list to every AWS Identity and Access Management user, group, or role used to access the notebook instance\. Use the `NotIpAddress` condition operator and the `aws:SourceIP` condition context key to specify the list of IP addresses that you want to have access to the notebook instance\. For more information, see [Limit Access to a Notebook Instance by IP Address](https://docs.aws.amazon.com/sagemaker/latest/dg/nbi-ip-filter.html)\.
+IAM authorization policies for this API are also enforced for every HTTP request and WebSocket frame that attempts to connect to the notebook instance\.For example, you can restrict access to this API and to the URL that it returns to a list of IP addresses that you specify\. Use the `NotIpAddress` condition operator and the `aws:SourceIP` condition context key to specify the list of IP addresses that you want to have access to the notebook instance\. For more information, see [Limit Access to a Notebook Instance by IP Address](https://docs.aws.amazon.com/sagemaker/latest/dg/nbi-ip-filter.html)\.
 
 **Note**  
 The URL that you get from a call to [CreatePresignedNotebookInstanceUrl](#API_CreatePresignedNotebookInstanceUrl) is valid only for 5 minutes\. If you try to use the URL after the 5\-minute limit expires, you are directed to the AWS console sign\-in page\.

doc_source/API_CreateTrainingJob.md

@@ -6,14 +6,14 @@ If you choose to host your model using Amazon SageMaker hosting services, you ca
 
 In the request body, you provide the following: 
 +  `AlgorithmSpecification` \- Identifies the training algorithm to use\. 
-+  `HyperParameters` \- Specify these algorithm\-specific parameters to influence the quality of the final model\. For a list of hyperparameters for each training algorithm provided by Amazon SageMaker, see [Algorithms](https://docs.aws.amazon.com/sagemaker/latest/dg/algos.html)\. 
++  `HyperParameters` \- Specify these algorithm\-specific parameters to enable the estimation of model parameters during training\. Hyperparameters can be tuned to optimize this learning process\. For a list of hyperparameters for each training algorithm provided by Amazon SageMaker, see [Algorithms](https://docs.aws.amazon.com/sagemaker/latest/dg/algos.html)\. 
 +  `InputDataConfig` \- Describes the training dataset and the Amazon S3 location where it is stored\.
 +  `OutputDataConfig` \- Identifies the Amazon S3 location where you want Amazon SageMaker to save the results of model training\. 
 
 
 +  `ResourceConfig` \- Identifies the resources, ML compute instances, and ML storage volumes to deploy for model training\. In distributed training, you specify more than one instance\. 
 +  `RoleARN` \- The Amazon Resource Number \(ARN\) that Amazon SageMaker assumes to perform tasks on your behalf during model training\. You must grant this role the necessary permissions so that Amazon SageMaker can successfully complete model training\. 
-+  `StoppingCondition` \- Sets a duration for training\. Use this parameter to cap model training costs\. 
++  `StoppingCondition` \- Sets a time limit for training\. Use this parameter to cap model training costs\. 
 
  For more information about Amazon SageMaker, see [How It Works](https://docs.aws.amazon.com/sagemaker/latest/dg/how-it-works.html)\. 
 
@@ -122,7 +122,7 @@ An array of `Channel` objects\. Each channel is a named input source\. `InputDat
 Algorithms can accept input data from one or more channels\. For example, an algorithm might have two channels of input data, `training_data` and `validation_data`\. The configuration for each channel provides the S3 location where the input data is stored\. It also provides information about the stored data: the MIME type, compression method, and whether the data is wrapped in RecordIO format\.   
 Depending on the input mode that the algorithm supports, Amazon SageMaker either copies input data files from an S3 bucket to a local directory in the Docker container, or makes it available as input streams\.   
 Type: Array of [Channel](API_Channel.md) objects  
-Array Members: Minimum number of 1 item\. Maximum number of 8 items\.  
+Array Members: Minimum number of 1 item\. Maximum number of 20 items\.  
 Required: No
 
  ** [OutputDataConfig](#API_CreateTrainingJob_RequestSyntax) **   <a name="SageMaker-CreateTrainingJob-request-OutputDataConfig"></a>
@@ -146,8 +146,8 @@ Pattern: `^arn:aws[a-z\-]*:iam::\d{12}:role/?[a-zA-Z_0-9+=,.@\-_/]+$`
 Required: Yes
 
  ** [StoppingCondition](#API_CreateTrainingJob_RequestSyntax) **   <a name="SageMaker-CreateTrainingJob-request-StoppingCondition"></a>
-Sets a duration for training\. Use this parameter to cap model training costs\. To stop a job, Amazon SageMaker sends the algorithm the `SIGTERM` signal, which delays job termination for 120 seconds\. Algorithms might use this 120\-second window to save the model artifacts\.   
-When Amazon SageMaker terminates a job because the stopping condition has been met, training algorithms provided by Amazon SageMaker save the intermediate results of the job\. This intermediate data is a valid model artifact\. You can use it to create a model using the `CreateModel` API\.   
+Specifies a limit to how long a model training job can run\. When the job reaches the time limit, Amazon SageMaker ends the training job\. Use this API to cap model training costs\.  
+To stop a job, Amazon SageMaker sends the algorithm the `SIGTERM` signal, which delays job termination for 120 seconds\. Algorithms can use this 120\-second window to save the model artifacts, so the results of training are not lost\.   
 Type: [StoppingCondition](API_StoppingCondition.md) object  
 Required: Yes
 

doc_source/API_CreateTransformJob.md

@@ -18,6 +18,11 @@ In the request body, you provide the following:
 ```
 {
    "[BatchStrategy](#SageMaker-CreateTransformJob-request-BatchStrategy)": "string",
+   "[DataProcessing](#SageMaker-CreateTransformJob-request-DataProcessing)": { 
+      "[InputFilter](API_DataProcessing.md#SageMaker-Type-DataProcessing-InputFilter)": "string",
+      "[JoinSource](API_DataProcessing.md#SageMaker-Type-DataProcessing-JoinSource)": "string",
+      "[OutputFilter](API_DataProcessing.md#SageMaker-Type-DataProcessing-OutputFilter)": "string"
+   },
    "[Environment](#SageMaker-CreateTransformJob-request-Environment)": { 
       "string" : "string" 
    },
@@ -69,6 +74,11 @@ To use only one record when making an HTTP invocation request to a container, se
 To fit as many records in a mini\-batch as can fit within the `MaxPayloadInMB` limit, set `BatchStrategy` to `MultiRecord` and `SplitType` to `Line`\.  
 Type: String  
 Valid Values:` MultiRecord | SingleRecord`   
+Required: No
+
+ ** [DataProcessing](#API_CreateTransformJob_RequestSyntax) **   <a name="SageMaker-CreateTransformJob-request-DataProcessing"></a>
+The data structure used to specify the data to be used for inference in a batch transform job and to associate the data that is relevant to the prediction results in the output\. The input filter provided allows you to exclude input data that is not needed for inference in a batch transform job\. The output filter provided allows you to include input data relevant to interpreting the predictions in the output from the job\. For more information, see [Associate Prediction Results with their Corresponding Input Records](http://docs.aws.amazon.com/sagemaker/latest/dg/batch-transform-data-processing.html)\.  
+Type: [DataProcessing](API_DataProcessing.md) object  
 Required: No
 
  ** [Environment](#API_CreateTransformJob_RequestSyntax) **   <a name="SageMaker-CreateTransformJob-request-Environment"></a>

doc_source/API_DataProcessing.md

@@ -0,0 +1,36 @@
+# DataProcessing<a name="API_DataProcessing"></a>
+
+The data structure used to specify the data to be used for inference in a batch transform job and to associate the data that is relevant to the prediction results in the output\. The input filter provided allows you to exclude input data that is not needed for inference in a batch transform job\. The output filter provided allows you to include input data relevant to interpreting the predictions in the output from the job\. For more information, see [Associate Prediction Results with their Corresponding Input Records](http://docs.aws.amazon.com/sagemaker/latest/dg/batch-transform-data-processing.html)\.
+
+## Contents<a name="API_DataProcessing_Contents"></a>
+
+ **InputFilter**   <a name="SageMaker-Type-DataProcessing-InputFilter"></a>
+A [JSONPath](http://docs.aws.amazon.com/sagemaker/latest/dg/batch-transform-data-processing.html#data-processing-operators) expression used to select a portion of the input data to pass to the algorithm\. Use the `InputFilter` parameter to exclude fields, such as an ID column, from the input\. If you want Amazon SageMaker to pass the entire input dataset to the algorithm, accept the default value `$`\.  
+Examples: `"$"`, `"$[1:]"`, `"$.features"`   
+Type: String  
+Length Constraints: Minimum length of 0\. Maximum length of 63\.  
+Required: No
+
+ **JoinSource**   <a name="SageMaker-Type-DataProcessing-JoinSource"></a>
+Specifies the source of the data to join with the transformed data\. The valid values are `None` and `Input` The default value is `None` which specifies not to join the input with the transformed data\. If you want the batch transform job to join the original input data with the transformed data, set `JoinSource` to `Input`\.   
+For JSON or JSONLines objects, such as a JSON array, Amazon SageMaker adds the transformed data to the input JSON object in an attribute called `SageMakerOutput`\. The joined result for JSON must be a key\-value pair object\. If the input is not a key\-value pair object, Amazon SageMaker creates a new JSON file\. In the new JSON file, and the input data is stored under the `SageMakerInput` key and the results are stored in `SageMakerOutput`\.  
+For CSV files, Amazon SageMaker combines the transformed data with the input data at the end of the input data and stores it in the output file\. The joined data has the joined input data followed by the transformed data and the output is a CSV file\.   
+Type: String  
+Valid Values:` Input | None`   
+Required: No
+
+ **OutputFilter**   <a name="SageMaker-Type-DataProcessing-OutputFilter"></a>
+A [JSONPath](http://docs.aws.amazon.com/sagemaker/latest/dg/batch-transform-data-processing.html#data-processing-operators) expression used to select a portion of the joined dataset to save in the output file for a batch transform job\. If you want Amazon SageMaker to store the entire input dataset in the output file, leave the default value, `$`\. If you specify indexes that aren't within the dimension size of the joined dataset, you get an error\.  
+Examples: `"$"`, `"$[0,5:]"`, `"$['id','SageMakerOutput']"`   
+Type: String  
+Length Constraints: Minimum length of 0\. Maximum length of 63\.  
+Required: No
+
+## See Also<a name="API_DataProcessing_SeeAlso"></a>
+
+For more information about using this API in one of the language\-specific AWS SDKs, see the following:
++  [AWS SDK for C\+\+](https://docs.aws.amazon.com/goto/SdkForCpp/sagemaker-2017-07-24/DataProcessing) 
++  [AWS SDK for Go](https://docs.aws.amazon.com/goto/SdkForGoV1/sagemaker-2017-07-24/DataProcessing) 
++  [AWS SDK for Go \- Pilot](https://docs.aws.amazon.com/goto/SdkForGoPilot/sagemaker-2017-07-24/DataProcessing) 
++  [AWS SDK for Java](https://docs.aws.amazon.com/goto/SdkForJava/sagemaker-2017-07-24/DataProcessing) 
++  [AWS SDK for Ruby V2](https://docs.aws.amazon.com/goto/SdkForRubyV2/sagemaker-2017-07-24/DataProcessing) 

doc_source/API_DescribeCompilationJob.md

@@ -120,7 +120,7 @@ Length Constraints: Minimum length of 20\. Maximum length of 2048\.
 Pattern: `^arn:aws[a-z\-]*:iam::\d{12}:role/?[a-zA-Z_0-9+=,.@\-_/]+$` 
 
  ** [StoppingCondition](#API_DescribeCompilationJob_ResponseSyntax) **   <a name="SageMaker-DescribeCompilationJob-response-StoppingCondition"></a>
-The duration allowed for model compilation\.  
+Specifies a limit to how long a model compilation job can run\. When the job reaches the time limit, Amazon SageMaker ends the compilation job\. Use this API to cap model training costs\.  
 Type: [StoppingCondition](API_StoppingCondition.md) object
 
 ## Errors<a name="API_DescribeCompilationJob_Errors"></a>

doc_source/API_DescribeTrainingJob.md

@@ -128,7 +128,7 @@ A timestamp that indicates when the training job was created\.
 Type: Timestamp
 
  ** [EnableInterContainerTrafficEncryption](#API_DescribeTrainingJob_ResponseSyntax) **   <a name="SageMaker-DescribeTrainingJob-response-EnableInterContainerTrafficEncryption"></a>
-To encrypt all communications between ML compute instances in distributed training, choose `True`\. Encryption provides greater security for distributed training, but training might take longer\. How long it takes depends on the amount of communication between compute instances, especially if you use a deep learning algorithm in distributed training\.  
+To encrypt all communications between ML compute instances in distributed training, choose `True`\. Encryption provides greater security for distributed training, but training might take longer\. How long it takes depends on the amount of communication between compute instances, especially if you use a deep learning algorithms in distributed training\.  
 Type: Boolean
 
  ** [EnableNetworkIsolation](#API_DescribeTrainingJob_ResponseSyntax) **   <a name="SageMaker-DescribeTrainingJob-response-EnableNetworkIsolation"></a>
@@ -157,7 +157,7 @@ Value Pattern: `.*`
  ** [InputDataConfig](#API_DescribeTrainingJob_ResponseSyntax) **   <a name="SageMaker-DescribeTrainingJob-response-InputDataConfig"></a>
 An array of `Channel` objects that describes each data input channel\.   
 Type: Array of [Channel](API_Channel.md) objects  
-Array Members: Minimum number of 1 item\. Maximum number of 8 items\.
+Array Members: Minimum number of 1 item\. Maximum number of 20 items\.
 
  ** [LabelingJobArn](#API_DescribeTrainingJob_ResponseSyntax) **   <a name="SageMaker-DescribeTrainingJob-response-LabelingJobArn"></a>
 The Amazon Resource Name \(ARN\) of the Amazon SageMaker Ground Truth labeling job that created the transform or training job\.  
@@ -217,7 +217,8 @@ A history of all of the secondary statuses that the training job has transitione
 Type: Array of [SecondaryStatusTransition](API_SecondaryStatusTransition.md) objects
 
  ** [StoppingCondition](#API_DescribeTrainingJob_ResponseSyntax) **   <a name="SageMaker-DescribeTrainingJob-response-StoppingCondition"></a>
-The condition under which to stop the training job\.   
+Specifies a limit to how long a model training job can run\. When the job reaches the time limit, Amazon SageMaker ends the training job\. Use this API to cap model training costs\.  
+To stop a job, Amazon SageMaker sends the algorithm the `SIGTERM` signal, which delays job termination for 120 seconds\. Algorithms can use this 120\-second window to save the model artifacts, so the results of training are not lost\.   
 Type: [StoppingCondition](API_StoppingCondition.md) object
 
  ** [TrainingEndTime](#API_DescribeTrainingJob_ResponseSyntax) **   <a name="SageMaker-DescribeTrainingJob-response-TrainingEndTime"></a>