MachineLearning.Client.create_data_source_from_rds(**kwargs)¶Creates a DataSource object from an Amazon Relational Database Service (Amazon RDS). A DataSource references data that can be used to perform CreateMLModel , CreateEvaluation , or CreateBatchPrediction operations.
CreateDataSourceFromRDSis an asynchronous operation. In response toCreateDataSourceFromRDS, Amazon Machine Learning (Amazon ML) immediately returns and sets theDataSourcestatus toPENDING. After theDataSourceis created and ready for use, Amazon ML sets theStatusparameter toCOMPLETED.DataSourcein theCOMPLETEDorPENDINGstate can be used only to perform>CreateMLModel>,CreateEvaluation, orCreateBatchPredictionoperations.
If Amazon ML cannot accept the input source, it sets the Status parameter to FAILED and includes an error message in the Message attribute of the GetDataSource operation response.
See also: AWS API Documentation
Request Syntax
response = client.create_data_source_from_rds(
DataSourceId='string',
DataSourceName='string',
RDSData={
'DatabaseInformation': {
'InstanceIdentifier': 'string',
'DatabaseName': 'string'
},
'SelectSqlQuery': 'string',
'DatabaseCredentials': {
'Username': 'string',
'Password': 'string'
},
'S3StagingLocation': 'string',
'DataRearrangement': 'string',
'DataSchema': 'string',
'DataSchemaUri': 'string',
'ResourceRole': 'string',
'ServiceRole': 'string',
'SubnetId': 'string',
'SecurityGroupIds': [
'string',
]
},
RoleARN='string',
ComputeStatistics=True|False
)
[REQUIRED]
A user-supplied ID that uniquely identifies the DataSource . Typically, an Amazon Resource Number (ARN) becomes the ID for a DataSource .
DataSource .[REQUIRED]
The data specification of an Amazon RDS DataSource :
DatabaseName - The name of the Amazon RDS database.InstanceIdentifier - A unique identifier for the Amazon RDS database instance.SubnetId , SecurityGroupIds ] pair for a VPC-based RDS DB instance.Datasource .SelectSqlQuery is stored in this location.DataSchema .DataSchemaUri is specified.Datasource . Sample - "{\"splitting\":{\"percentBegin\":10,\"percentEnd\":60}}"Describes the DatabaseName and InstanceIdentifier of an Amazon RDS database.
The ID of an RDS DB instance.
The name of a database hosted on an RDS DB instance.
The query that is used to retrieve the observation data for the DataSource .
The AWS Identity and Access Management (IAM) credentials that are used connect to the Amazon RDS database.
The username to be used by Amazon ML to connect to database on an Amazon RDS instance. The username should have sufficient permissions to execute an RDSSelectSqlQuery query.
The password to be used by Amazon ML to connect to a database on an RDS DB instance. The password should have sufficient permissions to execute the RDSSelectQuery query.
The Amazon S3 location for staging Amazon RDS data. The data retrieved from Amazon RDS using SelectSqlQuery is stored in this location.
A JSON string that represents the splitting and rearrangement processing to be applied to a DataSource . If the DataRearrangement parameter is not provided, all of the input data is used to create the Datasource .
There are multiple parameters that control what data is used to create a datasource:
percentBegin Use percentBegin to indicate the beginning of the range of the data used to create the Datasource. If you do not include percentBegin and percentEnd , Amazon ML includes all of the data when creating the datasource.percentEnd Use percentEnd to indicate the end of the range of the data used to create the Datasource. If you do not include percentBegin and percentEnd , Amazon ML includes all of the data when creating the datasource.complement The complement parameter instructs Amazon ML to use the data that is not included in the range of percentBegin to percentEnd to create a datasource. The complement parameter is useful if you need to create complementary datasources for training and evaluation. To create a complementary datasource, use the same values for percentBegin and percentEnd , along with the complement parameter. For example, the following two datasources do not share any data, and can be used to train and evaluate a model. The first datasource has 25 percent of the data, and the second one has 75 percent of the data. Datasource for evaluation: {"splitting":{"percentBegin":0, "percentEnd":25}} Datasource for training: {"splitting":{"percentBegin":0, "percentEnd":25, "complement":"true"}}strategy To change how Amazon ML splits the data for a datasource, use the strategy parameter. The default value for the strategy parameter is sequential , meaning that Amazon ML takes all of the data records between the percentBegin and percentEnd parameters for the datasource, in the order that the records appear in the input data. The following two DataRearrangement lines are examples of sequentially ordered training and evaluation datasources: Datasource for evaluation: {"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"sequential"}} Datasource for training: {"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"sequential", "complement":"true"}} To randomly split the input data into the proportions indicated by the percentBegin and percentEnd parameters, set the strategy parameter to random and provide a string that is used as the seed value for the random data splitting (for example, you can use the S3 path to your data as the random seed string). If you choose the random split strategy, Amazon ML assigns each row of data a pseudo-random number between 0 and 100, and then selects the rows that have an assigned number between percentBegin and percentEnd . Pseudo-random numbers are assigned using both the input seed string value and the byte offset as a seed, so changing the data results in a different split. Any existing ordering is preserved. The random splitting strategy ensures that variables in the training and evaluation data are distributed similarly. It is useful in the cases where the input data may have an implicit sort order, which would otherwise result in training and evaluation datasources containing non-similar data records. The following two DataRearrangement lines are examples of non-sequentially ordered training and evaluation datasources: Datasource for evaluation: {"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"random", "randomSeed"="s3://my_s3_path/bucket/file.csv"}} Datasource for training: {"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"random", "randomSeed"="s3://my_s3_path/bucket/file.csv", "complement":"true"}}A JSON string that represents the schema for an Amazon RDS DataSource . The DataSchema defines the structure of the observation data in the data file(s) referenced in the DataSource .
A DataSchema is not required if you specify a DataSchemaUri
Define your DataSchema as a series of key-value pairs. attributes and excludedVariableNames have an array of key-value pairs for their value. Use the following format to define your DataSchema .
{ "version": "1.0",
"recordAnnotationFieldName": "F1",
"recordWeightFieldName": "F2",
"targetFieldName": "F3",
"dataFormat": "CSV",
"dataFileContainsHeader": true,
"attributes": [
{ "fieldName": "F1", "fieldType": "TEXT" }, { "fieldName": "F2", "fieldType": "NUMERIC" }, { "fieldName": "F3", "fieldType": "CATEGORICAL" }, { "fieldName": "F4", "fieldType": "NUMERIC" }, { "fieldName": "F5", "fieldType": "CATEGORICAL" }, { "fieldName": "F6", "fieldType": "TEXT" }, { "fieldName": "F7", "fieldType": "WEIGHTED_INT_SEQUENCE" }, { "fieldName": "F8", "fieldType": "WEIGHTED_STRING_SEQUENCE" } ],
"excludedVariableNames": [ "F6" ] }
The Amazon S3 location of the DataSchema .
The role (DataPipelineDefaultResourceRole) assumed by an Amazon Elastic Compute Cloud (Amazon EC2) instance to carry out the copy operation from Amazon RDS to an Amazon S3 task. For more information, see Role templates for data pipelines.
The role (DataPipelineDefaultRole) assumed by AWS Data Pipeline service to monitor the progress of the copy task from Amazon RDS to Amazon S3. For more information, see Role templates for data pipelines.
The subnet ID to be used to access a VPC-based RDS DB instance. This attribute is used by Data Pipeline to carry out the copy task from Amazon RDS to Amazon S3.
The security group IDs to be used to access a VPC-based RDS DB instance. Ensure that there are appropriate ingress rules set up to allow access to the RDS DB instance. This attribute is used by Data Pipeline to carry out the copy operation from Amazon RDS to an Amazon S3 task.
[REQUIRED]
The role that Amazon ML assumes on behalf of the user to create and activate a data pipeline in the user's account and copy data using the SelectSqlQuery query from Amazon RDS to Amazon S3.
DataSource . The statistics are generated from the observation data referenced by a DataSource . Amazon ML uses the statistics internally during MLModel training. This parameter must be set to true if the DataSource needs to be used for MLModel training.dict
Response Syntax
{
'DataSourceId': 'string'
}
Response Structure
(dict) --
Represents the output of a CreateDataSourceFromRDS operation, and is an acknowledgement that Amazon ML received the request.
The CreateDataSourceFromRDS > operation is asynchronous. You can poll for updates by using the GetBatchPrediction operation and checking the Status parameter. You can inspect the Message when Status shows up as FAILED . You can also check the progress of the copy operation by going to the DataPipeline console and looking up the pipeline using the pipelineId from the describe call.
DataSourceId (string) --
A user-supplied ID that uniquely identifies the datasource. This value should be identical to the value of the DataSourceID in the request.
Exceptions
MachineLearning.Client.exceptions.InvalidInputExceptionMachineLearning.Client.exceptions.InternalServerExceptionMachineLearning.Client.exceptions.IdempotentParameterMismatchException