{"id":45,"date":"2020-08-17T07:53:41","date_gmt":"2020-08-17T07:53:41","guid":{"rendered":"https:\/\/machine-learning.webcloning.com\/2020\/08\/17\/accessing-data-sources-from-amazon-sagemaker-r-kernels\/"},"modified":"2020-08-17T07:53:41","modified_gmt":"2020-08-17T07:53:41","slug":"accessing-data-sources-from-amazon-sagemaker-r-kernels","status":"publish","type":"post","link":"https:\/\/salarydistribution.com\/machine-learning\/2020\/08\/17\/accessing-data-sources-from-amazon-sagemaker-r-kernels\/","title":{"rendered":"Accessing data sources from Amazon SageMaker R kernels"},"content":{"rendered":"
\n

Amazon SageMaker<\/a> notebooks now support R out-of-the-box<\/a>, without needing you to manually install R kernels<\/a> on the instances. Also, the notebooks come pre-installed with the reticulate<\/a> library, which offers an R interface for the Amazon SageMaker Python SDK<\/a> and enables you to invoke Python modules from within an R script. You can easily run machine learning (ML) models in R using the Amazon SageMaker R kernel to access the data from multiple data sources. The R kernel is available by default in all Regions that Amazon SageMaker is available in.<\/p>\n

R<\/a> is a programming language built for statistical analysis and is very popular in data science communities. In this post, we will show you how to connect to the following data sources from the Amazon SageMaker R kernel using Java Database Connectivity (JDBC)<\/a>:<\/p>\n

For more information about using Amazon SageMaker features using R, see R User Guide to Amazon SageMaker<\/a>.<\/p>\n

Solution overview<\/h2>\n

To build this solution, we first need to create a VPC with public and private subnets<\/a>. This will allow us to securely communicate with different resources and data sources inside an isolated network. Next, we create the data sources in the custom VPC and the notebook instance with all necessary configuration and access to connect various data sources using R.<\/p>\n

To make sure that the data sources are not reachable from the Internet, we create them inside a private subnet of the VPC. For this post, we create the following:<\/p>\n

Connect to the Amazon EMR cluster inside the private subnet using AWS Systems Manager Session Manager<\/a> to create Hive tables.<\/p>\n

To run the code using the R kernel in Amazon SageMaker, create an Amazon SageMaker notebook. Download the JDBC drivers for the data sources. Create a lifecycle configuration for the notebook<\/a> containing the setup script for R packages, and attach the lifecycle configuration to the notebook on create and on start to make sure the setup is complete.<\/p>\n

Finally, we can use the AWS Management Console<\/a> to navigate to the notebook to run code using the R kernel and access the data from various sources. The entire solution is also available in the GitHub repository<\/a>.<\/p>\n

Solution architecture<\/h2>\n

The following architecture diagram shows how you can use Amazon SageMaker to run code using the R kernel by establishing connectivity to various sources. You can also use the Amazon Redshift query editor or Amazon Athena query editor to create data resources. You need to use the Session Manager in AWS Systems Manager<\/a> to SSH to the Amazon EMR cluster to create Hive resources.<\/p>\n

\"\"<\/p>\n

Launching the AWS CloudFormation template<\/h2>\n

To automate resource creation, you run an AWS CloudFormation<\/a> template. The template gives you the option to create an Amazon EMR cluster, Amazon Redshift cluster, or Amazon Aurora MySQL-compatible cluster automatically, as opposed to executing each step manually. It will take a few minutes to create all the resources.<\/p>\n

    \n
  1. Choose the following link to launch the CloudFormation stack, which creates the required AWS resources to implement this solution:
    \"\"<\/a>\n<\/li>\n
  2. On the Create stack<\/strong> page, choose Next<\/strong>.
    \"\"\n<\/li>\n
  3. Enter a stack name.<\/li>\n
  4. You can change the default values for the following stack details:<\/li>\n<\/ol>\n\n\n\n\n\n\n\n\n
    Stack Details<\/em><\/strong><\/td>\nDefault Values<\/em><\/strong><\/td>\n<\/tr>\n
    Choose Second Octet for Class B VPC Address (10.xxx.0.0\/16)<\/td>\n0<\/td>\n<\/tr>\n
    SageMaker Jupyter Notebook Instance Type<\/td>\nml.t2.medium<\/td>\n<\/tr>\n
    Create EMR Cluster Automatically?<\/td>\n\u201cYes\u201d<\/td>\n<\/tr>\n
    Create Redshift Cluster Automatically?<\/td>\n\u201cYes\u201d<\/td>\n<\/tr>\n
    Create Aurora MySQL DB Cluster Automatically?<\/td>\n\u201cYes\u201d<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n
      \n
    1. Choose Next<\/strong>.<\/li>\n
    2. On the Configure stack options<\/strong> page, choose Next<\/strong>.<\/li>\n
    3. Select I acknowledge that AWS CloudFormation might create IAM resources<\/strong>.<\/li>\n
    4. Choose Create stack<\/strong>.<\/li>\n<\/ol>\n

      \"\"<\/p>\n

      You can now see the stack being created, as in the following screenshot.<\/p>\n

      \"\"<\/p>\n

      When stack creation is complete, the status shows as CREATE_COMPLETE<\/code>.<\/p>\n

      \"\"<\/p>\n

        \n
      1. On the Outputs<\/strong> tab, record the keys and their corresponding values.<\/li>\n<\/ol>\n

        \"\"<\/p>\n

        You use the following keys later in this post:<\/p>\n