R/sklearn_processing.R

In DyfanJones/sagemaker-r-mlframework: sagemaker machine learning developed by amazon

# NOTE: This code has been modified from AWS Sagemaker Python:
# https://github.com/aws/sagemaker-python-sdk/blob/master/src/sagemaker/sklearn/processing.py

#' @include sklearn_estimator.R

#' @import R6
#' @import sagemaker.core
#' @import sagemaker.common

#' @title SKLearnProcessor Class
#' @description Handles Amazon SageMaker processing tasks for jobs using scikit-learn.
#' @export
SKLearnProcessor = R6Class("SKLearnProcessor",
  inherit = sagemaker.common::FrameworkProcessor,
  public = list(

    #' @field estimator_cls
    #' Estimator object
    estimator_cls = SKLearn,

    #' @description Initialize an ``SKLearnProcessor`` instance. The SKLearnProcessor
    #'              handles Amazon SageMaker processing tasks for jobs using scikit-learn.
    #' @param framework_version (str): The version of the framework. Value is ignored when
    #'              ``image_uri`` is provided.
    #' @param role (str): An AWS IAM role name or ARN. Amazon SageMaker Processing uses
    #'              this role to access AWS resources, such as data stored in Amazon S3.
    #' @param instance_count (int): The number of instances to run a processing job with.
    #' @param instance_type (str): The type of EC2 instance to use for processing, for
    #'              example, 'ml.c4.xlarge'.
    #' @param py_version (str): Python version you want to use for executing your
    #'              model training code. One of 'py2' or 'py3'. Defaults to 'py3'. Value
    #'              is ignored when ``image_uri`` is provided.
    #' @param image_uri (str): The URI of the Docker image to use for the
    #'              processing jobs (default: None).
    #' @param command ([str]): The command to run, along with any command-line flags
    #'              to *precede* the ```code script```. Example: ["python3", "-v"]. If not
    #'              provided, ["python"] will be chosen (default: None).
    #' @param volume_size_in_gb (int): Size in GB of the EBS volume
    #'              to use for storing data during processing (default: 30).
    #' @param volume_kms_key (str): A KMS key for the processing volume (default: None).
    #' @param output_kms_key (str): The KMS key ID for processing job outputs (default: None).
    #' @param code_location (str): The S3 prefix URI where custom code will be
    #'              uploaded (default: None). The code file uploaded to S3 is
    #'              'code_location/job-name/source/sourcedir.tar.gz'. If not specified, the
    #'              default ``code location`` is 's3://{sagemaker-default-bucket}'
    #' @param max_runtime_in_seconds (int): Timeout in seconds (default: None).
    #'              After this amount of time, Amazon SageMaker terminates the job,
    #'              regardless of its current status. If `max_runtime_in_seconds` is not
    #'              specified, the default value is 24 hours.
    #' @param base_job_name (str): Prefix for processing name. If not specified,
    #'              the processor generates a default job name, based on the
    #'              processing image name and current timestamp (default: None).
    #' @param sagemaker_session (:class:`~sagemaker.session.Session`):
    #'              Session object which manages interactions with Amazon SageMaker and
    #'              any other AWS services needed. If not specified, the processor creates
    #'              one using the default AWS configuration chain (default: None).
    #' @param env (dict[str, str]): Environment variables to be passed to
    #'              the processing jobs (default: None).
    #' @param tags (list[dict]): List of tags to be passed to the processing job
    #'              (default: None). For more, see
    #'              \url{https://docs.aws.amazon.com/sagemaker/latest/dg/API_Tag.html}.
    #' @param network_config (:class:`~sagemaker.network.NetworkConfig`):
    #'              A :class:`~sagemaker.network.NetworkConfig`
    #'              object that configures network isolation, encryption of
    #'              inter-container traffic, security group IDs, and subnets (default: None).
    initialize = function(framework_version,
                          role,
                          instance_type,
                          instance_count,
                          py_version="py3",
                          image_uri=NULL,
                          command=NULL,
                          volume_size_in_gb=30,
                          volume_kms_key=NULL,
                          output_kms_key=NULL,
                          code_location=NULL,
                          max_runtime_in_seconds=NULL,
                          base_job_name=NULL,
                          sagemaker_session=NULL,
                          env=NULL,
                          tags=NULL,
                          network_config=NULL){
      if (is.null(command))
        command = "python3"

      session = sagemaker_session %||% sagemaker.core::Session$new()
      region = session$paws_region_name

      image_uri = sagemaker.core::ImageUris$new()$retrieve(
        "sklearn", region, version=framework_version, instance_type=instance_type
      )
      super$initialize(
        self$estimator_cls,
        framework_version,
        role=role,
        image_uri=image_uri,
        instance_count=instance_count,
        instance_type=instance_type,
        command=command,
        volume_size_in_gb=volume_size_in_gb,
        volume_kms_key=volume_kms_key,
        output_kms_key=output_kms_key,
        max_runtime_in_seconds=max_runtime_in_seconds,
        base_job_name=base_job_name,
        sagemaker_session=session,
        env=env,
        tags=tags,
        network_config=network_config
      )
    }
  ),
  lock_objects = F
)