class_docstr: Handle end-to-end training and deployment of XGBoost booster training.
  
  It can also handle training using customer provided XGBoost entry point script.
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docstr: An estimator that executes an XGBoost-based SageMaker Training Job.
  
  The managed XGBoost environment is an Amazon-built Docker container thatexecutes functions
  defined in the supplied ``entry_point`` Python script.
  
  Training is started by calling :meth:`~sagemaker.amazon.estimator.Framework.fit` on this
  Estimator. After training is complete, calling
  :meth:`~sagemaker.amazon.estimator.Framework.deploy` creates a hosted SageMaker endpoint
  and returns an :class:`~sagemaker.amazon.xgboost.model.XGBoostPredictor` instance that
  can be used to perform inference against the hosted model.
  
  Technical documentation on preparing XGBoost scripts for SageMaker training and using the
  XGBoost Estimator is available on the project home-page:
  https://github.com/aws/sagemaker-python-sdk
  
  Args:
      entry_point (str or PipelineVariable): Path (absolute or relative) to
          the Python source file which should be executed as the entry point to training.
          If ``source_dir`` is specified, then ``entry_point`` must point to
          a file located at the root of ``source_dir``.
      framework_version (str): XGBoost version you want to use for executing your model
          training code.
      source_dir (str or PipelineVariable): Path (absolute, relative or an S3 URI) to
          a directory with any other training source code dependencies aside from the entry
          point file (default: None). If ``source_dir`` is an S3 URI, it must
          point to a tar.gz file. Structure within this directory are preserved
          when training on Amazon SageMaker.
      hyperparameters (dict[str, str] or dict[str, PipelineVariable]): Hyperparameters
          that will be used for training (default: None).
          The hyperparameters are made accessible as a dict[str, str] to the training code
          on SageMaker. For convenience, this accepts other types for keys and values, but
          ``str()`` will be called to convert them before training.
      py_version (str): Python version you want to use for executing your model
          training code (default: 'py3').
      image_uri (str or PipelineVariable): If specified, the estimator will use this image
          for training and hosting, instead of selecting the appropriate SageMaker official
          image based on framework_version and py_version. It can be an ECR url or
          dockerhub image and tag.
          Examples:
              123.dkr.ecr.us-west-2.amazonaws.com/my-custom-image:1.0
              custom-image:latest.
      image_uri_region (str): If ``image_uri`` argument is None, the image uri
          associated with this object will be in this region.
          Default: region associated with SageMaker session.
      **kwargs: Additional kwargs passed to the
          :class:`~sagemaker.estimator.Framework` constructor.
  
  .. tip::
  
      You can find additional parameters for initializing this class at
      :class:`~sagemaker.estimator.Framework` and
      :class:`~sagemaker.estimator.EstimatorBase`.
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code: def __init__(
      self,
      entry_point: Union[str, PipelineVariable],
      framework_version: str,
      source_dir: Optional[Union[str, PipelineVariable]] = None,
      hyperparameters: Optional[
          Dict[str, Union[str, PipelineVariable]]
      ] = None,
      py_version: str = "py3",
      image_uri: Optional[Union[str, PipelineVariable]] = None,
      image_uri_region: Optional[str] = None,
      **kwargs,
  ):
      """An estimator that executes an XGBoost-based SageMaker Training Job.
  
      The managed XGBoost environment is an Amazon-built Docker container thatexecutes functions
      defined in the supplied ``entry_point`` Python script.
  
      Training is started by calling :meth:`~sagemaker.amazon.estimator.Framework.fit` on this
      Estimator. After training is complete, calling
      :meth:`~sagemaker.amazon.estimator.Framework.deploy` creates a hosted SageMaker endpoint
      and returns an :class:`~sagemaker.amazon.xgboost.model.XGBoostPredictor` instance that
      can be used to perform inference against the hosted model.
  
      Technical documentation on preparing XGBoost scripts for SageMaker training and using the
      XGBoost Estimator is available on the project home-page:
      https://github.com/aws/sagemaker-python-sdk
  
      Args:
          entry_point (str or PipelineVariable): Path (absolute or relative) to
              the Python source file which should be executed as the entry point to training.
              If ``source_dir`` is specified, then ``entry_point`` must point to
              a file located at the root of ``source_dir``.
          framework_version (str): XGBoost version you want to use for executing your model
              training code.
          source_dir (str or PipelineVariable): Path (absolute, relative or an S3 URI) to
              a directory with any other training source code dependencies aside from the entry
              point file (default: None). If ``source_dir`` is an S3 URI, it must
              point to a tar.gz file. Structure within this directory are preserved
              when training on Amazon SageMaker.
          hyperparameters (dict[str, str] or dict[str, PipelineVariable]): Hyperparameters
              that will be used for training (default: None).
              The hyperparameters are made accessible as a dict[str, str] to the training code
              on SageMaker. For convenience, this accepts other types for keys and values, but
              ``str()`` will be called to convert them before training.
          py_version (str): Python version you want to use for executing your model
              training code (default: 'py3').
          image_uri (str or PipelineVariable): If specified, the estimator will use this image
              for training and hosting, instead of selecting the appropriate SageMaker official
              image based on framework_version and py_version. It can be an ECR url or
              dockerhub image and tag.
              Examples:
                  123.dkr.ecr.us-west-2.amazonaws.com/my-custom-image:1.0
                  custom-image:latest.
          image_uri_region (str): If ``image_uri`` argument is None, the image uri
              associated with this object will be in this region.
              Default: region associated with SageMaker session.
          **kwargs: Additional kwargs passed to the
              :class:`~sagemaker.estimator.Framework` constructor.
  
      .. tip::
  
          You can find additional parameters for initializing this class at
          :class:`~sagemaker.estimator.Framework` and
          :class:`~sagemaker.estimator.EstimatorBase`.
      """
      instance_type = renamed_kwargs(
          "train_instance_type",
          "instance_type",
          kwargs.get("instance_type"),
          kwargs,
      )
      super(XGBoost, self).__init__(
          entry_point,
          source_dir,
          hyperparameters,
          image_uri=image_uri,
          **kwargs,
      )
  
      self.py_version = py_version
      self.framework_version = framework_version
  
      validate_py_version(py_version)
      validate_framework_version(framework_version)
  
      if image_uri is None:
          self.image_uri = image_uris.retrieve(
              self._framework_name,
              image_uri_region
              or self.sagemaker_session.boto_region_name,
              version=framework_version,
              py_version=self.py_version,
              instance_type=instance_type,
              image_scope="training",
          )
  
file_path: sagemaker-python-sdk/src/sagemaker/xgboost/estimator.py
imports: from __future__ import absolute_import
  import logging
  from typing import Union, Optional, Dict
  from sagemaker import image_uris
  from sagemaker.deprecations import renamed_kwargs
  from sagemaker.estimator import Framework, _TrainingJob
  from sagemaker.fw_utils import framework_name_from_image, framework_version_from_tag, UploadedCode
  from sagemaker.session import Session
  from sagemaker.vpc_utils import VPC_CONFIG_DEFAULT
  from sagemaker.workflow.entities import PipelineVariable
  from sagemaker.xgboost import defaults
  from sagemaker.xgboost.model import XGBoostModel
  from sagemaker.xgboost.utils import validate_py_version, validate_framework_version
label_desc: Use image node_id 1 to create a new XGBoost object from inherited base classes: Framework with example: obj = XGBoost(entry_point, framework_version, source_dir, hyperparameters, py_version, image_uri, image_uri_region)