class_docstr: Base class for Amazon first-party Estimator implementations.
  
  This class isn't intended to be instantiated directly.
class_docstr_tok: ["Base","class","for","Amazon","first-party","Estimator","implementations",".","This","class","is","n't","intended","to","be","instantiated","directly","."]
docstr: Fit this Estimator on serialized Record objects, stored in S3.
  
  ``records`` should be an instance of :class:`~RecordSet`. This
  defines a collection of S3 data files to train this ``Estimator`` on.
  
  Training data is expected to be encoded as dense or sparse vectors in
  the "values" feature on each Record. If the data is labeled, the label
  is expected to be encoded as a list of scalas in the "values" feature of
  the Record label.
  
  More information on the Amazon Record format is available at:
  https://docs.aws.amazon.com/sagemaker/latest/dg/cdf-training.html
  
  See :meth:`~AmazonAlgorithmEstimatorBase.record_set` to construct a
  ``RecordSet`` object from :class:`~numpy.ndarray` arrays.
  
  Args:
      records (:class:`~RecordSet`): The records to train this ``Estimator`` on
      mini_batch_size (int or None): The size of each mini-batch to use
          when training. If ``None``, a default value will be used.
      wait (bool): Whether the call should wait until the job completes
          (default: True).
      logs (bool): Whether to show the logs produced by the job. Only
          meaningful when wait is True (default: True).
      job_name (str): Training job name. If not specified, the estimator
          generates a default job name, based on the training image name
          and current timestamp.
      experiment_config (dict[str, str]): Experiment management configuration.
          Optionally, the dict can contain four keys:
          'ExperimentName', 'TrialName', 'TrialComponentDisplayName' and 'RunName'.
          The behavior of setting these keys is as follows:
          * If `ExperimentName` is supplied but `TrialName` is not a Trial will be
          automatically created and the job's Trial Component associated with the Trial.
          * If `TrialName` is supplied and the Trial already exists the job's Trial Component
          will be associated with the Trial.
          * If both `ExperimentName` and `TrialName` are not supplied the trial component
          will be unassociated.
          * `TrialComponentDisplayName` is used for display in Studio.
code: def fit(
      self,
      records: "RecordSet",
      mini_batch_size: Optional[int] = None,
      wait: bool = True,
      logs: bool = True,
      job_name: Optional[str] = None,
      experiment_config: Optional[Dict[str, str]] = None,
  ):
      """Fit this Estimator on serialized Record objects, stored in S3.
  
      ``records`` should be an instance of :class:`~RecordSet`. This
      defines a collection of S3 data files to train this ``Estimator`` on.
  
      Training data is expected to be encoded as dense or sparse vectors in
      the "values" feature on each Record. If the data is labeled, the label
      is expected to be encoded as a list of scalas in the "values" feature of
      the Record label.
  
      More information on the Amazon Record format is available at:
      https://docs.aws.amazon.com/sagemaker/latest/dg/cdf-training.html
  
      See :meth:`~AmazonAlgorithmEstimatorBase.record_set` to construct a
      ``RecordSet`` object from :class:`~numpy.ndarray` arrays.
  
      Args:
          records (:class:`~RecordSet`): The records to train this ``Estimator`` on
          mini_batch_size (int or None): The size of each mini-batch to use
              when training. If ``None``, a default value will be used.
          wait (bool): Whether the call should wait until the job completes
              (default: True).
          logs (bool): Whether to show the logs produced by the job. Only
              meaningful when wait is True (default: True).
          job_name (str): Training job name. If not specified, the estimator
              generates a default job name, based on the training image name
              and current timestamp.
          experiment_config (dict[str, str]): Experiment management configuration.
              Optionally, the dict can contain four keys:
              'ExperimentName', 'TrialName', 'TrialComponentDisplayName' and 'RunName'.
              The behavior of setting these keys is as follows:
              * If `ExperimentName` is supplied but `TrialName` is not a Trial will be
              automatically created and the job's Trial Component associated with the Trial.
              * If `TrialName` is supplied and the Trial already exists the job's Trial Component
              will be associated with the Trial.
              * If both `ExperimentName` and `TrialName` are not supplied the trial component
              will be unassociated.
              * `TrialComponentDisplayName` is used for display in Studio.
      """
      self._prepare_for_training(
          records, job_name=job_name, mini_batch_size=mini_batch_size
      )
  
      experiment_config = check_and_get_run_experiment_config(
          experiment_config
      )
      self.latest_training_job = _TrainingJob.start_new(
          self, records, experiment_config=experiment_config
      )
      if wait:
          self.latest_training_job.wait(logs=logs)
  
imports: from __future__ import absolute_import
  import json
  import logging
  import tempfile
  from typing import Union, Optional, Dict
  from six.moves.urllib.parse import urlparse
  from sagemaker import image_uris, s3_utils
  from sagemaker.amazon import validation
  from sagemaker.amazon.hyperparameter import Hyperparameter
  from sagemaker.amazon.common import write_numpy_to_dense_tensor
  from sagemaker.deprecations import renamed_warning
  from sagemaker.estimator import EstimatorBase, _TrainingJob
  from sagemaker.inputs import FileSystemInput, TrainingInput
  from sagemaker.utils import sagemaker_timestamp, check_and_get_run_experiment_config
  from sagemaker.workflow.entities import PipelineVariable
  from sagemaker.workflow.pipeline_context import runnable_by_pipeline
  from sagemaker.workflow import is_pipeline_variable
label_desc: Use image node_id 9 for calling the AmazonAlgorithmEstimatorBase obj's underlying member method code with example usage: obj.fit(records, mini_batch_size, wait, logs, job_name, experiment_config) without return types