original_docstring: A Estimator fit() call to start a model training job.
  
          Args:
              input_model_id (str): Model id of model to used as pre-trained model of the training job
              input_data_s3_prefix (str): Defines the location of s3 data to train on.
              manifest_file_path (str): Manifest file used to provide training data.
              wait (bool): Whether the call should wait until the job completes. Only
                  meaningful when running in SageMaker mode.
              logs (bool): Whether to show the logs produced by the job.
                  Only meaningful when wait is True (default: True).
          
docstring: A Estimator fit() call to start a model training job.
docstring_tokens: A Estimator fit () call to start a model training job .
code: def fit(
          self,
          input_model_id=None,
          input_data_s3_prefix=None,
          manifest_file_path=None,
          wait=False,
          logs=True,
      ):
          """A Estimator fit() call to start a model training job.
  
          Args:
              input_model_id (str): Model id of model to used as pre-trained model of the training job
              input_data_s3_prefix (str): Defines the location of s3 data to train on.
              manifest_file_path (str): Manifest file used to provide training data.
              wait (bool): Whether the call should wait until the job completes. Only
                  meaningful when running in SageMaker mode.
              logs (bool): Whether to show the logs produced by the job.
                  Only meaningful when wait is True (default: True).
          """
          # update object var, to be reflected in DDb Record as well.
          self.model_record.add_new_training_job_info(
              input_model_id=input_model_id,
              input_data_s3_prefix=input_data_s3_prefix,
              manifest_file_path=manifest_file_path,
          )
          self.model_db_client.update_model_record(self._jsonify())
  
          if input_model_id is None:
              self._fit_first_model(
                  input_data_s3_prefix=input_data_s3_prefix,
                  manifest_file_path=manifest_file_path,
                  wait=wait,
                  logs=logs,
              )
          else:
              # use 'input_model_id' as pretrained model for training
              input_model_record = self.model_db_client.get_model_record(
                  self.experiment_id, input_model_id
              )
              model_artifact_path = input_model_record.get("s3_model_output_path")
              rl_estimator_args = self._get_rl_estimator_args()
              rl_estimator_args["model_channel_name"] = "pretrained_model"
              rl_estimator_args["model_uri"] = model_artifact_path
              self.rl_estimator = RLEstimator(**rl_estimator_args)
  
              if manifest_file_path:
                  inputs = sagemaker.inputs.TrainingInput(
                      s3_data=manifest_file_path, s3_data_type="ManifestFile"
                  )
              else:
                  inputs = input_data_s3_prefix
  
              self.rl_estimator.fit(job_name=self.model_id, inputs=inputs, wait=wait, logs=logs)
code_tokens: def fit ( self , input_model_id = None , input_data_s3_prefix = None , manifest_file_path = None , wait = False , logs = True , ) : self . model_record . add_new_training_job_info ( input_model_id = input_model_id , input_data_s3_prefix = input_data_s3_prefix , manifest_file_path = manifest_file_path , ) self . model_db_client . update_model_record ( self . _jsonify ( ) ) if input_model_id is None : self . _fit_first_model ( input_data_s3_prefix = input_data_s3_prefix , manifest_file_path = manifest_file_path , wait = wait , logs = logs , ) else : input_model_record = self . model_db_client . get_model_record ( self . experiment_id , input_model_id ) model_artifact_path = input_model_record . get ( "s3_model_output_path" ) rl_estimator_args = self . _get_rl_estimator_args ( ) rl_estimator_args [ "model_channel_name" ] = "pretrained_model" rl_estimator_args [ "model_uri" ] = model_artifact_path self . rl_estimator = RLEstimator ( ** rl_estimator_args ) if manifest_file_path : inputs = sagemaker . inputs . TrainingInput ( s3_data = manifest_file_path , s3_data_type = "ManifestFile" ) else : inputs = input_data_s3_prefix self . rl_estimator . fit ( job_name = self . model_id , inputs = inputs , wait = wait , logs = logs )
short_docstring: A Estimator fit() call to start a model training job.
short_docstring_tokens: A Estimator fit () call to start a model training job .
comment: """A Estimator fit() call to start a model training job.
  
          Args:
              input_model_id (str): Model id of model to used as pre-trained model of the training job
              input_data_s3_prefix (str): Defines the location of s3 data to train on.
              manifest_file_path (str): Manifest file used to provide training data.
              wait (bool): Whether the call should wait until the job completes. Only
                  meaningful when running in SageMaker mode.
              logs (bool): Whether to show the logs produced by the job.
                  Only meaningful when wait is True (default: True).
          """$SEP$# update object var, to be reflected in DDb Record as well.$SEP$# use 'input_model_id' as pretrained model for training