def _prepare_for_training()

in src/sagemaker/estimator.py [0:0]

• 48 lines of code
• 21 McCabe index (conditional complexity)

    def _prepare_for_training(self, job_name=None):
        """Set any values in the estimator that need to be set before training.

        Args:
            job_name (str): Name of the training job to be created. If not
                specified, one is generated, using the base name given to the
                constructor if applicable.
        """
        self._current_job_name = self._get_or_create_name(job_name)

        # if output_path was specified we use it otherwise initialize here.
        # For Local Mode with local_code=True we don't need an explicit output_path
        if self.output_path is None:
            local_code = get_config_value("local.local_code", self.sagemaker_session.config)
            if self.sagemaker_session.local_mode and local_code:
                self.output_path = ""
            else:
                self.output_path = s3.s3_path_join(
                    "s3://",
                    self.sagemaker_session.default_bucket(),
                    self.sagemaker_session.default_bucket_prefix,
                    with_end_slash=True,
                )
                self._is_output_path_set_from_default_bucket_and_prefix = True

        if self.git_config:
            updated_paths = git_utils.git_clone_repo(
                self.git_config, self.entry_point, self.source_dir, self.dependencies
            )
            self.entry_point = updated_paths["entry_point"]
            self.source_dir = updated_paths["source_dir"]
            self.dependencies = updated_paths["dependencies"]

        if self.source_dir or self.entry_point or self.dependencies:
            # validate source dir will raise a ValueError if there is something wrong with
            # the source directory. We are intentionally not handling it because this is a
            # critical error.
            if (
                self.source_dir
                and not is_pipeline_variable(self.source_dir)
                and not self.source_dir.lower().startswith("s3://")
            ):
                validate_source_dir(self.entry_point, self.source_dir)

            # if we are in local mode with local_code=True. We want the container to just
            # mount the source dir instead of uploading to S3.
            local_code = get_config_value("local.local_code", self.sagemaker_session.config)

            if self.sagemaker_session.local_mode and local_code:
                # if there is no source dir, use the directory containing the entry point.
                if self.source_dir is None:
                    self.source_dir = os.path.dirname(self.entry_point)
                self.entry_point = os.path.basename(self.entry_point)

                code_dir = "file://" + self.source_dir
                script = self.entry_point
            elif self.enable_network_isolation() and self.entry_point:
                self.uploaded_code = self._stage_user_code_in_s3()
                code_dir = self.CONTAINER_CODE_CHANNEL_SOURCEDIR_PATH
                script = self.uploaded_code.script_name
                self.code_uri = self.uploaded_code.s3_prefix
            else:
                self.uploaded_code = self._stage_user_code_in_s3()
                code_dir = self.uploaded_code.s3_prefix
                script = self.uploaded_code.script_name

            # Modify hyperparameters in-place to point to the right code directory and
            # script URIs
            self._script_mode_hyperparam_update(code_dir, script)

        self._prepare_rules()
        self._prepare_debugger_for_training()
        self._prepare_profiler_for_training()