def _validate_and_set_debugger_configs()

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

• 22 lines of code
• 15 McCabe index (conditional complexity)

    def _validate_and_set_debugger_configs(self):
        """Set defaults for debugging."""
        if self.debugger_hook_config is None and _region_supports_debugger(
            self.sagemaker_session.boto_region_name
        ):
            self.debugger_hook_config = DebuggerHookConfig(s3_output_path=self.output_path)
        elif not self.debugger_hook_config:
            # set hook config to False if _region_supports_debugger is False
            self.debugger_hook_config = False

        # Disable debugger if checkpointing is enabled by the customer
        if self.checkpoint_s3_uri and self.checkpoint_local_path and self.debugger_hook_config:
            if self._framework_name in {"mxnet", "pytorch", "tensorflow"}:
                if self.instance_count > 1 or (
                    hasattr(self, "distribution")
                    and self.distribution is not None  # pylint: disable=no-member
                ):
                    logger.info(
                        "SMDebug Does Not Currently Support \
                        Distributed Training Jobs With Checkpointing Enabled"
                    )
                    self.debugger_hook_config = False

        if self.debugger_hook_config is False:
            if self.environment is None:
                self.environment = {}
            self.environment[DEBUGGER_FLAG] = "0"