Method _prepare_trainer

python/paddle/base/executor.py:2415–2476

(
        self,
        program=None,
        dataset=None,
        scope=None,
        thread=0,
        debug=False,
        fetch_list=None,
        fetch_info=None,
        print_period=100,
    )

            ]

    def _prepare_trainer(
        self,
        program=None,
        dataset=None,
        scope=None,
        thread=0,
        debug=False,
        fetch_list=None,
        fetch_info=None,
        print_period=100,
    ):
        if scope is None:
            scope = global_scope()
        if fetch_list is None:
            fetch_list = []
        if fetch_info is None:
            fetch_info = []
        assert len(fetch_list) == len(fetch_info)
        compiled = isinstance(program, compiler.CompiledProgram)
        if not compiled:
            # TODO: Need a better way to distinguish and specify different execution mode
            if program._pipeline_opt:
                trainer = TrainerFactory()._create_trainer(
                    program._pipeline_opt
                )
            elif program._heter_pipeline_opt:
                trainer = TrainerFactory()._create_trainer(
                    program._heter_pipeline_opt
                )
            else:
                trainer = TrainerFactory()._create_trainer(program._fleet_opt)
                trainer._set_thread_barrier(program._is_distributed)
            trainer._set_program(program)
        else:
            if program._pipeline_opt:
                trainer = TrainerFactory()._create_trainer(
                    program.program._pipeline_opt
                )
            elif program._heter_pipeline_opt:
                trainer = TrainerFactory()._create_trainer(
                    program.program._heter_pipeline_opt
                )
            else:
                trainer = TrainerFactory()._create_trainer(
                    program.program._fleet_opt
                )
            trainer._set_program(program.program)

        if thread <= 0:
            if dataset.thread_num <= 0:
                raise RuntimeError(
                    "You should set thread num first, either in Dataset"
                    "or in Executor.train_from_dataset"
                )
            else:
                trainer._set_thread(dataset.thread_num)
        else:
            trainer._set_thread(thread)