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How to use save_resume_state method in Slash

Best Python code snippet using slash

ppo_trainer.py

Source:ppo_trainer.py

...632                if ppo_cfg.use_linear_clip_decay:633                    self.agent.clip_param = ppo_cfg.clip_param * (634                        1 - self.percent_done()635                    )636                if rank0_only() and self._should_save_resume_state():637                    requeue_stats = dict(638                        env_time=self.env_time,639                        pth_time=self.pth_time,640                        count_checkpoints=count_checkpoints,641                        num_steps_done=self.num_steps_done,642                        num_updates_done=self.num_updates_done,643                        _last_checkpoint_percent=self._last_checkpoint_percent,644                        prev_time=(time.time() - self.t_start) + prev_time,645                        running_episode_stats=self.running_episode_stats,646                        window_episode_stats=dict(self.window_episode_stats),647                    )648                    save_resume_state(649                        dict(650                            state_dict=self.agent.state_dict(),651                            optim_state=self.agent.optimizer.state_dict(),652                            lr_sched_state=lr_scheduler.state_dict(),653                            config=self.config,654                            requeue_stats=requeue_stats,655                        ),656                        self.config,657                    )658                if EXIT.is_set():659                    profiling_wrapper.range_pop()  # train update660                    self.envs.close()661                    requeue_job()662                    return...

base_trainer.py

Source:base_trainer.py

...132                    )133                    # We save a resume state during evaluation so that134                    # we can resume evaluating incase the job gets135                    # preempted.136                    save_resume_state(137                        {138                            "config": self.config,139                            "prev_ckpt_ind": prev_ckpt_ind,140                        },141                        self.config,142                        filename_key="eval",143                    )144                    if (prev_ckpt_ind + 1) == self.config.NUM_CHECKPOINTS:145                        break146    def _eval_checkpoint(147        self,148        checkpoint_path: str,149        writer: TensorboardWriter,150        checkpoint_index: int = 0,151    ) -> None:152        raise NotImplementedError153    def save_checkpoint(self, file_name) -> None:154        raise NotImplementedError155    def load_checkpoint(self, checkpoint_path, *args, **kwargs) -> Dict:156        raise NotImplementedError157class BaseRLTrainer(BaseTrainer):158    r"""Base trainer class for RL trainers. Future RL-specific159    methods should be hosted here.160    """161    device: torch.device  # type: ignore162    config: Config163    video_option: List[str]164    num_updates_done: int165    num_steps_done: int166    _flush_secs: int167    _last_checkpoint_percent: float168    def __init__(self, config: Config) -> None:169        super().__init__()170        assert config is not None, "needs config file to initialize trainer"171        self.config = config172        self._flush_secs = 30173        self.num_updates_done = 0174        self.num_steps_done = 0175        self._last_checkpoint_percent = -1.0176        if config.NUM_UPDATES != -1 and config.TOTAL_NUM_STEPS != -1:177            raise RuntimeError(178                "NUM_UPDATES and TOTAL_NUM_STEPS are both specified.  One must be -1.\n"179                " NUM_UPDATES: {} TOTAL_NUM_STEPS: {}".format(180                    config.NUM_UPDATES, config.TOTAL_NUM_STEPS181                )182            )183        if config.NUM_UPDATES == -1 and config.TOTAL_NUM_STEPS == -1:184            raise RuntimeError(185                "One of NUM_UPDATES and TOTAL_NUM_STEPS must be specified.\n"186                " NUM_UPDATES: {} TOTAL_NUM_STEPS: {}".format(187                    config.NUM_UPDATES, config.TOTAL_NUM_STEPS188                )189            )190        if config.NUM_CHECKPOINTS != -1 and config.CHECKPOINT_INTERVAL != -1:191            raise RuntimeError(192                "NUM_CHECKPOINTS and CHECKPOINT_INTERVAL are both specified."193                "  One must be -1.\n"194                " NUM_CHECKPOINTS: {} CHECKPOINT_INTERVAL: {}".format(195                    config.NUM_CHECKPOINTS, config.CHECKPOINT_INTERVAL196                )197            )198        if config.NUM_CHECKPOINTS == -1 and config.CHECKPOINT_INTERVAL == -1:199            raise RuntimeError(200                "One of NUM_CHECKPOINTS and CHECKPOINT_INTERVAL must be specified"201                " NUM_CHECKPOINTS: {} CHECKPOINT_INTERVAL: {}".format(202                    config.NUM_CHECKPOINTS, config.CHECKPOINT_INTERVAL203                )204            )205    def percent_done(self) -> float:206        if self.config.NUM_UPDATES != -1:207            return self.num_updates_done / self.config.NUM_UPDATES208        else:209            return self.num_steps_done / self.config.TOTAL_NUM_STEPS210    def is_done(self) -> bool:211        return self.percent_done() >= 1.0212    def should_checkpoint(self) -> bool:213        needs_checkpoint = False214        if self.config.NUM_CHECKPOINTS != -1:215            checkpoint_every = 1 / self.config.NUM_CHECKPOINTS216            if (217                self._last_checkpoint_percent + checkpoint_every218                < self.percent_done()219            ):220                needs_checkpoint = True221                self._last_checkpoint_percent = self.percent_done()222        else:223            needs_checkpoint = (224                self.num_updates_done % self.config.CHECKPOINT_INTERVAL225            ) == 0226        return needs_checkpoint227    def _should_save_resume_state(self) -> bool:228        return SAVE_STATE.is_set() or (229            (230                not self.config.RL.preemption.save_state_batch_only231                or is_slurm_batch_job()232            )233            and (234                (235                    int(self.num_updates_done + 1)236                    % self.config.RL.preemption.save_resume_state_interval237                )238                == 0239            )240        )241    @property...

resuming.py

Source:resuming.py

...74        old_sessions_ids = [session.session_id for session in old_sessions_query.all()]75        if old_sessions_ids:76            conn.query(ResumeState).filter(ResumeState.session_id.in_(old_sessions_ids)).delete(synchronize_session=False)77        old_sessions_query.delete(synchronize_session=False)78def save_resume_state(session_result, collected_tests):79    session_metadata = SessionMetadata(80        session_id=session_result.session.id,81        src_folder=os.path.abspath(os.getcwd()),82        created_at=datetime.now())83    tests_to_resume = []84    for result in session_result.iter_test_results():85        metadata = result.test_metadata86        test_to_resume = ResumeState(session_id=session_result.session.id, file_name=metadata.file_path, address_in_file=metadata.address_in_file)87        test_to_resume.variation = str(metadata.variation.id) if metadata.variation else None88        if result.is_success_finished():89            test_to_resume.status = ResumeTestStatus.SUCCESS90        elif not result.is_started() or result.is_skip():91            test_to_resume.status = ResumeTestStatus.PLANNED92        else:...

ddp_utils.py

Source:ddp_utils.py

...106    # SLURM always sends SIGTERM so we can use this to save and exit107    signal.signal(signal.SIGTERM, _clean_exit_and_save_handler)108    signal.signal(signal.SIGUSR1, _requeue_handler)109@rank0_only110def save_resume_state(state: Any, filename_or_config: Union[Config, str]):111    r"""Saves the resume job state to the specified filename.112        This is useful when working with preemptable job partitions.113    :param state: The state to save114    :param filename_or_config: The filename of the saved state or the config to construct it.115    """116    if isinstance(filename_or_config, Config):117        filename = resume_state_filename(filename_or_config)118    else:119        filename = filename_or_config120    torch.save(state, filename)121def load_resume_state(filename_or_config: Union[Config, str]) -> Optional[Any]:122    r"""Loads the saved resume state123    :param filename_or_config: The filename of the saved state or the config to construct it.124    :return: The saved state if the file exists, else none...

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