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How to use save_stdout_to_file method in lisa

Best Python code snippet using lisa_python

process.py

Source:process.py

...47                "passed as parameter expected_exit_code. Must be int or "48                "List[int]"49            )50        return assert_that(expected_exit_codes, message).contains(self.exit_code)51    def save_stdout_to_file(self, saved_path: Path) -> "ExecutableResult":52        with open(saved_path, "w") as f:53            f.write(self.stdout)54        return self55# TODO: So much cleanup here. It was using duck typing.56class Process:57    def __init__(58        self,59        id_: str,60        shell: Shell,61        parent_logger: Optional[Logger] = None,62    ) -> None:63        # the shell can be LocalShell or SshShell64        self._shell = shell65        self._id_ = id_...

experiment_template.py

Source:experiment_template.py

...72    'address': 'auto',73}74script_name = os.path.basename(__file__).strip('.py')75fname = f'log_{script_name}_{datetime.now().strftime("%y%m%d_%H%M")}.txt'76BruteLogger.save_stdout_to_file(path=exp_config['log_dir'], fname=fname, encoding='utf8', also_stderr=True)77assert not (exp_config['use_cma_ipop'] and exp_config['use_dibb_ipop']), 'It is not recommended to use both CMA-IPOP and DIBB-IPOP at the same time'78if exp_config['use_notify']:79    from notify_run import Notify80    notify = Notify()81    82ray.init(**ray_config)83def format_time(seconds):84    hh = int(seconds // 3600)85    mm = int(seconds % 3600 // 60)86    ss = int(seconds % 3600 % 60)87    return f'{hh:02}h {mm:02}m {ss:02}s'88def RemCocoWrapper(rem_coco):89    """Filters the relevant problems and uses tqdm to track progress.."""90    return tqdm(rem_coco.problems,...

experiment.py

Source:experiment.py

...21        assert (not self.cfg['resume']) or (not self.cfg['timestamp']), "Cannot resume experiment with timestamp activated"22        assert (not self.cfg['transfer'] == 'sequential') or (not self.cfg['resume']), "Cannot resume training on sequential"23        self.make_paths()24        # Setup logger25        BruteLogger.save_stdout_to_file(path=self.paths['logs'])26        # Resolve CPU threads and cuda device27        torch.set_num_threads(self.cfg['trg_n_cpu'])28        if torch.cuda.is_available():29            # Need to have a GPU and to precise it at the end of the experiment file name30            # Or in the terminal after the file name31            # Assertion blocks if we cannot cast to int, i.e. last part of experiment file name is not an int32            assert isinstance(int(self.cfg['trg_gpu']), int), "Please precise your GPU at the end of the experiment file name"33            # Will anyway stop if the index is not available or wrong34            self.cuda_device = f"cuda:{int(self.cfg['trg_gpu'])}"35        # Otherwise just go with CPU36        else:37            self.cuda_device = 'cpu'38            torch.set_num_threads(self.cfg['trg_n_cpu'])39        # Give a dropout, learning rate, optimizer and loss function specific to each DMs...

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