def cli_main( modify_parser: Optional[Callable[[argparse.ArgumentParser], None]] = None ) -> None: parser = options.get_training_parser() args = options.parse_args_and_arch(parser, modify_parser=modify_parser) cfg = convert_namespace_to_omegaconf(args) if args.profile: with torch.cuda.profiler.profile(): with torch.autograd.profiler.emit_nvtx(): distributed_utils.call_main(cfg, main) else: distributed_utils.call_main(cfg, main)
def cli_main( modify_parser: Optional[Callable[[argparse.ArgumentParser], None]] = None ) -> None: parser = options.get_training_parser() args = options.parse_args_and_arch(parser, modify_parser=modify_parser) cfg = convert_namespace_to_omegaconf(args) if cfg.common.use_plasma_view: server = PlasmaStore(path=cfg.common.plasma_path) logger.info(f"Started plasma server pid {server.server.pid} {cfg.common.plasma_path}") if args.profile: with torch.cuda.profiler.profile(): with torch.autograd.profiler.emit_nvtx(): distributed_utils.call_main(cfg, main) else: distributed_utils.call_main(cfg, main)
def train_language_model( data_dir, arch, extra_flags=None, run_validation=False, extra_valid_flags=None, task="language_modeling", world_size=1, ): train_parser = options.get_training_parser() train_args = options.parse_args_and_arch( train_parser, [ "--task", task, data_dir, "--arch", arch, "--optimizer", "adam", "--lr", "0.0001", "--max-tokens", "500", "--tokens-per-sample", "500", "--save-dir", data_dir, "--max-epoch", "1", "--no-progress-bar", "--distributed-world-size", str(world_size), "--ddp-backend", "no_c10d", "--num-workers", "0", ] + (extra_flags or []), ) cfg = convert_namespace_to_omegaconf(train_args) distributed_utils.call_main(cfg, train.main) if run_validation: # test validation validate_parser = options.get_validation_parser() validate_args = options.parse_args_and_arch( validate_parser, [ "--task", task, data_dir, "--path", os.path.join(data_dir, "checkpoint_last.pt"), "--valid-subset", "valid", "--max-tokens", "500", "--no-progress-bar", "--num-workers", "0", ] + (extra_valid_flags or []), ) validate.main(validate_args)
def train_translation_model( data_dir, arch, extra_flags=None, task="translation", run_validation=False, lang_flags=None, extra_valid_flags=None, world_size=1, ): if lang_flags is None: lang_flags = [ "--source-lang", "in", "--target-lang", "out", ] train_parser = options.get_training_parser() train_args = options.parse_args_and_arch( train_parser, [ "--task", task, data_dir, "--save-dir", data_dir, "--arch", arch, "--optimizer", "nag", "--lr", "0.05", "--max-tokens", "500", "--max-epoch", "1", "--no-progress-bar", "--distributed-world-size", str(world_size), "--num-workers", "0", ] + lang_flags + (extra_flags or []), ) cfg = convert_namespace_to_omegaconf(train_args) distributed_utils.call_main(cfg, train.main) if run_validation: # test validation validate_parser = options.get_validation_parser() validate_args = options.parse_args_and_arch( validate_parser, [ "--task", task, data_dir, "--path", os.path.join(data_dir, "checkpoint_last.pt"), "--valid-subset", "valid", "--max-tokens", "500", "--no-progress-bar", "--num-workers", "0", ] + lang_flags + (extra_valid_flags or []), ) validate.main(validate_args)