gistfile1.txt

____________________________ test_dump_best_config _____________________________
 

 
    def test_dump_best_config() -> None:
 
        with tempfile.TemporaryDirectory() as tmp_dir:
 
    
 
            def objective(trial: optuna.Trial) -> float:
 
                trial.suggest_uniform("DROPOUT", dropout, dropout)
 
                executor = optuna.integration.AllenNLPExecutor(trial, input_config_file, tmp_dir)
 
                return executor.run()
 
    
 
            dropout = 0.5
 
            input_config_file = os.path.join(
 
                os.path.dirname(os.path.realpath(__file__)), "example.jsonnet"
 
            )
 
            output_config_file = os.path.join(tmp_dir, "result.json")
 
    
 
            study = optuna.create_study(direction="maximize")
 
>           study.optimize(objective, n_trials=1)
 

 
tests/integration_tests/allennlp_tests/test_allennlp.py:142: 
 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
 
optuna/study.py:286: in optimize
 
    func, n_trials, timeout, catch, callbacks, gc_after_trial, None
 
optuna/study.py:629: in _optimize_sequential
 
    self._run_trial_and_callbacks(func, catch, callbacks, gc_after_trial)
 
optuna/study.py:660: in _run_trial_and_callbacks
 
    trial = self._run_trial(func, catch, gc_after_trial)
 
optuna/study.py:684: in _run_trial
 
    result = func(trial)
 
tests/integration_tests/allennlp_tests/test_allennlp.py:133: in objective
 
    return executor.run()
 
optuna/integration/allennlp.py:129: in run
 
    allennlp.commands.train.train_model(params, self._serialization_dir)
 
venv/lib/python3.6/site-packages/allennlp/commands/train.py:230: in train_model
 
    dry_run=dry_run,
 
venv/lib/python3.6/site-packages/allennlp/commands/train.py:418: in _train_worker
 
    params=params, serialization_dir=serialization_dir, local_rank=process_rank,
 
venv/lib/python3.6/site-packages/allennlp/common/from_params.py:580: in from_params
 
    **extras,
 
venv/lib/python3.6/site-packages/allennlp/common/from_params.py:611: in from_params
 
    return constructor_to_call(**kwargs)  # type: ignore
 
venv/lib/python3.6/site-packages/allennlp/commands/train.py:646: in from_partial_objects
 
    data_loader_ = data_loader.construct(dataset=datasets["train"])
 
venv/lib/python3.6/site-packages/allennlp/common/lazy.py:46: in construct
 
    return self._constructor(**kwargs)
 
venv/lib/python3.6/site-packages/allennlp/common/from_params.py:446: in constructor
 
    return value_cls.from_params(params=deepcopy(popped_params), **constructor_extras)
 
venv/lib/python3.6/site-packages/allennlp/common/from_params.py:580: in from_params
 
    **extras,
 
venv/lib/python3.6/site-packages/allennlp/common/from_params.py:611: in from_params
 
    return constructor_to_call(**kwargs)  # type: ignore
 
venv/lib/python3.6/site-packages/allennlp/data/dataloader.py:151: in from_partial_objects
 
    batches_per_epoch=batches_per_epoch,
 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
 

 
self = <allennlp.data.dataloader.DataLoader object at 0x7f4d142ba438>
 
dataset = <allennlp.data.dataset_readers.dataset_reader.AllennlpDataset object at 0x7f4d14512ba8>
 
batch_size = 32, shuffle = False, sampler = None, batch_sampler = None
 
num_workers = 0, collate_fn = <function allennlp_collate at 0x7f4d9fc31730>
 
pin_memory = False, drop_last = False, timeout = 0, worker_init_fn = None
 
multiprocessing_context = None, batches_per_epoch = None
 

 
    def __init__(
 
        self,
 
        dataset: data.Dataset,
 
        batch_size: int = 1,
 
        shuffle: bool = False,
 
        sampler: Sampler = None,
 
        batch_sampler: BatchSampler = None,
 
        num_workers: int = 0,
 
        # NOTE: The default for collate_fn is different from the normal `None`.
 
        # We assume that if you are using this class you are using an
 
        # allennlp dataset of instances, which would require this.
 
        collate_fn=allennlp_collate,
 
        pin_memory: bool = False,
 
        drop_last: bool = False,
 
        timeout: int = 0,
 
        worker_init_fn=None,
 
        multiprocessing_context: str = None,
 
        batches_per_epoch: int = None,
 
    ):
 
        if num_workers and isinstance(dataset, AllennlpLazyDataset):
 
            warnings.warn(
 
                "Using multi-process data loading with a lazy dataset could lead to "
 
                "deadlocks with certain tokenizers. See:\n"
 
                "  https://github.com/allenai/allennlp/issues/4330\n",
 
                UserWarning,
 
            )
 
        super().__init__(
 
            dataset=dataset,
 
            batch_size=batch_size,
 
            shuffle=shuffle,
 
            sampler=sampler,
 
            batch_sampler=batch_sampler,
 
            num_workers=num_workers,
 
            collate_fn=collate_fn,
 
            pin_memory=pin_memory,
 
            drop_last=drop_last,
 
            timeout=timeout,
 
            worker_init_fn=worker_init_fn,
 
>           multiprocessing_context=multiprocessing_context,
 
        )
 
E       TypeError: intercept_args() got an unexpected keyword argument 'multiprocessing_context'
 

 
venv/lib/python3.6/site-packages/allennlp/data/dataloader.py:88: TypeError