我正在Dask（python）中运行它，并且仅在使用大数据集时才出现错误。查找答案时，没有找到与Dask相关的任何内容，并且解决方案似乎不适用于我的问题。在初始化LSTM时，我已经看到了将batch_first=True放入import dask.dataframe as dd
from dask.diagnostics import ProgressBar
import stanfordnlp
nlp = stanfordnlp.Pipeline(processors='tokenize,mwt,lemma,pos', lang='en')
ddf = dd.from_pandas(df, npartitions=4)
ddf['tokens'] = ddf[column].apply(lambda text: nlp(text),
meta=(column, 'object'))
with ProgressBar():
df = ddf.compute()
的几个答案，但是我不知道该怎么做，因为我没有直接使用pytorch。
Traceback (most recent call last):
File "posfinder.py", line 137, in <module>
POS_TAGGED = find_pos(DATA, COLUMN, WANTED_POS)
File "posfinder.py", line 36, in find_pos
df = ddf.compute()
File "/home/bertil/anaconda3/lib/python3.7/site-packages/dask/base.py", line 175, in compute
(result,) = compute(self, traverse=False, **kwargs)
File "/home/bertil/anaconda3/lib/python3.7/site-packages/dask/base.py", line 446, in compute
results = schedule(dsk, keys, **kwargs)
File "/home/bertil/anaconda3/lib/python3.7/site-packages/dask/threaded.py", line 82, in get
**kwargs
File "/home/bertil/anaconda3/lib/python3.7/site-packages/dask/local.py", line 491, in get_async
raise_exception(exc, tb)
File "/home/bertil/anaconda3/lib/python3.7/site-packages/dask/compatibility.py", line 130, in reraise
raise exc
File "/home/bertil/anaconda3/lib/python3.7/site-packages/dask/local.py", line 233, in execute_task
result = _execute_task(task, data)
File "/home/bertil/anaconda3/lib/python3.7/site-packages/dask/core.py", line 119, in _execute_task
return func(*args2)
File "/home/bertil/anaconda3/lib/python3.7/site-packages/dask/optimization.py", line 1059, in __call__
return core.get(self.dsk, self.outkey, dict(zip(self.inkeys, args)))
File "/home/bertil/anaconda3/lib/python3.7/site-packages/dask/core.py", line 149, in get
result = _execute_task(task, cache)
File "/home/bertil/anaconda3/lib/python3.7/site-packages/dask/core.py", line 119, in _execute_task
return func(*args2)
File "/home/bertil/anaconda3/lib/python3.7/site-packages/dask/compatibility.py", line 107, in apply
return func(*args, **kwargs)
File "/home/bertil/anaconda3/lib/python3.7/site-packages/dask/dataframe/core.py", line 4826, in apply_and_enforce
df = func(*args, **kwargs)
File "/home/bertil/anaconda3/lib/python3.7/site-packages/dask/utils.py", line 854, in __call__
return getattr(obj, self.method)(*args, **kwargs)
File "/home/bertil/anaconda3/lib/python3.7/site-packages/pandas/core/series.py", line 3591, in apply
mapped = lib.map_infer(values, f, convert=convert_dtype)
File "pandas/_libs/lib.pyx", line 2217, in pandas._libs.lib.map_infer
File "posfinder.py", line 33, in <lambda>
ddf['tokens'] = ddf['Message'].apply(lambda text: nlp(text),
File "/home/bertil/anaconda3/lib/python3.7/site-packages/stanfordnlp/pipeline/core.py", line 176, in __call__
self.process(doc)
File "/home/bertil/anaconda3/lib/python3.7/site-packages/stanfordnlp/pipeline/core.py", line 170, in process
self.processors[processor_name].process(doc)
File "/home/bertil/anaconda3/lib/python3.7/site-packages/stanfordnlp/pipeline/lemma_processor.py", line 66, in process
ps, es = self.trainer.predict(b, self.config['beam_size'])
File "/home/bertil/anaconda3/lib/python3.7/site-packages/stanfordnlp/models/lemma/trainer.py", line 88, in predict
preds, edit_logits = self.model.predict(src, src_mask, pos=pos, beam_size=beam_size)
File "/home/bertil/anaconda3/lib/python3.7/site-packages/stanfordnlp/models/common/seq2seq_model.py", line 172, in predict
h_in, (hn, cn) = self.encode(enc_inputs, src_lens)
File "/home/bertil/anaconda3/lib/python3.7/site-packages/stanfordnlp/models/common/seq2seq_model.py", line 116, in encode
packed_h_in, (hn, cn) = self.encoder(packed_inputs, (self.h0, self.c0))
File "/home/bertil/anaconda3/lib/python3.7/site-packages/torch/nn/modules/module.py", line 547, in __call__
result = self.forward(*input, **kwargs)
File "/home/bertil/anaconda3/lib/python3.7/site-packages/torch/nn/modules/rnn.py", line 562, in forward
return self.forward_packed(input, hx)
File "/home/bertil/anaconda3/lib/python3.7/site-packages/torch/nn/modules/rnn.py", line 554, in forward_packed
output, hidden = self.forward_impl(input, hx, batch_sizes, max_batch_size, sorted_indices)
File "/home/bertil/anaconda3/lib/python3.7/site-packages/torch/nn/modules/rnn.py", line 523, in forward_impl
self.check_forward_args(input, hx, batch_sizes)
File "/home/bertil/anaconda3/lib/python3.7/site-packages/torch/nn/modules/rnn.py", line 500, in check_forward_args
'Expected hidden[0] size {}, got {}')
File "/home/bertil/anaconda3/lib/python3.7/site-packages/torch/nn/modules/rnn.py", line 166, in check_hidden_size
raise RuntimeError(msg.format(expected_hidden_size, tuple(hx.size())))
RuntimeError: Expected hidden[0] size (2, 10, 100), got (2, 5, 100)
terminate called without an active exception
Aborted (core dumped)
错误：
driver.implicitly_wait(15)