Question

我正在尝试在我们的HPC集群上运行分布式超参数优化。这段代码在sklearn上运行正常（缓慢），但是使用内置的dask功能，工作人员会立即因关键错误而失败。

client = Client('<ip>')
import numpy as np
import dask.array as da
X_train = np.load(<some numpy x binary>)
Y_train = np.load(<some numpy y binary>)

X_train = da.from_array(X_train, chunks=50000)
Y_train = da.from_array(Y_train, chunks=50000)

#from xgboost import XGBClassifier # These work
from dask_xgboost import XGBClassifier
#from sklearn.model_selection import GridSearchCV # These work
from dask_ml.model_selection import GridSearchCV
opt_clf = XGBClassifier()
hyparams={}
for par, val in param.items():
    hyparams[par]=[val]
hyparams["eta"] = [0.2,0.5,0.9]
search = GridSearchCV(opt_clf, hyparams, scoring='f1_macro')

任务已分配给工人，我可以看到他们身上保留着的记忆，但是所有火车作业都失败了：

---------------------------------------------------------------------------
KeyError                                  Traceback (most recent call last)
<ipython-input-12-cfe3d66f9554> in <module>
      1 get_ipython().run_line_magic('time', '')
----> 2 search.fit(X_train, Y_train)
      3 search.cv_results_

~/.local/lib/python3.6/site-packages/dask_ml/model_selection/_search.py in fit(self, X, y, groups, **fit_params)
   1264                     else:
   1265                         logger.warning("{} has failed... retrying".format(future.key))
-> 1266                         future.retry()
   1267                         ac.add(future)
   1268 

~/.local/lib/python3.6/site-packages/distributed/client.py in retry(self, **kwargs)
    310         Client.retry
    311         """
--> 312         return self.client.retry([self], **kwargs)
    313 
    314     def cancelled(self):

~/.local/lib/python3.6/site-packages/distributed/client.py in retry(self, futures, asynchronous)
   2234         futures: list of Futures
   2235         """
-> 2236         return self.sync(self._retry, futures, asynchronous=asynchronous)
   2237 
   2238     async def _publish_dataset(self, *args, name=None, **kwargs):

~/.local/lib/python3.6/site-packages/distributed/client.py in sync(self, func, asynchronous, callback_timeout, *args, **kwargs)
    831         else:
    832             return sync(
--> 833                 self.loop, func, *args, callback_timeout=callback_timeout, **kwargs
    834             )
    835 

~/.local/lib/python3.6/site-packages/distributed/utils.py in sync(loop, func, callback_timeout, *args, **kwargs)
    337     if error[0]:
    338         typ, exc, tb = error[0]
--> 339         raise exc.with_traceback(tb)
    340     else:
    341         return result[0]

~/.local/lib/python3.6/site-packages/distributed/utils.py in f()
    321             if callback_timeout is not None:
    322                 future = asyncio.wait_for(future, callback_timeout)
--> 323             result[0] = yield future
    324         except Exception as exc:
    325             error[0] = sys.exc_info()

~/.local/lib/python3.6/site-packages/tornado/gen.py in run(self)
    733 
    734                     try:
--> 735                         value = future.result()
    736                     except Exception:
    737                         exc_info = sys.exc_info()

~/.local/lib/python3.6/site-packages/distributed/client.py in _retry(self, futures)
   2223         response = await self.scheduler.retry(keys=keys, client=self.id)
   2224         for key in response:
-> 2225             st = self.futures[key]
   2226             st.retry()
   2227 

KeyError: 'xgbclassifier-a93a193c47acb611cde5ecdeb7347809'

有人对此问题有任何提示或调试方法吗？似乎工人只是没有返回经过训练的模型。

谢谢

dask_ml GridSearchCV分布式崩溃，出现KeyError

0 个答案: