Use new data
Compute tight constraints for training set (n_jobs = 28)

_RemoteTraceback Traceback (most recent call last)
_RemoteTraceback:
"""
Traceback (most recent call last):
File "D:\ProgramData\Anaconda3\envs\Python36\lib\site-packages\joblib\externals\loky\process_executor.py", line 404, in _process_worker
call_item = call_queue.get(block=True, timeout=timeout)
File "D:\ProgramData\Anaconda3\envs\Python36\lib\multiprocessing\queues.py", line 113, in get
return _ForkingPickler.loads(res)
File "", line 971, in _find_and_load
File "", line 955, in _find_and_load_unlocked
File "", line 656, in _load_unlocked
File "", line 626, in load_backward_compatible
File "D:\ProgramData\Anaconda3\envs\Python36\lib\site-packages\mlopt-0.0.2-py3.6.egg\mlopt_init.py", line 1, in
from mlopt.optimizer import Optimizer
File "", line 971, in _find_and_load
File "", line 955, in _find_and_load_unlocked
File "", line 656, in _load_unlocked
File "", line 626, in _load_backward_compatible
File "D:\ProgramData\Anaconda3\envs\Python36\lib\site-packages\mlopt-0.0.2-py3.6.egg\mlopt\optimizer.py", line 4, in
from mlopt.learners import LEARNER_MAP, installed_learners
File "", line 971, in _find_and_load
File "", line 955, in _find_and_load_unlocked
File "", line 656, in _load_unlocked
File "", line 626, in load_backward_compatible
File "D:\ProgramData\Anaconda3\envs\Python36\lib\site-packages\mlopt-0.0.2-py3.6.egg\mlopt\learners_init.py", line 1, in
from mlopt.learners.pytorch.pytorch import PytorchNeuralNet
File "", line 971, in _find_and_load
File "", line 955, in _find_and_load_unlocked
File "", line 656, in _load_unlocked
File "", line 626, in _load_backward_compatible
File "D:\ProgramData\Anaconda3\envs\Python36\lib\site-packages\mlopt-0.0.2-py3.6.egg\mlopt\learners\pytorch\pytorch.py", line 3, in
from optuna.integration import PyTorchLightningPruningCallback
File "", line 1020, in handle_fromlist
File "D:\ProgramData\Anaconda3\envs\Python36\lib\site-packages\optuna\integration_init.py", line 91, in getattr
module = self.get_module(self.class_to_module[name])
File "D:\ProgramData\Anaconda3\envs\Python36\lib\site-packages\optuna\integration_init.py", line 103, in get_module
return importlib.import_module("." + module_name, self.name)
File "D:\ProgramData\Anaconda3\envs\Python36\lib\importlib_init.py", line 126, in import_module
return bootstrap.gcd_import(name[level:], package, level)
File "D:\ProgramData\Anaconda3\envs\Python36\lib\site-packages\optuna\integration\pytorch_lightning.py", line 7, in
from pytorch_lightning import LightningModule
File "D:\ProgramData\Anaconda3\envs\Python36\lib\site-packages\pytorch_lightning_init.py", line 56, in
from pytorch_lightning.core import LightningDataModule, LightningModule
File "D:\ProgramData\Anaconda3\envs\Python36\lib\site-packages\pytorch_lightning\core_init.py", line 2, in
from pytorch_lightning.core.datamodule import LightningDataModule
File "D:\ProgramData\Anaconda3\envs\Python36\lib\site-packages\pytorch_lightning\core\datamodule.py", line 21, in
import torch
File "D:\ProgramData\Anaconda3\envs\Python36\lib\site-packages\torch_init.py", line 124, in
raise err
OSError: [WinError 1455] 页面文件太小，无法完成操作。 Error loading "D:\ProgramData\Anaconda3\envs\Python36\lib\site-packages\torch\lib\caffe2_detectron_ops_gpu.dll" or one of its dependencies.
"""

The above exception was the direct cause of the following exception:

BrokenProcessPool Traceback (most recent call last)
in
6
7 # Train solver
----> 8 m.train(theta_train, learner=mlopt.PYTORCH)

D:\ProgramData\Anaconda3\envs\Python36\lib\site-packages\mlopt-0.0.2-py3.6.egg\mlopt\optimizer.py in train(self, X, sampling_fn, parallel, learner, filter_strategies, **learner_options)
319 self.get_samples(X, sampling_fn,
320 parallel=parallel,
--> 321 filter_strategies=filter_strategies)
322
323 # Define learner

D:\ProgramData\Anaconda3\envs\Python36\lib\site-packages\mlopt-0.0.2-py3.6.egg\mlopt\optimizer.py in get_samples(self, X, sampling_fn, parallel, filter_strategies)
224 message="Compute " +
225 "tight constraints " +
--> 226 "for training set")
227
228 stg.logger.info("Checking for infeasible points")

D:\ProgramData\Anaconda3\envs\Python36\lib\site-packages\mlopt-0.0.2-py3.6.egg\mlopt\problem.py in solve_parametric(self, theta, batch_size, parallel, message)
380 results = Parallel(n_jobs=n_jobs, batch_size=batch_size)(
381 delayed(self.populate_and_solve)(theta.iloc[i])
--> 382 for i in tqdm(range(n))
383 )
384

D:\ProgramData\Anaconda3\envs\Python36\lib\site-packages\joblib\parallel.py in call(self, iterable)
1040
1041 with self._backend.retrieval_context():
-> 1042 self.retrieve()
1043 # Make sure that we get a last message telling us we are done
1044 elapsed_time = time.time() - self._start_time

D:\ProgramData\Anaconda3\envs\Python36\lib\site-packages\joblib\parallel.py in retrieve(self)
919 try:
920 if getattr(self._backend, 'supports_timeout', False):
--> 921 self._output.extend(job.get(timeout=self.timeout))
922 else:
923 self._output.extend(job.get())

D:\ProgramData\Anaconda3\envs\Python36\lib\site-packages\joblib_parallel_backends.py in wrap_future_result(future, timeout)
538 AsyncResults.get from multiprocessing."""
539 try:
--> 540 return future.result(timeout=timeout)
541 except CfTimeoutError:
542 raise TimeoutError()

D:\ProgramData\Anaconda3\envs\Python36\lib\concurrent\futures_base.py in result(self, timeout)
430 raise CancelledError()
431 elif self._state == FINISHED:
--> 432 return self.__get_result()
433 else:
434 raise TimeoutError()

D:\ProgramData\Anaconda3\envs\Python36\lib\concurrent\futures_base.py in __get_result(self)
382 def __get_result(self):
383 if self._exception:
--> 384 raise self._exception
385 else:
386 return self._result

BrokenProcessPool: A task has failed to un-serialize. Please ensure that the arguments of the function are all picklable.