Traceback (most recent call last):


 "/home/ubuntu/workspace/automlbenchmark/amlb/benchmark.py", line 391, in run

meta_result = framework.run(self._dataset, task_config)


 "/home/ubuntu/workspace/automlbenchmark/frameworks/autogluon_nobag/__init__.py", line 4, in run

return run(*args, **kwargs)


 "/home/ubuntu/workspace/automlbenchmark/frameworks/autogluon_nobag/exec.py", line 16, in run

return exec_template.run(dataset=dataset, config=config, parameters=parameters)


 "/home/ubuntu/workspace/automlbenchmark/frameworks/autogluon/exec_template.py", line 82, in run

**fit_params


 "/home/ubuntu/workspace/autogluon/autogluon/task/tabular_prediction/tabular_prediction.py", line 425, in fit

hyperparameters=hyperparameters, time_limit=time_limits_orig, save_data=cache_data, verbosity=verbosity)


 "/home/ubuntu/workspace/autogluon/autogluon/utils/tabular/ml/learner/default_learner.py", line 99, in fit

hyperparameters=hyperparameters)


 "/home/ubuntu/workspace/autogluon/autogluon/utils/tabular/ml/trainer/auto_trainer.py", line 39, in train

self.train_multi_and_ensemble(X_train, y_train, X_test, y_test, models, hyperparameter_tune=hyperparameter_tune, feature_prune=feature_prune)


 "/home/ubuntu/workspace/autogluon/autogluon/utils/tabular/ml/trainer/abstract_trainer.py", line 507, in train_multi_and_ensemble

self.train_multi_levels(X_train, y_train, X_test, y_test, models=models, hyperparameter_tune=hyperparameter_tune, feature_prune=feature_prune, level_start=0, level_end=self.stack_ensemble_levels)


 "/home/ubuntu/workspace/autogluon/autogluon/utils/tabular/ml/trainer/abstract_trainer.py", line 522, in train_multi_levels

self.stack_new_level(X=X_train, y=y_train, X_test=X_test, y_test=y_test, models=models, level=level, hyperparameter_tune=hyperparameter_tune, feature_prune=feature_prune, time_limit_core=time_limit_core, time_limit_aux=time_limit_aux)


 "/home/ubuntu/workspace/autogluon/autogluon/utils/tabular/ml/trainer/abstract_trainer.py", line 533, in stack_new_level

aux_models = self.stack_new_level_aux(X=X_test, y=y_test, fit=False, level=level+1, time_limit=time_limit_aux)


 "/home/ubuntu/workspace/autogluon/autogluon/utils/tabular/ml/trainer/abstract_trainer.py", line 568, in stack_new_level_aux

X_train_stack_preds = self.get_inputs_to_stacker(X, level_start=0, level_end=level, fit=fit)


 "/home/ubuntu/workspace/autogluon/autogluon/utils/tabular/ml/trainer/abstract_trainer.py", line 797, in get_inputs_to_stacker

X = dummy_stackers[level+1].preprocess(X=X, preprocess=False, fit=False, compute_base_preds=True)


 "/home/ubuntu/workspace/autogluon/autogluon/utils/tabular/ml/models/ensemble/stacker_ensemble_model.py", line 92, in preprocess

y_pred_proba = base_model.predict_proba(X)


 "/home/ubuntu/workspace/autogluon/autogluon/utils/tabular/ml/models/tabular_nn/tabular_nn_model.py", line 384, in predict_proba

return self._predict_tabular_data(new_data=X, process=True, predict_proba=True)


 "/home/ubuntu/workspace/autogluon/autogluon/utils/tabular/ml/models/tabular_nn/tabular_nn_model.py", line 398, in _predict_tabular_data

new_data = self.process_test_data(new_data, batch_size=self.batch_size, num_dataloading_workers=self.num_dataloading_workers, labels=None)


 "/home/ubuntu/workspace/autogluon/autogluon/utils/tabular/ml/models/tabular_nn/tabular_nn_model.py", line 486, in process_test_data

problem_type=self.problem_type, labels=labels, is_test=True)


 "/home/ubuntu/workspace/autogluon/autogluon/utils/tabular/ml/models/tabular_nn/tabular_nn_dataset.py", line 150, in __init__

self.generate_dataset_and_dataloader(data_list=data_list)


 "/home/ubuntu/workspace/autogluon/autogluon/utils/tabular/ml/models/tabular_nn/tabular_nn_dataset.py", line 158, in generate_dataset_and_dataloader

num_workers=self.num_dataloading_workers)
# no need to shuffle test data


 "/home/ubuntu/virtual/automlbenchmark/lib/python3.6/site-packages/mxnet/gluon/data/dataloader.py", line 642, in __init__

initargs=[self._dataset, is_np_shape(), is_np_array()])


 "/home/ubuntu/anaconda3/lib/python3.6/multiprocessing/context.py", line 119, in Pool

context=self.get_context())


 "/home/ubuntu/anaconda3/lib/python3.6/multiprocessing/pool.py", line 174, in __init__

self._repopulate_pool()


 "/home/ubuntu/anaconda3/lib/python3.6/multiprocessing/pool.py", line 239, in _repopulate_pool

w.start()


 "/home/ubuntu/anaconda3/lib/python3.6/multiprocessing/process.py", line 105, in start

self._popen = self._Popen(self)


 "/home/ubuntu/anaconda3/lib/python3.6/multiprocessing/context.py", line 277, in _Popen

return Popen(process_obj)


 "/home/ubuntu/anaconda3/lib/python3.6/multiprocessing/popen_fork.py", line 19, in __init__

self._launch(process_obj)


 "/home/ubuntu/anaconda3/lib/python3.6/multiprocessing/popen_fork.py", line 66, in _launch

self.pid = os.fork()
OSError: [Errno 12] Cannot allocate memory

Traceback (most recent call last):


 "ocrdection.py", line 44, in <module>

print pytesseract.image_to_string(Image.open('test.png'))


 "/Users/padalingam/anaconda2/lib/python2.7/site-packages/pytesseract/pytesseract.py", line 193, in image_to_string

return run_and_get_output(image, 'txt', lang, config, nice)


 "/Users/padalingam/anaconda2/lib/python2.7/site-packages/pytesseract/pytesseract.py", line 140, in run_and_get_output

run_tesseract(**kwargs)


 "/Users/padalingam/anaconda2/lib/python2.7/site-packages/pytesseract/pytesseract.py", line 111, in run_tesseract

proc = subprocess.Popen(command, stderr=subprocess.PIPE)


 "/Users/padalingam/anaconda2/lib/python2.7/subprocess.py", line 390, in __init__

errread, errwrite)


 "/Users/padalingam/anaconda2/lib/python2.7/subprocess.py", line 1025, in _execute_child

raise child_exception
OSError: [Errno 2] No such file or directory
