pickle

问题 import pickle imelda = ('More Mayhem', 'IMelda May', '2011', ((1, 'Pulling the Rug'), (2, 'Psycho'), (3, 'Mayhem'), (4, 'Kentish Town Waltz'))) with open("imelda.pickle", "wb") as pickle_file: pickle.dump(imelda, pickle_file) I am trying to execute this code, but the console keeps telling me: module 'pickle' has no attribute 'dump' Do I have to install pickle via pip? I am not sure what is happening here. 回答1: Happened to me too. I had a file called pickle.py in my current directory. Just

问题 I'm doing a fair amount of parallel processing in Python using the multiprocessing module. I know certain objects CAN be pickle (thus passed as arguments in multi-p) and others can't. E.g. class abc(): pass a=abc() pickle.dumps(a) 'ccopy_reg\n_reconstructor\np1\n(c__main__\nabc\np2\nc__builtin__\nobject\np3\nNtRp4\n.' But I have some larger classes in my code (a dozen methods, or so), and this happens: a=myBigClass() pickle.dumps(a) Traceback (innermost last): File "<stdin>", line 1, in

问题 My original issue is that I am trying to do the following: def submit_decoder_process(decoder, input_line): decoder.process_line(input_line) return decoder self.pool = Pool(processes=num_of_processes) self.pool.apply_async(submit_decoder_process, [decoder, input_line]).get() decoder is a bit involved to describe here, but the important thing is that decoder is an object that is initialized with PyParsing expression that calls setParseAction(). This fails pickle that multiprocessing uses and

问题 I am trying to I am tring to delete stop words via spark,the code is as follow from nltk.corpus import stopwords from pyspark.context import SparkContext from pyspark.sql.session import SparkSession sc = SparkContext('local') spark = SparkSession(sc) word_list=["ourselves","out","over", "own", "same" ,"shan't" ,"she", "she'd", "what", "the", "fuck", "is", "this","world","too","who","who's","whom","yours","yourself","yourselves"] wordlist=spark.createDataFrame([word_list]).rdd def stopwords

问题 Another pickling question ... The following leads to pickling errors. I think it is to do with the scoping or something. Am not sure yet. The goal is to have a decorator that takes arguments and enriches a function with methods. If the best way is to simply construct classes explicitly then that is fine but this is meant to hide things from users writing "content". import concurrent.futures import functools class A(): def __init__(self, fun, **kwargs): self.fun = fun self.stuff = kwargs

问题 Another pickling question ... The following leads to pickling errors. I think it is to do with the scoping or something. Am not sure yet. The goal is to have a decorator that takes arguments and enriches a function with methods. If the best way is to simply construct classes explicitly then that is fine but this is meant to hide things from users writing "content". import concurrent.futures import functools class A(): def __init__(self, fun, **kwargs): self.fun = fun self.stuff = kwargs

问题 I have a large object in my Python3 code which, when tried to be pickled with the pickle module throws the following error: TypeError: cannot serialize '_io.BufferedReader' object However, dill.dump() and dill.load() are able to save and restore the object seamlessly. What causes the trouble for the pickle module? Now that dill saves and reconstructs the object without any error, is there any way to verify if the pickling and unpickling with dill went well? How's it possible that pickle fails

问题 I have a large object in my Python3 code which, when tried to be pickled with the pickle module throws the following error: TypeError: cannot serialize '_io.BufferedReader' object However, dill.dump() and dill.load() are able to save and restore the object seamlessly. What causes the trouble for the pickle module? Now that dill saves and reconstructs the object without any error, is there any way to verify if the pickling and unpickling with dill went well? How's it possible that pickle fails

问题 I currently have a neural network module: import torch.nn as nn class NN(nn.Module): def __init__(self,args,lambda_f,nn1, loss, opt): super().__init__() self.args = args self.lambda_f = lambda_f self.nn1 = nn1 self.loss = loss self.opt = opt # more nn.Params stuff etc... def forward(self, x): #some code using fields return out I am trying to checkpoint it but because pytorch saves using state_dict s it means I can't save the lambda functions I was actually using if I checkpoint with the

问题 I currently have a neural network module: import torch.nn as nn class NN(nn.Module): def __init__(self,args,lambda_f,nn1, loss, opt): super().__init__() self.args = args self.lambda_f = lambda_f self.nn1 = nn1 self.loss = loss self.opt = opt # more nn.Params stuff etc... def forward(self, x): #some code using fields return out I am trying to checkpoint it but because pytorch saves using state_dict s it means I can't save the lambda functions I was actually using if I checkpoint with the