pickle

问题 I've written a small python script that uses multiprocessing (See https://stackoverflow.com/a/41875711/1878788). It works when I test it: $ ./forkiter.py 0 1 2 3 4 sum of x+1: 15 sum of 2*x: 20 sum of x*x: 30 But when I try to profile it with cProfile , I get the following: $ python3.6 -m cProfile -o forkiter.prof ./forkiter.py 0 1 2 3 4 Traceback (most recent call last): File "/home/bli/lib/python3.6/runpy.py", line 193, in _run_module_as_main "__main__", mod_spec) File "/home/bli/lib

问题 I am building a wrapper for Luigi Tasks and I ran into a snag with the Register class that's actually an ABC metaclass and not being pickable when I create a dynamic type . The following code, more or less, is what I'm using to develop the dynamic class. class TaskWrapper(object): '''Luigi Spark Factory from the provided JobClass Args: JobClass(ScrubbedClass): The job to wrap options: Options as passed into the JobClass ''' def __new__(self, JobClass, **options): # Validate we have a good job

问题 i can not send my numpy array in socket, i use pickle but il my client pickle crash with this error: pickle data was truncated my server : i create numpy array and i want to send in my client with pickle (it's work) import socket, pickle import numpy as np from PIL import ImageGrab import cv2 while(True): HOST = 'localhost' PORT = 50007 s = socket.socket(socket.AF_INET, socket.SOCK_STREAM) s.setsockopt(socket.SOL_SOCKET, socket.SO_SNDBUF, 4096) s.bind((HOST, PORT)) s.listen(1) conn, addr = s

问题 I'm starting to dive into deploying a predictive model to a web app using Flask, and unfortunately getting stuck at the starting gate. What I did: I pickled my model in my model.py program: import numpy as np from sklearn.externals import joblib class NeuralNetwork(): """ Two (hidden) layer neural network model. First and second layer contain the same number of hidden units """ def __init__(self, input_dim, units, std=0.0001): self.params = {} self.input_dim = input_dim self.params['W1'] = np

问题 I am using the below code to save a random forest model. I am using cPickle to save the trained model. As I see new data, can I train the model incrementally. Currently, the train set has about 2 years data. Is there a way to train on another 2 years and (kind of) append it to the existing saved model. rf = RandomForestRegressor(n_estimators=100) print ("Trying to fit the Random Forest model --> ") if os.path.exists('rf.pkl'): print ("Trained model already pickled -- >") with open('rf.pkl',

问题 Working on a project that requires that I am able to pickle the container object at any point, since we expect it to fail on external conditions quite frequently and be able to fully pick up where we left off. I'm using the python logging library quite extensively, and all of my classes start by setting up a logger like: class foo: def __init__(self): self.logger = logging.getLogger("package.foo") Since I'm pickling a container class, it has several layers of classes within it, each with

问题 How do I "pickle" instances of Django models in a database into sample python code I can use to load sample data? I want to: 1) Take a snapshot of several hundred records that I have stored in a MySQL database for a Django project 2) Take this snapshot and modify the data in it (blanking out names) 3) Transform this data into a "pickled string" or actual python code that I can use to load the data into a new users account. The main feature I'm trying to implement is to select one of my

问题 I would like to serialize on machine A and deserialize on machine B a python lambda. There are a couple of obvious problems with that: the pickle module does not serialize or deserialize code. It only serializes the names of classes/methods/functions some of the answers I found with google suggest the use of the low-level marshal module to serialize the func_code attribute of the lambda but they fail to describe how one could reconstruct a function object from the deserialized code object

问题 Closed . This question is opinion-based. It is not currently accepting answers. Want to improve this question? Update the question so it can be answered with facts and citations by editing this post. Closed 3 years ago . Python has both the pickle and cPickle modules for serialization. cPickle has an obvious advantage over pickle : speed. What, if any, advantage does pickle have over cPickle ? 回答1: The pickle module implements an algorithm for turning an arbitrary Python object into a series

问题 In django_session table session_data is stored which is first pickled using pickle module of python and then encoded in base64 by using base64 module of python. I got the decoded pickled session_data. session_data from django_session table: gAJ9cQEoVQ9fc2Vzc2lvbl9leHBpcnlxAksAVRJfYXV0aF91c2VyX2JhY2tlbmRxA1UpZGphbmdvLmNvbnRyaWIuYXV0aC5iYWNrZW5kcy5Nb2RlbEJhY2tlbmRxBFUNX2F1dGhfdXNlcl9pZHEFigECdS5iZmUwOWExOWI0YTZkN2M0NDc2MWVjZjQ5ZDU0YjNhZA== after decoding it by base64.decode(session_data): \x80