I saved my list with the pickle module. After restarting the program, if I query the list contents without adding new data, I can see the records, but if I add new data, I cannot see the old records. Why i can’t see old records ? Answer It’s because you are starting the program with an empty list. you should
Tag: pickle
Saving and Loading a Vowpal Wabbit model in python with –safe_resume –cb_explore
I would like to do online training with a contextual Vowpal Wabbit model, so I need to save and reload frequently. However, whenever I try to reload a model (which was initialized with –save_resume), I get an exception with: Example Code to reproduce: Python 3.8.5 vowpalwabbit==8.10.1 If I don’t use –save_resume, load and save work, however the model performance is
How to decrease Image object size that dumped through pickle in Python
I’m working on a socket data transfer project. I want to watch client screen. I’m using pillow and pickle module both server and client but when I trying to send ImageGrab.grab() object, object size is 3Mb. It’s very high data for transferring. Although object size is 3MB, saved size (ImageGrab.grab().save(“example.jpg”)) is 200 kb. When i save file then read saved
Pickle data not loading
Here I the data I try to save as a “pickle file” Then in a separate script i open it: It seems to run smoothly, however the only thing that appears in my variable explorer is a Buffered Reader Object. Answer pkl.load returns the loaded object. Your code immediately discards it. You should assign in to a variable:
Python 3.9: unpickling of IsoCalendarDate data returns a tuple
Consider the following discussion / feature in Python3.9: https://bugs.python.org/issue24416 In short, it was decided that the result of datetime.date.isocalendar would be changed to a namedtuple instead of tuple. Now, I can see the benefit in doing that, but they also decided “to pickle” the new object (datetime.IsoCalendarDate) as a tuple: https://github.com/python/cpython/commit/1b97b9b0ad9a2ff8eb5c8f2e2e7c2aec1d13a330#diff-2a8962dcecb109859cedd81ddc5729bea57d156e0947cb8413f99781a0860fd1R1214 So my question is, why did they make it
PyTorch – RuntimeError: [enforce fail at inline_container.cc:209] . file not found: archive/data.pkl
Problem I’m trying to load a file using PyTorch, but the error states archive/data.pkl does not exist. Code Output Hypothesis I’m guessing this has something to do with pickle, from the docs: This save/load process uses the most intuitive syntax and involves the least amount of code. Saving a model in this way will save the entire module using Python’s
Why python pickle does not work dos to unix?
Few days ago I coded a mini game to learn socket and threading. When I run my game server and client on windows it just works perfectly but when I moved my server file to my test server it gives me this pickle error: What could be the problem? Whole game files: ——————————————————————————— Codes that might help: server.py: app.py: Answer
Pickle all variables
I”m looking for a way to pickle all the variables with their values in a sorted order. I’m trying the method below: Expected result: Append value of va to totalList , then vb’s value..and so on. And then dump the totalList to a pickle file. I’m getting result like this: I want to get rid of the results (‘pickle’, ‘time’,
Cannot pickle Tensorflow object in Python – TypeError: can’t pickle _thread._local objects
I want to pickle the history object after running a keras fit on tensorflow. But I am getting an error. It gives me the following error: PS: To get the code to run, download the fashion_mnist data: https://s3.amazonaws.com/img-datasets/mnist.pkl.g Answer As Karl suggested, the history object cannot be pickled. But it’s dictionary can:
How to use pickle to save sklearn model
I want to dump and load my Sklearn trained model using Pickle. How to do that? Answer Save: Load: In the specific case of scikit-learn, it may be better to use joblib’s replacement of pickle (dump & load), which is more efficient on objects that carry large numpy arrays internally as is often the case for fitted scikit-learn estimators: Save: