In Python 2.x and 3.0, 3.1 and 3.2, multiprocessing.Pool() objects are not context managers. You cannot use them in a with statement. Only in Python 3.3 and up can you use them as such. From the Python 3 multiprocessing.Pool() documentation: New in version 3.3: Pool objects now support the context management protocol – see Context … Read more
pickling is recursive, not sequential. Thus, to pickle a list, pickle will start to pickle the containing list, then pickle the first element… diving into the first element and pickling dependencies and sub-elements until the first element is serialized. Then moves on to the next element of the list, and so on, until it finally … Read more
Here is a simple workaround for issue 24658. Use pickle.loads or pickle.dumps and break the bytes object into chunks of size 2**31 – 1 to get it in or out of the file. import pickle import os.path file_path = “pkl.pkl” n_bytes = 2**31 max_bytes = 2**31 – 1 data = bytearray(n_bytes) ## write bytes_out = … Read more
cPickle comes with the standard library… in python 2.x. You are on python 3.x, so if you want cPickle, you can do this: >>> import _pickle as cPickle However, in 3.x, it’s easier just to use pickle. No need to install anything. If something requires cPickle in python 3.x, then that’s probably a bug.
np.ndarray uses __reduce__ to pickle itself. We can take a look at what it actually returns when you call that function to get an idea of what’s going on: >>> obj = RealisticInfoArray([1, 2, 3], info=’foo’) >>> obj.__reduce__() (<built-in function _reconstruct>, (<class ‘pick.RealisticInfoArray’>, (0,), ‘b’), (1, (3,), dtype(‘int64’), False, ‘\x01\x00\x00\x00\x00\x00\x00\x00\x02\x00\x00\x00\x00\x00\x00\x00\x03\x00\x00\x00\x00\x00\x00\x00’)) So, we get a 3-tuple … Read more
The pickle module normally can’t pickle instance methods: >>> import pickle >>> class A(object): … def z(self): print “hi” … >>> a = A() >>> pickle.dumps(a.z) Traceback (most recent call last): File “<stdin>”, line 1, in <module> File “/usr/local/lib/python2.7/pickle.py”, line 1374, in dumps Pickler(file, protocol).dump(obj) File “/usr/local/lib/python2.7/pickle.py”, line 224, in dump self.save(obj) File “/usr/local/lib/python2.7/pickle.py”, line … Read more
Pickle serializes a single object at a time, and reads back a single object – the pickled data is recorded in sequence on the file. If you simply do pickle.load you should be reading the first object serialized into the file (not the last one as you’ve written). After unserializing the first object, the file-pointer … Read more
As pickle’s docs say, in order to save and restore a class instance (actually a function, too), you must respect certain constraints: pickle can save and restore class instances transparently, however the class definition must be importable and live in the same module as when the object was stored whyteboard.tools is not the “the same … Read more
This seems like some sort of incompatibility. It’s trying to load a “binstring” object, which is assumed to be ASCII, while in this case it is binary data. If this is a bug in the Python 3 unpickler, or a “misuse” of the pickler by numpy, I don’t know. Here is something of a workaround, … Read more
I got this error when running in an jupyter notebook. I think the problem was that I was using %load_ext autoreload autoreload 2. Restarting my kernel and rerunning solved the problem.