Pickling is a way to convert a python object (list, dict, etc.) into a character stream. The idea is that this character stream contains all the information necessary to reconstruct the object in another python script. As for where the pickled information is stored, usually one would do: with open(‘filename’, ‘wb’) as f: var = … Read more
It simply means it can be serialized by the pickle module. For a basic explanation of this, see What can be pickled and unpickled?. Pickling Class Instances provides more details, and shows how classes can customize the process.
You should write the pickled data with a lower protocol number in Python 3. Python 3 introduced a new protocol with the number 3 (and uses it as default), so switch back to a value of 2 which can be read by Python 2. Check the protocolparameter in pickle.dump. Your resulting code will look like … Read more
You need to either read the file first (as binary bytes) and use pickle.loads(), or pass an open file object to the pickle.load() command. The latter is preferable: with open(‘out/cache/’ +hashed_url, ‘rb’) as pickle_file: content = pickle.load(pickle_file) Neither method supports loading a pickle from a filename.
Generally Your pkl file is, in fact, a serialized pickle file, which means it has been dumped using Python’s pickle module. To un-pickle the data you can: import pickle with open(‘serialized.pkl’, ‘rb’) as f: data = pickle.load(f) For the MNIST data set Note gzip is only needed if the file is compressed: import gzip import … Read more
By choosing to open the file in mode wb, you are choosing to write in raw binary. There is no character encoding being applied. Thus to read this file, you should simply open in mode rb.
Python 2 From the Python 2 documentation, while serializing (i.e. writing to a pickle file), use: output = open(‘data.pkl’, ‘wb’) I would choose .pkl as the extension when using Python 2. Python 3 The example in the Python 3 documentation now uses .pickle as the file extension for serialization: with open(‘data.pickle’, ‘wb’) as f: pickle.dump(…) … Read more
joblib is usually significantly faster on large numpy arrays because it has a special handling for the array buffers of the numpy datastructure. To find about the implementation details you can have a look at the source code. It can also compress that data on the fly while pickling using zlib or lz4. joblib also … Read more
You could serialise the function bytecode and then reconstruct it on the caller. The marshal module can be used to serialise code objects, which can then be reassembled into a function. ie: import marshal def foo(x): return x*x code_string = marshal.dumps(foo.__code__) Then in the remote process (after transferring code_string): import marshal, types code = marshal.loads(code_string) … Read more
Pickling will serialize your list (convert it, and it’s entries to a unique byte string), so you can save it to disk. You can also use pickle to retrieve your original list, loading from the saved file. So, first build a list, then use pickle.dump to send it to a file… Python 3.4.1 (default, May … Read more