I can't load my model because I can't put a PosixPath

According to provided error message you are using pathlib. So you don’t need to use + '/' + here: str(path) + '/' + str(fname)

/ as path separator works on Linux/Unix:

state = pickle.load(open(path / fname, 'rb'))

On Windows use .joinpath() instead:

state = pickle.load(open(path.joinpath(fname), 'rb'))

If you are not going to use the pathlib, use os.path.join(). It will automatically select right format for your OS.

According my own question, I find a way using:

from pathlib import Path

folder_path = Path('./models/model1')

UPDATE 1

This solution only works on Linux, on Windows I still get an error.

The issue here is related to the differences in the way Python handles paths depending on the OS:

• PosixPath – on Linux / Unix

• WindowsPath – on Windows

When persisting objects using pickle on one OS (say Linux – as in this case) information about the type / class is persisted too (here: PosixPath).

Now, when the pickle file is being loaded Python assumes it is going to be able to recreate objects based on the type information it previously persisted. In this case it tries to recreate an object of PosixPath type which is prevented by pathlib library and cannot be instantiated on Windows. On Windows there should be WindowsPath be used instead but pickle module does not handle such OS-dependent logic very well hence it helplessly throws the error.

You could theoretically interfere in the code of pathlib to remove the OS check but there is no easy workaround but avoiding pickling of OS-dependent objects (e.g. storing paths as strings – as os.path does – would certainly workaround this issue).

There is also another possibility – to use a platform-independent PurePosixPath class for path objects.

I am working on the same- deploying fastai model as a web server and got the same issue and this is what I did…
While exporting the model use joblib or pickle to pickle the model instead of using learn.export() and in server use the below code.

 __model = pickle.load(open(os.path.join('./artifacts/saved_model.pkl'), 'rb'))

By doing this it is able to solve the path issue but since the model is trained using GPU it gives the error which asks to map the storage to CPU

For the posix path error:
when you train your model on colab/gradient and download it,
then do inference on Windows.

Just redirect PosixPath to WindowsPath:

import pathlib

temp = pathlib.PosixPath
pathlib.PosixPath = pathlib.WindowsPath

When working on Windows, you could temporarily set pathlib.PosixPath to WindowsPath. It is important to revert to original value, especially when there is an exception during the loading of pickle.

A simple way is to do a try / finally:

posix_backup = pathlib.PosixPath
try:
    pathlib.PosixPath = pathlib.WindowsPath
    learn_inf = load_learner(EXPORT_PATH)
finally:
    pathlib.PosixPath = posix_backup

If you do it a lot, you could make the flow smoother as follows:

1. Define a function that could temporarily do the change
2. Use it in a with block

You could add this somewhere (top of a script or dedicated cell if you use Jupyter).

from contextlib import contextmanager
import pathlib

@contextmanager
def set_posix_windows():
    posix_backup = pathlib.PosixPath
    try:
        pathlib.PosixPath = pathlib.WindowsPath
        yield
    finally:
        pathlib.PosixPath = posix_backup

And then, just use it like this:

EXPORT_PATH = pathlib.Path("model.pkl")

with set_posix_windows():
    learn_inf = load_learner(EXPORT_PATH)

… also, check the answer from sophros: https://stackoverflow.com/a/62836990/1603480

use this for local use of fastai on windows :

import pathlib
temp = pathlib.PosixPath
pathlib.PosixPath = pathlib.WindowsPath