使用pickle.load时报错could not find MARK

这个错误通常是由于使用错误的pickle版本导致的。请确保你在使用pickle.load()之前和之后都使用相同版本的pickle。

如果你正在使用Python 3.x，那么你需要使用"rb"模式打开pickle文件，以二进制模式读取文件内容。例如：

import pickle

with open('filename.pkl', 'rb') as f:
    data = pickle.load(f)

如果你仍然遇到问题，可以尝试使用更高版本的pickle或者使用其他序列化库，如json或msgpack。

相关问题

magic_number = pickle_module.load(f, **pickle_load_args) _pickle.UnpicklingError: could not find MARK

This error occurs when unpickling a Python object using the pickle module. It means that the pickle data being loaded does not have the expected format and is missing a crucial marker.

Here are some possible reasons for this error:

1. The pickle data was not created using the same version of Python as the one being used to unpickle it. This can cause compatibility issues.

2. The pickle data was corrupted during transmission or storage. This can cause missing or incomplete markers.

3. The pickle data was tampered with or modified, causing it to lose its integrity.

To fix this error, you can try the following:

1. Check that the pickle data being loaded is valid and was created using the same version of Python as the one being used to unpickle it.

2. Verify that the pickle data has not been corrupted or tampered with by comparing it to a known good copy.

3. Try using a different serialization library or format to store and load your data, such as JSON or YAML.

_pickle.UnpicklingError: could not find MARK

This error occurs when Python's pickle module is unable to unpickle the object because it cannot find the MARK object. The MARK object is a special object created by the pickle module during the serialization process to mark the beginning of a new object.

There are several reasons why this error could occur:

1. The data being unpickled is corrupt or incomplete.
2. The data was pickled by a different version of Python or with different pickle settings.
3. The data was tampered with or modified after being pickled.

To resolve this error, you should try the following:

1. Check that the data being unpickled is not corrupt or incomplete.
2. Make sure that the data was pickled with the same version of Python and pickle settings that you are using to unpickle it.
3. If you suspect that the data has been tampered with or modified, try to obtain a fresh copy of the data.

If none of these solutions work, you may need to consider using a different serialization format or implementing a custom serialization solution.