When working with Python’s multiprocessing
or manager
module, you may encounter the error: TypeError: 'DictProxy' object is not JSON serializable
. This issue arises when attempting to serialise a DictProxy
object (a special object created by multiprocessing.Manager
to allow shared access to data between processes) using the json
module.
In addition to the TypeError
, users may also face an AttributeError
related to pickling functions when using multiprocessing, especially in interactive environments such as Jupyter or IPython notebooks. This post explores why both of these errors occur and how to fix them. We’ll provide an example that reproduces the error and show step-by-step solutions to resolve it.
Why the Errors Occur
AttributeError (Can’t get attribute ‘worker’ on <module>
): This error happens when Python’s multiprocessing
module tries to “pickle” a function (like worker
) for use in a child process. If the function is not defined in the main module (__main__
), the child process cannot find it, leading to this AttributeError
. This is common in environments like Jupyter notebooks or IPython, where the multiprocessing context is not correctly handled.
TypeError (DictProxy
not JSON serializable): Python’s json
module only serializes basic data types like dict
, list
, str
, etc. A DictProxy
object, which enables shared access to data between processes, is not a standard dict
. Thus, attempting to serialize it with json.dumps()
raises the TypeError
.
Example Reproducing Both Errors
Below is an example that reproduces both the TypeError
and the AttributeError
.
import multiprocessing import json def worker(shared_dict): shared_dict['key'] = 'value' if __name__ == '__main__': manager = multiprocessing.Manager() shared_dict = manager.dict() # DictProxy object # Start a new process to modify the shared dict p = multiprocessing.Process(target=worker, args=(shared_dict,)) p.start() p.join() # Trying to serialize the DictProxy object try: json_data = json.dumps(shared_dict) except TypeError as e: print(f"Error: {e}")
Output:
Error: Object of type DictProxy is not JSON serializable
In addition, if you run this in a Jupyter notebook, you may also encounter:
AttributeError: Can't get attribute 'worker' on <module '__main__' (<class '_frozen_importlib.BuiltinImporter'>)>
Solution 1: Convert to Regular dict
To solve the TypeError
, convert the DictProxy
object to a regular dict
before attempting to serialize it. This can be done easily by passing the DictProxy
object into Python’s dict()
constructor.
Here’s how you can fix it:
import multiprocessing import json def worker(shared_dict): shared_dict['key'] = 'value' if __name__ == '__main__': manager = multiprocessing.Manager() shared_dict = manager.dict() # DictProxy object # Start a new process to modify the shared dict p = multiprocessing.Process(target=worker, args=(shared_dict,)) p.start() p.join() # Convert the DictProxy to a regular dict before serializing json_data = json.dumps(dict(shared_dict)) print(f"Serialized data: {json_data}")
Output:
Serialized data: {"key": "value"}
In this updated code, you can successfully serialise the DictProxy
object by converting it into a standard Python dict using dict(shared_dict).
Solution 2: Fixing AttributeError
in Interactive Environments
If you are running the code in an interactive environment like Jupyter or IPython, the multiprocessing module may not behave as expected, causing an AttributeError
. This is because multiprocessing requires functions to be defined in the __main__
scope to properly pickle them for use in subprocesses.
To fix this error, ensure that all multiprocessing-related code is placed inside an if __name__ == '__main__':
block. Here is the corrected code:
import multiprocessing import json def worker(shared_dict): shared_dict['key'] = 'value' if __name__ == '__main__': manager = multiprocessing.Manager() shared_dict = manager.dict() # Start a new process to modify the shared dict p = multiprocessing.Process(target=worker, args=(shared_dict,)) p.start() p.join() # Convert the DictProxy to a regular dict before serializing json_data = json.dumps(dict(shared_dict)) print(f"Serialized data: {json_data}")
By placing the multiprocessing code inside if __name__ == '__main__':
, the script ensures that child processes can correctly locate the worker
function, avoiding the AttributeError
.
Solution 3: Using concurrent.futures.ProcessPoolExecutor
If you prefer to avoid the complexities of multiprocessing in environments like Jupyter notebooks, you can use concurrent.futures.ProcessPoolExecutor
, which handles multiprocessing more gracefully. Here’s how you can refactor the code:
from concurrent.futures import ProcessPoolExecutor import json def worker(shared_dict): shared_dict['key'] = 'value' return shared_dict if __name__ == '__main__': with ProcessPoolExecutor() as executor: shared_dict = {} future = executor.submit(worker, shared_dict) shared_dict = future.result() # Serialize the dictionary json_data = json.dumps(shared_dict) print(f"Serialized data: {json_data}")
Serialized data: {"key": "value"}
ProcessPoolExecutor
simplifies multiprocessing, especially in interactive environments, by internally handling some pickling and process management.
Conclusion
The TypeError: 'DictProxy' object is not JSON serializable
occurs when trying to serialize a DictProxy
object from Python’s multiprocessing
module. The solution is to convert it to a regular dictionary using dict()
before serialization. Additionally, in interactive environments like Jupyter, using multiprocessing may cause an AttributeError
. To fix this, place your multiprocessing code inside an if __name__ == '__main__':
block or use ProcessPoolExecutor
for more robust multiprocessing handling.
By understanding both the TypeError
and AttributeError
, you can efficiently resolve these issues in your multiprocessing code.
Congratulations on reading to the end of this tutorial!
For further reading on related TypeErrors … is not JSON serializable see:
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Suf is a senior advisor in data science with deep expertise in Natural Language Processing, Complex Networks, and Anomaly Detection. Formerly a postdoctoral research fellow, he applied advanced physics techniques to tackle real-world, data-heavy industry challenges. Before that, he was a particle physicist at the ATLAS Experiment of the Large Hadron Collider. Now, he’s focused on bringing more fun and curiosity to the world of science and research online.