I want to serialize a trained scikit pipeline object to reload it for predictions. What I saw pickle and joblib dump are two common methods for that, whereas joblib is the preferable approach.
In my case I want to store the serialized python object in the database and load it from there, deserialize it and use it for predictions. Is it possible to serialize the object without any file system access?
Yes, for the pickle library you can get the serialized version of an object by using pickle.dumps instead of pickle.dump.
serialized_object = pickle.dumps(object) This returns a bytes object, which you should then be able to store in your database, potentially converting it to base64 before doing so, or maybe directly.
You can do this:
import joblib from io import BytesIO import base64 with BytesIO() as tmp_bytes: joblib.dump({"test": "test"}, tmp_bytes) bytes_obj = tmp_bytes.getvalue() base64_obj = base64.b64encode(bytes_obj) Then, bytes_obj is a bytes object. And base64_obj is the base64 version. Select what you like.
