Serialization and Deserialization Techniques in Python

Using pickle, joblib, Yaml, JSON, dill …

This post is focused on different ways to interact with JSON with Python, pickl, joblib and yaml.

Machine learning models Serialization and deserialization using JSON, pickle, joblib, yamel and manual methods.
Here, will see the different ways to interact with JSON with Python, pickl, dill, joblib and yaml.

Serialization is the process of translating data structures or object state into a format that can be stored (for example, in a file or memory buffer) or transmitted and reconstructed later.

In serialization, an object is transformed into a format that can be stored, so as to be able to deserialize it later and recreate the original object from the serialized format in the context of data storage.

There are a few ways to put trained machine learning (ML) models into production. The most common method is to serialize the model using some particular format after training, and deserialize that model in the production environment.

The common methods for serializing and deserializing methods are JSON, Pickle, Joblib, onnx or pmml, keras supports hdf5 and alternative serialization packages dill which generalizes pickle at the cost of performance.

Lets see few practical examples of JSON, Pickle, joblib, Yaml

Encoding of Python Objects in JSON

Pickling is the process whereby a Python object hierarchy is converted into a byte stream (usually not human readable) to be written to a file, this is also known as Serialization. Unpickling is the reverse operation, whereby a byte stream is converted back into a working Python object hierarchy.

Pickle is operationally simplest way to store the object. The Python Pickle module is an object-oriented way to store objects directly in a special storage format.

Python pickle module is used for serializing and de-serializing a Python object structure. Any object in Python can be pickled so that it can be saved on disk. What pickle does is that it “serializes” the object first before writing it to file. 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.

The pickle module is used for implementing binary protocols for serializing and de-serializing a Python object structure.

Methods provided by the pickle module :

The pickle interface provides four different methods.

dump() − The dump() method serializes to an open file (file-like object).

dumps() − Serializes to a string

load() − Deserializes from an open-like object.

loads() − Deserializes from a string.

Advantages & Disadvantages of pickle

we can pickle objects with the following data types like Booleans, Integers, Floats, Complex numbers, Strings, Tuples, Lists, Sets, Dictionaries and classes and functions.

see the major differences between JSON and Pickle

JSON is a lightweight format for data-interchange, that is easily readable by humans. Although it was derived from JavaScript, JSON is standardized and language-independent. This is a serious advantage over pickle. It’s also more secure and much faster than pickle.

However, if you only need to use Python, then the pickle module is still a good choice for its ease of use and ability to reconstruct complete Python objects.

For Machine learning model deployment usually pickl is a good choice than JSON especially while working with complex models.

The second tool we describe is pickle the standard Python tool for object (de)serialization. Afterwards, we look at the joblib library which offers easy (de)serialization of objects containing large data arrays, and finally we present a manual approach for saving and restoring objects to/from JSON (JavaScript Object Notation). None of these approaches represents an optimal solution, but the right fit should be chosen according to the needs of our project.

The joblib package provides dump and load functions for serializing Python objects, with particular optimizations for large numpy arrays. It is intended to be a drop-in replacement for pickle and can be effective for sklearn models which store lots of data internally, such as random forest or cluster-based classifiers.

Pickle requires a file object to be passed as an argument, Joblib works with both file objects and string filenames. In case your model contains large arrays of data, each array will be stored in a separate file, but the save and restore procedure will remain the same. Joblib also allows different compression methods, such as ‘zlib’, ‘gzip’, ‘bz2’, and different levels of compression.

biggest drawback of the Pickle and Joblib tools is its compatibility over different models and Python versions.

YAML is my favorite format. It is a human-friendly data serialization format. Unlike Pickle and JSON, it is not part of the Python standard library, so you need to install it

Anyway, whenever you want to have full control over the save and restore process, the best way is to build your own functions manually.

The dill package extends the functionality of pickle by enabling the serialization of a much larger set of Python objects

Example of manually saving and restoring objects using JSON. This approach allows us to select the data which needs to be saved, such as the model parameters, coefficients, training data, and anything else we need.

Keras is a simple and powerful Python library for deep learning.
Lets see how to save and load them from disk using .h5 file
In this post, you will discover how you can save your Keras models to file and load them up again to make predictions using pimas indian diabets dataset.

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