9. Model persistence

After training a scikit-learn model, it is desirable to have a way to persist the model for future use without having to retrain. This can be accomplished using pickle, joblib, skops, ONNX, or PMML. In most cases pickle can be used to persist a trained scikit-learn model. Once all transitive scikit-learn dependencies have been pinned, the trained model can then be loaded and executed under conditions similar to those in which it was originally pinned. The following sections will give you some hints on how to persist a scikit-learn model and will provide details on what each alternative can offer.

9.1. Workflow Overview

In this section we present a general workflow on how to persist a scikit-learn model. We will demonstrate this with a simple example using Python’s built-in persistence module, namely pickle.

9.1.1. Storing the model in an artifact

Once the model training process in completed, the trained model can be stored as an artifact with the help of pickle. The model can be saved using the process of serialization, where the Python object hierarchy is converted into a byte stream. We can persist a trained model in the following manner:

>>> from sklearn import svm
>>> from sklearn import datasets
>>> import pickle
>>> clf = svm.SVC()
>>> X, y = datasets.load_iris(return_X_y=True)
>>> clf.fit(X, y)
SVC()
>>> s = pickle.dumps(clf)

9.1.2. Replicating the training environment in production

The versions of the dependencies used may differ from training to production. This may result in unexpected behaviour and errors while using the trained model. To prevent such situations it is recommended to use the same dependencies and versions in both the training and production environment. These transitive dependencies can be pinned with the help of pip, conda, poetry, conda-lock, pixi, etc.

Note

To execute a pickled scikit-learn model in a reproducible environment it is advisable to pin all transitive scikit-learn dependencies. This prevents any incompatibility issues that may arise while trying to load the pickled model. You can read more about persisting models with pickle over here.

9.1.3. Loading the model artifact

The saved scikit-learn model can be loaded using pickle for future use without having to re-train the entire model from scratch. The saved model artifact can be unpickled by converting the byte stream into an object hierarchy. This can be done with the help of pickle as follows:

>>> clf2 = pickle.loads(s) 
>>> clf2.predict(X[0:1]) 
array([0])
>>> y[0] 
0

9.1.4. Serving the model artifact

The last step after training a scikit-learn model is serving the model. Once the trained model is successfully loaded it can be served to manage different prediction requests. This can involve deploying the model as a web service using containerization, or other model deployment strategies, according to the specifications. In the next sections, we will explore different approaches to persist a trained scikit-learn model.

9.2. Persisting models with pickle

As demonstrated in the previous section, pickle uses serialization and deserialization to persist scikit-learn models. Instead of using dumps and loads, dump and load can also be used in the following way:

>>> from sklearn.tree import DecisionTreeClassifier
>>> from sklearn import datasets
>>> clf = DecisionTreeClassifier()
>>> X, y = datasets.load_iris(return_X_y=True)
>>> clf.fit(X, y)
DecisionTreeClassifier()
>>> from pickle import dump, load
>>> with open('filename.pkl', 'wb') as f: dump(clf, f) 
>>> with open('filename.pkl', 'rb') as f: clf2 = load(f) 
>>> clf2.predict(X[0:1]) 
array([0])
>>> y[0]
0

For applications that involve writing and loading the serialized object to or from a file, dump and load can be used instead of dumps and loads. When file operations are not required the pickled representation of the object can be returned as a bytes object with the help of the dumps function. The reconstituted object hierarchy of the pickled data can then be returned using the loads function.

9.3. Persisting models with joblib

In the specific case of scikit-learn, it may be better to use joblib’s replacement of pickle (dump & load), which is more efficient on objects that carry large numpy arrays internally as is often the case for fitted scikit-learn estimators, but can only pickle to the disk and not to a string:

>>> from joblib import dump, load
>>> dump(clf, 'filename.joblib')

Later you can load back the pickled model (possibly in another Python process) with:

>>> clf = load('filename.joblib')

Note

dump and load functions also accept file-like object instead of filenames. More information on data persistence with Joblib is available here.

InconsistentVersionWarning Click for more details

When an estimator is unpickled with a scikit-learn version that is inconsistent with the version the estimator was pickled with, a InconsistentVersionWarning is raised. This warning can be caught to obtain the original version the estimator was pickled with:

from sklearn.exceptions import InconsistentVersionWarning
warnings.simplefilter("error", InconsistentVersionWarning)

try:
    est = pickle.loads("model_from_prevision_version.pickle")
except InconsistentVersionWarning as w:
    print(w.original_sklearn_version)

9.4. Security & maintainability limitations for pickle and joblib

pickle (and joblib by extension), has some issues regarding maintainability and security. Because of this,

  • Never unpickle untrusted data as it could lead to malicious code being executed upon loading.

  • While models saved using one version of scikit-learn might load in other versions, this is entirely unsupported and inadvisable. It should also be kept in mind that operations performed on such data could give different and unexpected results.

In order to rebuild a similar model with future versions of scikit-learn, additional metadata should be saved along the pickled model:

  • The training data, e.g. a reference to an immutable snapshot

  • The python source code used to generate the model

  • The versions of scikit-learn and its dependencies

  • The cross validation score obtained on the training data

This should make it possible to check that the cross-validation score is in the same range as before.

Aside for a few exceptions, pickled models should be portable across architectures assuming the same versions of dependencies and Python are used. If you encounter an estimator that is not portable please open an issue on GitHub. Pickled models are often deployed in production using containers, like Docker, in order to freeze the environment and dependencies.

If you want to know more about these issues and explore other possible serialization methods, please refer to this talk by Alex Gaynor.

9.5. Persisting models with a more secure format using skops

skops provides a more secure format via the skops.io module. It avoids using pickle and only loads files which have types and references to functions which are trusted either by default or by the user.

Using skops Click for more details

The API is very similar to pickle, and you can persist your models as explain in the docs using skops.io.dump and skops.io.dumps:

import skops.io as sio
obj = sio.dumps(clf)

And you can load them back using skops.io.load and skops.io.loads. However, you need to specify the types which are trusted by you. You can get existing unknown types in a dumped object / file using skops.io.get_untrusted_types, and after checking its contents, pass it to the load function:

unknown_types = sio.get_untrusted_types(data=obj)
clf = sio.loads(obj, trusted=unknown_types)

If you trust the source of the file / object, you can pass trusted=True:

clf = sio.loads(obj, trusted=True)

Please report issues and feature requests related to this format on the skops issue tracker.

9.6. Persisting models with interoperable formats

For reproducibility and quality control needs, when different architectures and environments should be taken into account, exporting the model in Open Neural Network Exchange format or Predictive Model Markup Language (PMML) format might be a better approach than using pickle alone. These are helpful where you may want to use your model for prediction in a different environment from where the model was trained.

ONNX is a binary serialization of the model. It has been developed to improve the usability of the interoperable representation of data models. It aims to facilitate the conversion of the data models between different machine learning frameworks, and to improve their portability on different computing architectures. More details are available from the ONNX tutorial. To convert scikit-learn model to ONNX a specific tool sklearn-onnx has been developed.

PMML is an implementation of the XML document standard defined to represent data models together with the data used to generate them. Being human and machine readable, PMML is a good option for model validation on different platforms and long term archiving. On the other hand, as XML in general, its verbosity does not help in production when performance is critical. To convert scikit-learn model to PMML you can use for example sklearn2pmml distributed under the Affero GPLv3 license.

9.7. Summarizing the keypoints

Based on the different approaches for model persistence, the keypoints for each approach can be summarized as follows:

  • pickle: It is native to Python and any Python object can be serialized and deserialized using pickle, including custom Python classes and objects. While pickle can be used to easily save and load scikit-learn models, unpickling of untrusted data might lead to security issues.

  • joblib: Efficient storage and memory mapping techniques make it faster when working with large machine learning models or large numpy arrays. However, it may trigger the execution of malicious code while loading untrusted data.

  • skops: Trained scikit-learn models can be easily shared and put into production using skops. It is more secure compared to alternate approaches as it allows users to load data from trusted sources. It however, does not allow for persistence of arbitrary Python code.

  • ONNX: It provides a uniform format for persisting any machine learning or deep learning model (other than scikit-learn) and is useful for model inference. It can however, result in compatibility issues with different frameworks.

  • PMML: Platform independent format that can be used to persist models and reduce the risk of vendor lock-ins. The complexity and verbosity of this format might make it harder to use for larger models.