How to save pytorch model

Saving and Loading Models¶

This document provides solutions to a variety of use cases regarding the saving and loading of PyTorch models. Feel free to read the whole document, or just skip to the code you need for a desired use case.

When it comes to saving and loading models, there are three core functions to be familiar with:

torch.save: Saves a serialized object to disk. This function uses Python’s pickle utility for serialization. Models, tensors, and dictionaries of all kinds of objects can be saved using this function.

torch.load: Uses pickle’s unpickling facilities to deserialize pickled object files to memory. This function also facilitates the device to load the data into (see Saving & Loading Model Across Devices).

torch.nn.Module.load_state_dict: Loads a model’s parameter dictionary using a deserialized state_dict. For more information on state_dict, see What is a state_dict?.

Contents:

In PyTorch, the learnable parameters (i.e. weights and biases) of an torch.nn.Module model are contained in the model’s parameters (accessed with model.parameters() ). A state_dict is simply a Python dictionary object that maps each layer to its parameter tensor. Note that only layers with learnable parameters (convolutional layers, linear layers, etc.) and registered buffers (batchnorm’s running_mean) have entries in the model’s state_dict. Optimizer objects ( torch.optim ) also have a state_dict, which contains information about the optimizer’s state, as well as the hyperparameters used.

Because state_dict objects are Python dictionaries, they can be easily saved, updated, altered, and restored, adding a great deal of modularity to PyTorch models and optimizers.

Example:¶

Let’s take a look at the state_dict from the simple model used in the Training a classifier tutorial.

Output:

Saving & Loading Model for Inference¶

Save/Load state_dict (Recommended)¶

Save:

Load:

When saving a model for inference, it is only necessary to save the trained model’s learned parameters. Saving the model’s state_dict with the torch.save() function will give you the most flexibility for restoring the model later, which is why it is the recommended method for saving models.

Remember that you must call model.eval() to set dropout and batch normalization layers to evaluation mode before running inference. Failing to do this will yield inconsistent inference results.

If you only plan to keep the best performing model (according to the acquired validation loss), don’t forget that best_model_state = model.state_dict() returns a reference to the state and not its copy! You must serialize best_model_state or use best_model_state = deepcopy(model.state_dict()) otherwise your best best_model_state will keep getting updated by the subsequent training iterations. As a result, the final model state will be the state of the overfitted model.

Save/Load Entire Model¶

Save:

Load:

This save/load process uses the most intuitive syntax and involves the least amount of code. Saving a model in this way will save the entire module using Python’s pickle module. The disadvantage of this approach is that the serialized data is bound to the specific classes and the exact directory structure used when the model is saved. The reason for this is because pickle does not save the model class itself. Rather, it saves a path to the file containing the class, which is used during load time. Because of this, your code can break in various ways when used in other projects or after refactors.

Remember that you must call model.eval() to set dropout and batch normalization layers to evaluation mode before running inference. Failing to do this will yield inconsistent inference results.

Export/Load Model in TorchScript Format¶

One common way to do inference with a trained model is to use TorchScript, an intermediate representation of a PyTorch model that can be run in Python as well as in a high performance environment like C++. TorchScript is actually the recommended model format for scaled inference and deployment.

Using the TorchScript format, you will be able to load the exported model and run inference without defining the model class.

Export:

Load:

Remember that you must call model.eval() to set dropout and batch normalization layers to evaluation mode before running inference. Failing to do this will yield inconsistent inference results.

For more information on TorchScript, feel free to visit the dedicated tutorials. You will get familiar with the tracing conversion and learn how to run a TorchScript module in a C++ environment.

How do I save a trained model in PyTorch?

How do I save a trained model in PyTorch? I have read that:

9 Answers 9

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Found this page on their github repo:

Recommended approach for saving a model

There are two main approaches for serializing and restoring a model.

The first (recommended) saves and loads only the model parameters:

The second saves and loads the entire model:

However in this case, the serialized data is bound to the specific classes and the exact directory structure used, so it can break in various ways when used in other projects, or after some serious refactors.

See also: Save and Load the Model section from the official PyTorch tutorials.

It depends on what you want to do.

Case # 1: Save the model to use it yourself for inference: You save the model, you restore it, and then you change the model to evaluation mode. This is done because you usually have BatchNorm and Dropout layers that by default are in train mode on construction:

Case # 2: Save model to resume training later: If you need to keep training the model that you are about to save, you need to save more than just the model. You also need to save the state of the optimizer, epochs, score, etc. You would do it like this:

Since you are resuming training, DO NOT call model.eval() once you restore the states when loading.

This way is still not bullet proof and since pytorch is still undergoing a lot of changes, I wouldn’t recommend it.

The pickle Python library implements binary protocols for serializing and de-serializing a Python object.

When you import torch (or when you use PyTorch) it will import pickle for you and you don’t need to call pickle.dump() and pickle.load() directly, which are the methods to save and to load the object.

In fact, torch.save() and torch.load() will wrap pickle.dump() and pickle.load() for you.

A state_dict the other answer mentioned deserves just a few more notes.

What state_dict do we have inside PyTorch? There are actually two state_dict s.

The second state_dict is the optimizer state dict. You recall that the optimizer is used to improve our learnable parameters. But the optimizer state_dict is fixed. Nothing to learn there.

Because state_dict objects are Python dictionaries, they can be easily saved, updated, altered, and restored, adding a great deal of modularity to PyTorch models and optimizers.

Let’s create a super simple model to explain this:

This code will output the following:

Note this is a minimal model. You may try to add stack of sequential

PyTorch Save Model – Complete Guide

In this Python tutorial, we will learn about How to save the PyTorch model in Python and we will also cover different examples related to the saving model. Moreover, we will cover these topics.

PyTorch save model

In this section, we will learn about how to save the PyTorch model in Python.

Code:

Before using the Pytorch save the model function, we want to install the torch module by the following command.

After installing the torch module also install the touch vision module with the help of this command.

After installing everything our code of the PyTorch saves model can be run smoothly.

PyTorch save model Example

In this section, we will learn about how to save the PyTorch model explain it with the help of an example in Python.

Code:

In the following code, we will import some torch libraries to train a classifier by making the model and after making save it.

Output:

After running the above code, we get the following output in which we can see that we can train a classifier and after training save the model.

PyTorch save model checkpoint

In this section, we will learn about how to save the PyTorch model checkpoint in Python.

Code:

In the following code, we will import the torch module from which we can save the model checkpoints.

Output:

After running the above code we get the following output in which we can see that the multiple checkpoints are printed on the screen after that the save() function is used to save the checkpoint model.

PyTorch save model architecture

In this section, we will learn about how we can save PyTorch model architecture in python.

Pytorch save model architecture is defined as to design a structure in other we can say that a constructing a building.

Code:

In the following code, we will import some libraries which help to run the code and save the model.

After saving the model we can load the model to check the best fit model.

After loading the model we want to import the data and also create the data loader.

In the below code, we will define the function and create an architecture of the model.

PyTorch save the model for inference

In this section, we will learn about PyTorch save the model for inference in python.

Code:

In the following code, we will import some libraries from which we can save the model inference.

Output:

After running the above code, we get the following output in which we can see that model inference.

PyTorch save the model during training

In this section, we will learn about how we can save the PyTorch model during training in python.

The PyTorch model saves during training with the help of a torch.save() function after saving the function we can load the model and also train the model.

Code:

In the following code, we will import some libraries for training the model during training we can save the model.

Output:

After running the above code, we get the following output in which we can see that training data is downloading on the screen.

PyTorch save the model to onnx

In this section, we will learn about how PyTorch save the model to onnx in Python.

Code:

In the following code, we will import some libraries from which we can save the model to onnx.

So, in this tutorial, we discussed PyTorch Save Model and we have also covered different examples related to its implementation. Here is the list of examples that we have covered.

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How To Save and Load Model In PyTorch With A Complete Example

A practical example of how to save and load a model in PyTorch. We are going to look at how to continue training and load the model for inference

T he goal of this article is to show you how to save a model and load it to continue training after previous epoch and make a prediction. If you are reading this article, I assume you are familiar with the basic of deep learning and PyTorch.

Have you experienced a situation where you spend hours or days training your model and then it stops in the middle? Or you are not satisfied with your model performance and want to train the model again? There are multiple reasons why we might need a flexible way to save and load our model.

Most of the free cloud services such as Kaggle, Google Colab, etc have idle time outs that will disconnect your notebook, plus the notebook will be disconnected or interrupted once it reaches its limit time. Unless you train for a small number of epochs with GPU, the process takes time. Being able to save the model gives you a huge advantage and save the day. To be flexible, I am going to save both the latest checkpoint and the best checkpoint.

Fashion_MNIST_data will be used as our dataset and we’ll write a complete flow from import data to make the prediction. In this exercise, I am going to use a Kaggle notebook.

Step 1: Setting up

Step 2: Importing libraries and creating helper functions

Importing libraries

Saving function

save_ckp is created to save checkpoint, the latest one and the best one. This creates flexibility: either you are interested in the state of the latest checkpoint or the best checkpoint.

In our case, we want to save a checkpoint that allows us to use this information to continue our model training. Here is the information needed:

Loading function

load_chkp is created for loading model. It takes:

Step 3: Importing dataset Fashion_MNIST_data and creating data loader

PyTorch For Deep Learning — Saving and Loading models

Despite the framework that is being used, saving the model is a very important thing so as to use it again readily instead of training the neural network again from the start.

Sometimes, training might even take weeks to be completed. So, each and everytime we need to use our network, we can’t afford to train them right from the beginning.

another advantage of saving the models then and there is that we can compare 2 different models to choose which in is much more effective and efficient for the task given.

having said that, let’s jump right into the code

2. creating a dummy linear network

3. Saving the model

4. Load the model

torch.load is a function that can be used to load the model back into a variable.
the parameter it takes is the path of the file in which the original model is saved and returns the model that could be stored in a python variable

5. Comparing the weights of the orignal model with the saved model

6. Making Predictions with the loaded model

Conclusion

As we already saw, saving the model is very important especially when deep neural network is involved. so, don’t forget to save the model while working in large projects.

The above code is the easy method for saving the model in pytorch. There are still other ways to do that. maybe, those will be covered in other blog posts.

The code files are available at my github repository