Master the Art of Saving and Loading Fastai Models

Table of Contents:

1. Introduction
2. Saving and Loading Trained Fastai Models 2.1 The Export Function 2.2 The Load Learner Function
3. Saving Trained Models with Tabular Datasets
4. Loading Saved Models with Tabular Datasets
5. Setting the Path for Model Saving
6. Saving and Loading Models in Colab
7. Saving and Loading Models in Gradient
8. One-Off Exercise with the Loaded Model
9. Web Deployment of the Model
10. Conclusion

Saving and Loading Trained Fastai Models

In the world of deep learning, it is crucial to know how to save and load trained models for future use. This article will walk you through the process of saving and loading trained Fastai models, providing you with the necessary knowledge to deploy your models effectively.

The Export Function

To save a trained Fastai deep learning model, you need to use the export function. This function allows you to save the model to the file system, making it easily accessible for future use. By specifying the desired file name for the saved model, you can ensure that it is conveniently stored and organized.

The Load Learner Function

Once you have saved your trained model, it's time to load it for further utilization. The load_learner function comes into play here. By specifying the name of the pickle file that contains the saved model as an argument, you can load the model effortlessly. This function allows you to quickly validate the model or deploy it for specific use cases.

Saving Trained Models with Tabular Datasets

If you have trained a Fastai model using tabular datasets, you might wonder how to save and load these models specifically. Well, Fastai provides notebooks dedicated to saving and loading models trained with tabular datasets. These notebooks, namely "Saving Models Trained with Tabular Datasets" and "Loading Saved Models Trained with Tabular Datasets," can be found in the ch3 directory of the repository.

Loading Saved Models with Tabular Datasets

To load a saved model trained with tabular datasets, you follow the same process as loading any other model. The load_learner function is used, and you specify the name of the pickle file where the model was saved. By doing this, you can quickly access and utilize your trained tabular models for further analysis or deployment.

Setting the Path for Model Saving

Before saving or loading a model, it is essential to set the path for saving the model to a writable directory. This step ensures that the model is stored in the correct location and is easily retrievable when needed. The path can be explicitly set for different environments such as Colab or Gradient, ensuring compatibility and flexibility.

Saving and Loading Models in Colab

When saving and loading models in Google Colab, the fully qualified path needs to be explicitly set to correspond to the same directory and drive as the notebook. This step ensures that the model is saved and loaded correctly within the Colab environment, allowing for seamless access and utilization.

Saving and Loading Models in Gradient

In Gradient, the process of saving and loading models is more straightforward. The path for the model can be set directly to the current directory, eliminating the need for explicit path handling. This convenience enables efficient model management and deployment within the Gradient environment.

One-Off Exercise with the Loaded Model

To validate that the loaded model works as expected, you can perform a one-off exercise using a single data point. By setting the path for the model as done previously, you can load the model using the load_learner function. Then, you can test the loaded model's performance on a test data point, ensuring that it produces the desired results.

Web Deployment of the Model

Apart from one-off exercises and validation, you can also deploy the loaded model for web-based applications. By copying the pickle file for the saved model into the same directory as the Flask server module, you can load the model using the load_learner function. Once the model is loaded, it is ready for deployment, powering your web application's predictive capabilities.

Conclusion

Saving and loading trained models is a crucial aspect of deep learning model management. Fastai provides seamless functionality through the export and load_learner functions for easy model storage and retrieval. Whether you are performing one-off exercises or deploying models for web applications, understanding the process is essential for successful model utilization.

💡 Highlights:

• Fastai makes it easy to save and load trained deep learning models.
• The export function is used to save models to the file system.
• The load_learner function allows for the loading of saved models.
• Fastai provides dedicated notebooks for saving and loading models with tabular datasets.
• Setting the path for model saving is crucial for proper organization and access.
• Models can be saved and loaded efficiently in both Colab and Gradient environments.
• Validating loaded models and web deployment are essential use cases for model utilization.

FAQ:

Q: Can I save Fastai models trained with tabular datasets? A: Yes, Fastai provides dedicated notebooks for saving and loading models trained with tabular datasets.

Q: How do I set the path for saving and loading models? A: The path can be explicitly set for compatibility with different environments such as Colab or Gradient.

Q: Can I deploy the loaded model for web applications? A: Yes, by copying the pickle file for the saved model into the Flask server module directory, you can deploy it for web-based applications. Resources:

• Fastai GitHub Repository