Delve into PyTorch basics: autograd, model classes, datasets, and data loaders. Gain insights into model development while avoiding common pitfalls. Start creating and training your own PyTorch models.

Deep learning with Pytorch, A beginner's Guide By Daniel Godoy.png

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This course is designed to provide you with an easy-to-follow, structured, incremental, and from-first-principles approach to learning PyTorch. In this course, you’ll be introduced to the fundamentals of PyTorch: autograd, model classes, datasets, data loaders, and more. You will develop, step-by-step, not only the models themselves but also your understanding of them. You'll be shown both the reasoning behind the code and how to avoid some common pitfalls and errors along the way. By the time you finish this course, you’ll have a thorough understanding of the concepts and tools necessary to start developing and training your own models using PyTorch.

Deep Learning with PyTorch Step-by-Step: Part I - Fundamentals

# Saving checkpoints

To **checkpoint** the model to resume training later, we can use the `save_checkpoint` method, which handles the state dictionaries for us and saves them to a file:

# Saving checkpoint of model
sbs.save_checkpoint('model_checkpoint.pth')

# Resuming training

Remember, when we did this in the chapter, **Rethinking the Training Loop**, we had to set up the stage before actually loading the model, loading the data, and configuring the model. We still need to do this, but we are now using the latest version of the model configuration section:

Learn about running a classy pipeline for checkpointing and resuming training steps.


Checkpointing

Learn about running a classy pipeline for checkpointing and resuming training steps.

Introduction

Visualizing Gradient Descent

A Simple Regression Problem

Rethinking the Training Loop

Going Classy

A Simple Classification Problem

Conclusion

Appendix

Checkpointing

Saving checkpoints

Resuming training