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Mastering PyTorch

You're reading from   Mastering PyTorch Create and deploy deep learning models from CNNs to multimodal models, LLMs, and beyond

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Product type Paperback
Published in May 2024
Publisher Packt
ISBN-13 9781801074308
Length 558 pages
Edition 2nd Edition
Tools
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Author (1):
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Ashish Ranjan Jha Ashish Ranjan Jha
Author Profile Icon Ashish Ranjan Jha
Ashish Ranjan Jha
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Table of Contents (21) Chapters Close

Preface 1. Overview of Deep Learning Using PyTorch 2. Deep CNN Architectures FREE CHAPTER 3. Combining CNNs and LSTMs 4. Deep Recurrent Model Architectures 5. Advanced Hybrid Models 6. Graph Neural Networks 7. Music and Text Generation with PyTorch 8. Neural Style Transfer 9. Deep Convolutional GANs 10. Image Generation Using Diffusion 11. Deep Reinforcement Learning 12. Model Training Optimizations 13. Operationalizing PyTorch Models into Production 14. PyTorch on Mobile Devices 15. Rapid Prototyping with PyTorch 16. PyTorch and AutoML 17. PyTorch and Explainable AI 18. Recommendation Systems with PyTorch 19. PyTorch and Hugging Face 20. Index

Summary

In this chapter, we explored the world of deploying trained PyTorch deep learning models in production systems.

In the next chapter, we will learn how to deploy trained PyTorch models on different mobile operating systems – Android and iOS.

Reference list

  1. Full code for Creating a PyTorch model inference pipeline exercise: https://github.com/PacktPublishing/Mastering-PyTorch/blob/master/Chapter10/mnist_pytorch.ipynb
  2. Notebook for Creating a PyTorch model inference pipeline exercise: https://github.com/PacktPublishing/Mastering-PyTorch/blob/master/Chapter10/run_inference.ipynb
  3. Flask library: https://flask.palletsprojects.com/en/1.1.x/
  4. Full code for the Using Flask to build our model server exercise: https://github.com/PacktPublishing/Mastering-PyTorch/blob/master/Chapter10/server.py
  5. Flask server code: https://github.com/PacktPublishing/Mastering-PyTorch/blob/master/Chapter10/make_request.py
  6. What are Microservices: https...
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