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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

PyTorch on Mobile Devices

In the previous chapter, we learned extensively about operationalizing PyTorch models as services in production systems. While productionizing machine learning (ML) models as services in the cloud remains the most popular form of ML deployment, several use cases require models to be deployed on mobile devices, such as:

  • User data protection – mobile models do not require third-party data transfer, as the processing is done where the data is first acquired
  • Reduced latency – mobile models save us the cloud network I/O time
  • Better user experience – mobile models can provide real-time user interaction with lower latency compared to running models remotely from the cloud
  • Leveraging dedicated mobile hardware and software for ML (eg., coreML) that mobile phone makers are increasingly adding to their products

In this chapter, we will learn how to deploy PyTorch models on mobile devices using PyTorch Mobile...

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