PyTorch Cookbook: 100+ Solutions across RNNs, CNNs, python tools, distributed training and graph networks
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About this ebook
Starting a PyTorch Developer and Deep Learning Engineer career? Check out this 'PyTorch Cookbook,' a comprehensive guide with essential recipes and solutions for PyTorch and the ecosystem. The book covers PyTorch deep learning development from beginner to expert in well-written chapters.
The book s
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PyTorch Cookbook - Matthew Rosch
Prologue
The introduction to PyTorch Cookbook
prepares readers for an exciting journey into the fascinating realms of deep learning and neural networks, with PyTorch as a foundation. Presenting itself as more than just a book, it leads its readers by the hand and shows them the ropes as they navigate the challenging terrain of machine learning and AI.
The current era is defined by the exponential development of both information and technology. The ever-increasing capabilities of deep learning and artificial intelligence are changing entire markets and expanding the boundaries of possibility. PyTorch is the shining star at the epicentre of this technological renaissance, luring professionals and amateurs alike. However, the abundance of information can be overwhelming, and the road to mastery may seem paved with obstacles, just as it would be in any dynamic field. The goal of the PyTorch Cookbook
is to make the art and science of deep learning accessible by explaining its complexities and making them easier to understand.
This book is more than just a collection of algorithms and codes; it is also a thoughtful assemblage of useful advice and examples. Its goal is to give readers a firm grasp of the fundamentals of PyTorch and the confidence to apply what they've learned in practical situations. Beginning with tensors and computational graphs, this book introduces the fundamental building blocks of PyTorch and sets the stage for the challenges that lie ahead. From simple feedforward networks to more advanced Convolutional Neural Networks, Recurrent Neural Networks, and Graph Neural Networks, this book covers them all. The inclusion of relevant real-world examples and applications alongside theoretical concepts improves the learning experience and makes the material more memorable.
However, the PyTorch Cookbook
is not just for one person to study from. It takes into account the rising demand for remote education and scalability, getting readers ready for group efforts and widespread rollouts. To foretell the future of AI in mobile devices, it explores cutting-edge fields like mobile and embedded development. The chapters are structured around troubleshooting and error handling to better prepare readers for potential project roadblocks.
The wider PyTorch ecosystem is one of the book's distinguishing features. Readers will learn about and see examples of using ONNX Runtime, PySyft, Pyro, Deep Graph Library (DGL), Fastai, and Ignite, among other tools and libraries. The book emphasises the need to keep up with technological developments, and these chapters do just that by showcasing the boundless opportunities that lie ahead.
PyTorch Cookbook
100+ Solutions put into practice across RNNs, CNNs, PyTorch tools, distributed training and graph networks
Matthew Rosch
Copyright © 2023 by GitforGits
All rights reserved. This book is protected under copyright laws and no part of it may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or by any information storage and retrieval system, without the prior written permission of the publisher. Any unauthorized reproduction, distribution, or transmission of this work may result in civil and criminal penalties and will be dealt with in the respective jurisdiction at anywhere in India, in accordance with the applicable copyright laws.
Published by: GitforGits
www.gitforgits.com
support@gitforgits.com
Printed in India
First Printing: October 2023
Cover Design by: Kitten Publishing
For permission to use material from this book, please contact GitforGits at support@gitforgits.com.
Content
Prologue
Preface
Chapter 1: Introduction to PyTorch 2.0
Getting Started
PyTorch 2.0 Features and Capabilities
Introduction to PyTorch 2.0
Features of PyTorch 2.0
Why PyTorch 2.0?
Installing PyTorch 2.0 on Linux
System Requirements
Installing Dependencies
Creating Virtual Environment (Optional)
Installing PyTorch via Pip
Verifying Installation
Troubleshooting Common Issues
Additional Tools and Libraries
Create and Verify Tensors
Understanding Tensors
Creating Tensors
Tensor Properties
Verifying Tensor Creation
Manipulating Tensors
Moving Tensors between CPU and GPU
Interoperability with NumPy
Tensor Operations
Understanding Tensor-Matrix Multiplication
Performing Matrix-Matrix Multiplication
Performing Matrix-Vector Multiplication
Performing Batch Matrix-Matrix Multiplication
In-Place Multiplication
Multiplication with Broadcasting
Leveraging GPU for Multiplication
Special Matrix Multiplication Functions
Installing CUDA
System Requirements
Checking for Existing GPU
Removing Previous NVIDIA Driver Versions
Installing NVIDIA Driver
Installing CUDA Toolkit
Configuring Environment Variables
Verifying Installation
Installing PyTorch with CUDA Support
Testing PyTorch with CUDA
Writing First Neural Network
Importing Libraries and Preparing Data
Defining Neural Network Architecture
Creating Neural Network Instance
Choosing Loss Function and Optimizer
Preprocessing Data
Training Neural Network
Evaluating Model
Testing Neural Networks
Splitting Data into Training and Testing Sets
Training Model with Training Data
Validating Model on Testing Set
Calculating Accuracy and Other Metrics
Analyzing Results and Making Adjustments
Getting Started with TorchScript
Understanding TorchScript
Tracing the Model
Scripting Specific Methods
Serializing and Loading Model
Integrating TorchScript with LibTorch (C++ API)
Summary
Chapter 2: Deep Learning Building Blocks
Introduction to Deep Learning
Introduction to Linear Layers
Understanding Linear Layers
Applying Linear Layers
Training Extended Model
Implementing Activation Function
Understanding Activation Functions
Implementing Activation Functions in PyTorch
Minimizing Loss Function
Understanding Loss Function
Implementing Loss Function in PyTorch
Calculating Loss During Training
Optimizing Loss Function
Training Loop with Optimization
Optimization Techniques
Overview of Optimization Techniques
Applying Adam Optimizer to Model
Regularization Techniques
Overview of Regularization Techniques
Applying Dropout to Model
Understanding Impact of Dropout
Creating Custom Layers
Subclassing nn.Module
Defining Custom Layer's Operations
Integrating Custom Layer into Previous Model
Training and Utilizing Model with Custom Layer
Common Challenges & Solutions
Data Preprocessing Errors
Model Architecture Errors
Training Errors
Optimization Errors
CUDA and GPU Errors
Custom Layer Errors
Summary
Chapter 3: Convolutional Neural Networks
Convolutional Neural Networks Overview
Introduction
Structure and Functionality
Usage and Applications
My First CNN
Importing Necessary Libraries
Loading Data
Defining CNN Architecture
Instantiating Model, Loss Function, and Optimizer
Training Model
Explore GoogLeNet
Understanding Inception Module
Implementing GoogLeNet with PyTorch
Import Libraries
Define Inception Module
Integrating Inception Modules into GoogLeNet
Training and Evaluation
Applying Image Augmentation on CIFAR-10
Dataset Information
Import Libraries
Define Transformations
Load CIFAR-10 Dataset with Augmentations
Visualize Augmented Images
Integrate with Model Training
Advantages and Considerations
Performing Object Detection on COCO
Dataset Information
Import Libraries
Define Transformations
Load COCO Dataset
Load Pre-Trained Model
Perform Object Detection
Explanation of Faster R-CNN
Perform Semantic Segmentation
Import Libraries and Load Dataset
Define Transformations
Load Pre-Trained Model
Process an Image
Perform Segmentation
Interpret the Output
Visualization
Understanding DeepLabV3
Exploring Filters and Feature Maps
Filters
Feature Maps
Sample Program: Using Filters and Feature Maps
Import Libraries and Load an Image
Define Convolutional Layer
Apply Convolutional Layer
Analyze the Filter
Multiple Filters
Building Time Series Model
Selecting Dataset
Importing Libraries and Loading the Dataset
Preprocessing Data
Defining Model
Training Model
Making Predictions
Visualization
Common Challenges & Solutions
Mismatched Input and Filter Dimensions
GPU Memory Error
Overfitting
Difficulty in Convergence (Optimization)
Incompatible Data Types
Errors with Time Series Data
Image Augmentation Issues
Object Detection and Semantic Segmentation Errors
Custom Layers Implementation Mistakes
Dataset URL Errors
Model Serialization with TorchScript
CUDA Compatibility Issues
Summary
Chapter 4: Recurrent Neural Networks
Recurrent Neural Networks Overview
Data Preparation for LSTM
Choosing the Dataset and Objective
Loading and Inspecting Data
Data Preprocessing
Transforming Data into Sequences
Splitting Data into Training and Test Sets
Reshaping Data for LSTM Input
Building LSTM Model
Importing Required Libraries
Defining LSTM Model Class
Instantiating Model
Defining Loss Function and Optimizer
Training LSTM Model
Exploring Gated Recurrent Units (GRU)
Introduction to GRU and LSTM
Architecture Differences: LSTM vs. GRU
Computational Complexity
Learning Capabilities
Empirical Performance
Building GRU Model
Importing Libraries and Loading Data
Defining GRU Model
Setting Hyperparameters
Creating Model
Training Model
Evaluation
Understanding Sequential Modeling
What is Sequential Modeling?
Applications of Sequential Modeling
Sample Program: Building Sequence Model
Choosing Appropriate Model
Structuring Model Architecture
Sequence-Specific Preprocessing
Advanced Training Techniques
Interpretation and Evaluation
Sample Program: Time Series Analysis using LSTM
Preprocessing and Structuring Data
Building LSTM Model
Training Model
Making Predictions and Evaluation
Creating Multi-layer RNN
Designing Model Architecture
Preparing Data
Training Multi-layer RNN
Evaluation and Predictions
Common Challenges & Solutions
Vanishing and Exploding Gradients Problem
Overfitting Problem
Difficulty in Learning Long-term Dependencies
Computational Cost and Training Time
Sequence Length Variability
Model Complexity and Selection
Data Preprocessing Issues
Summary
Chapter 5: Natural Language Processing
Role of PyTorch in Natural Language Processing
Preprocessing Textual Data
Importing Libraries
Defining Fields
Loading Dataset
Building Vocabulary
Creating Iterators
Accessing Batch
Additional Preprocessing
Building Text Classification Model
Define Model Architecture
Initialize Model
Loss and Optimizer
Training Model
Evaluating Model
Building Seq2Seq Model
Loading the Dataset
Defining Encoder
Defining Decoder
Combining Encoder and Decoder
Training the Model
Inference
Building Transformers
Introduction to Transformers
Building Transformer Model
Enhancing NER Model
Enhancing Word Representations
Adding More Layers
Model Training Enhancements
Putting It All Together
NLP Pipeline Optimization
Data Preprocessing Optimization
Model Architecture Optimization
Training Optimization
Inference Optimization
Optimizing Custom Layers
Utilizing Distributed Training
Common Challenges & Solutions
Data Preprocessing Errors
Model Architecture Errors
Training Errors
Inference Errors
Optimization Errors
Deployment Errors
Summary
Chapter 6: Graph Neural Networks (GNNs)
Introduction to Graph Neural Networks
Comparison with Recurrent Neural Networks
Comparison with Convolutional Neural Networks
Unique Features of GNNs
My First GNN Model
Installing PyTorch Geometric
Importing Necessary Libraries
Creating Graph
Defining GNN Model
Training Model
Evaluating Model
Adding Convolution Layers to GNN
Importing Necessary Libraries
Extending GNN Model
Training Extended Model
Evaluating Extended Model
Adding Attentional Layers
Graph Attention Network (GAT) Layer
Modifying Existing Model
Training and Evaluating Model with Attention
Applying Node Regression
Modifying Model for Regression
Loss Function and Metrics for Regression
Training Loop for Node Regression
Evaluation
Handling Node Classification
Modifying Model for Classification
Loss Function and Metrics for Classification
Training Loop for Node Classification
Evaluation
Applying Graph Embedding
Choose Graph Embedding Technique
Install Required Libraries
Prepare Graph Data
Create Node2Vec Model
Train Embedding Model
Get Node Embeddings
Utilize Embeddings in Graph Neural Network
Train GNN with New Features
Common Challenges & Solutions
Graph Data Preparation
Graph Embedding Challenges
Model Architecture Challenges
Training Challenges
Scalability Challenges
Interpretability Challenges
Node Regression and Classification Challenges
Library Installation Challenges
Attentional Layers Challenges
Real-world Deployment Challenges
Summary
Chapter 7: Working with Popular PyTorch Tools
PyTorch Ecosystem: Tools & Libraries
ONNX Runtime for PyTorch
Sample Program: Using ONNX Runtime
Import Libraries
Define PyTorch Model
Export to ONNX Format
Load and Run with ONNX Runtime
PySyft for PyTorch
Sample Program: Using PySyft
Import Libraries
Hook PyTorch to PySyft
Create Virtual Workers
Distribute Data to Workers
Define and Train Model Federatedly
Pyro for PyTorch
Sample Program: Using Pyro
Import Libraries
Define Bayesian Neural Network
Define Model
Training with SVI
Making Predictive Inference
Deep Graph Library (DGL)
Sample Program: Using DGL and PyTorch
Define Graph and Features
Create GCN Layer
Instantiate Model and Set Hyperparameters
Training Loop
Utility of FastAI
Sample Program: fastai for Text Classification
Prepare Data
Create Language Model
Finding Optimal Learning Rate
Training Language Model
Create and Train Text Classifier
Predict and Evaluate
Exploring Ignite
Sample Program: Ignite for Text Classification
Import Libraries
Prepare Data
Define Model, Loss, and Optimizer
Create Trainer and Evaluator
Attach Handlers
Run Training
Evaluate and Predict
Common Challenges & Solutions
ONNX Runtime
PySyft
Pyro
Deep Graph Library (DGL)
Fastai
Ignite
Summary
Chapter 8: Distributed Training and Scalability
Overview of Distributed Training
Working with Data Parallelism
Brief Overview
Data Parallelism in PyTorch
Explore Multi-GPU Training
Multi-GPU Training using Data Parallelism
Multi-GPU Training using Model Parallelism
Key Considerations
Cluster Training Concepts
Distributed Training Architecture
Distributed Data Parallelism
Synchronization Methods
Communication Strategies
Fault Tolerance
Performing Cluster Training
Preparing Code
Launching Training
Monitoring and Debugging
Performance Optimization Techniques
Efficient Data Loading
Model Parallelism
Mixed Precision Training
Utilize Efficient Convolutional Algorithms
Gradient Accumulation
Asynchronous Data Transfer and Processing
Distributed Optimizers
Profiling Tools
Common Challenges & Solutions
CUDA Memory Errors
Data Loading Bottlenecks
Communication Overheads in Multi-GPU Training
Model Not Converging in Distributed Training
Deadlocks in Multi-GPU Training
Errors with Mixed Precision Training
Errors in Cluster Training
Profiling Overheads
Model Parallelism Challenges
Version and Compatibility Issues
Summary
Chapter 9: Mobile and Embedded Deployment
PyTorch for Mobile and Embedded System
PyTorch on Mobile Devices
PyTorch in Embedded Systems
Future Possibilities
Conversion to TorchScript Models
Importing Libraries
Defining Model
Creating Instance of the Model
Converting to TorchScript using Tracing
Converting to TorchScript using Scripting
Loading and Running TorchScript Model
Deploying Model on Android
Install Android Development Environment
Create Android Project
Include PyTorch Mobile Libraries
Add TorchScript Model
Load and Run Model in Android Code
Build and Test on Android Device
Exploring PyTorch Lite
Key Features
Sample Program using PyTorch Lite
Performing Real-time Inferencing
Setting up the Environment
Real-time Inferencing
Exploring Model Compression
Model Compression Techniques
Model Compression using Pruning and Quantization
Common Challenges & Solutions
Model Conversion to TorchScript
Quantization Errors
Pruning-related Errors
Deployment on Android Devices
Real-Time Inferencing Challenges
Model Compression Errors
Multi-GPU and Cluster Training Errors
PyTorch Lite Implementation Errors
General Compatibility and Performance Issues
Summary
Index
Epilogue
Preface
Starting a PyTorch Developer and Deep Learning Engineer career? Check out this 'PyTorch Cookbook,' a comprehensive guide with essential recipes and solutions for PyTorch and the ecosystem. The book covers PyTorch deep learning development from beginner to expert in well-written chapters.
The book simplifies neural networks, training, optimisation, and deployment strategies chapter by chapter. The first part covers PyTorch basics, data preprocessing, tokenization, and vocabulary. Next, it builds CNN, RNN, Attentional Layers, and Graph Neural Networks. The book emphasises distributed training, scalability, and multi-GPU training for real-world scenarios. Practical embedded systems, mobile development, and model compression solutions illuminate on-device AI applications. However, the book goes beyond code and algorithms. It also offers hands-on troubleshooting and debugging for end-to-end deep learning development. 'PyTorch Cookbook' covers data collection to deployment errors and provides detailed solutions to overcome them.
This book integrates PyTorch with ONNX Runtime, PySyft, Pyro, Deep Graph Library (DGL), Fastai, and Ignite, showing you how to use them for your projects. This book covers real-time inferencing, cluster training, model serving, and cross-platform compatibility. You'll learn to code deep learning architectures, work with neural networks, and manage deep learning development stages. 'PyTorch Cookbook' is a complete manual that will help you become a confident PyTorch developer and a smart Deep Learning engineer. Its clear examples and practical advice make it a must-read for anyone looking to use PyTorch and advance in deep learning.
In this book you will get:
Comprehensive introduction to PyTorch, equipping readers with foundational skills for deep learning.
Practical demonstrations of various neural networks, enhancing understanding through hands-on practice.
Exploration of Graph Neural Networks (GNN), opening doors to cutting-edge research fields.
In-depth insight into PyTorch tools and libraries, expanding capabilities beyond core functions.
Step-by-step guidance on distributed training, enabling scalable deep learning and AI projects.
Real-world application insights, bridging the gap between theoretical knowledge and practical execution.
Focus on mobile and embedded development with PyTorch, leading to on-device AI.
Emphasis on error handling and troubleshooting, preparing readers for real-world challenges.
Advanced topics like real-time inferencing and model compression, providing future-ready skills.
GitforGits
Prerequisites
This book is designed for readers from all walks of life, whether a seasoned professional looking to expand their skillset, an academic seeking to delve deeper into research, or a beginner taking their first steps into the world of AI. Knowing basics of machine learning is preferred.
Codes Usage
Are you in need of some helpful code examples to assist you in your programming and documentation? Look no further! Our book offers a wealth of supplemental material, including code examples and exercises.
Not only is this book here to aid you in getting your job done, but you have our permission to use the example code in your programs and documentation. However, please note that if you are reproducing a