Advanced TensorFlow Techniques
Chapter 1: TensorFlow Execution and Graphs
TensorFlow's Execution Modes: Eager vs. Graph
Understanding tf.function and AutoGraph
Tracing Mechanics and Graph Representation
Implementing Control Flow in Graphs
Automatic Differentiation with tf.GradientTape
Managing Resources and Memory
Debugging TensorFlow Programs
Practice: Optimizing Function Tracing
Chapter 2: High-Performance TensorFlow
Profiling TensorFlow Code with TensorBoard Profiler
Optimizing GPU Utilization
Mixed Precision Training Techniques
Introduction to Tensor Processing Units (TPUs)
XLA (Accelerated Linear Algebra) Compilation
Performance Considerations for tf.data Pipelines
Hands-on Practical: Profiling and Accelerating a Model
Chapter 3: Scaling Training with Distributed Strategies
Fundamentals of Distributed Machine Learning
Overview of tf.distribute.Strategy
MirroredStrategy for Single-Node, Multi-GPU Training
MultiWorkerMirroredStrategy for Multi-Node Training
ParameterServerStrategy Concepts
TPUStrategy for Training on TPUs
Handling Data Parallelism Effectively
Debugging Distributed Training Jobs
Practice: Implementing Distributed Training
Chapter 4: Advanced API Usage and Custom Components
Subclassing tf.keras.Model for Flexibility
Creating Custom tf.keras Layers
Implementing Custom Loss Functions
Developing Custom Metrics
Writing Custom Training Loops
Working with Ragged Tensors and Sparse Tensors
Using TensorFlow Addons for Specialized Operations
Hands-on Practical: Building a Custom Model Pipeline
Chapter 5: Production ML Pipelines with TFX
Introduction to TensorFlow Extended (TFX)
TFX Standard Components Overview
Data Ingestion and Validation
Feature Engineering with Transform
Model Training and Tuning
Model Validation and Analysis
Serving and Deployment with Pusher
Orchestrating TFX Pipelines
Practice: Building a Simple TFX Pipeline
Chapter 6: Model Deployment and Optimization
Saving and Loading Advanced Model Formats
Introduction to TensorFlow Serving
Deploying Models with TF Serving via REST and gRPC
Model Optimization Techniques
Introduction to TensorFlow Lite (TF Lite)
Converting Models for TF Lite
Optimizing for On-Device Inference
Hands-on Practical: Deploying a Model with TF Serving
Chapter 7: Implementing Advanced Architectures
Building Attention Mechanisms from Scratch
Implementing Transformer Blocks
Generative Adversarial Networks (GANs) Concepts
Coding a Simple GAN in TensorFlow
Graph Neural Network (GNN) Basics with TF
Reinforcement Learning Agents with TF-Agents
Practice: Implementing a Transformer Encoder Layer
Debugging Distributed Training Jobs
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- Distributed training with TensorFlow, TensorFlow Authors, 2024 - Provides comprehensive guidance on setting up and using TensorFlow's distributed training strategies, which is essential for understanding and debugging distributed jobs.
- Profile TensorFlow performance with the Keras Callback and TensorBoard, TensorFlow Authors, 2024 - Details how to use TensorBoard Profiler for performance analysis in TensorFlow, including distributed scenarios, which is a key debugging tool for identifying bottlenecks and stragglers.
- Deep Learning (Chapter 16: Parallel and Distributed Training), Ian Goodfellow, Yoshua Bengio, and Aaron Courville, 2016 (MIT Press) - Offers foundational theoretical and practical considerations for parallel and distributed training, including communication, synchronization, and data/model parallelism challenges that often lead to debugging issues.
- Performance Analysis of Distributed Deep Learning Training, Fan Yang, Xiaoxiao Wu, Jinkun Geng, Kaiwei Tu, Zongjian Hu, Jianxun Liu, Xiang Li, and Xiaoming Li, 2020 arXiv preprint arXiv:2010.02640 DOI: 10.48550/arXiv.2010.02640 - Examines common performance bottlenecks and analysis methods in distributed deep learning, addressing topics like stragglers, communication overheads, and system resource utilization that are central to debugging.