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
Overview of tf.distribute.Strategy
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- Distributed training with TensorFlow, TensorFlow Developers, 2024 - The official guide to
tf.distribute.Strategy, explaining its principles, various strategies, and usage examples with Keras and custom training loops. - Horovod: Fast and Easy Distributed Deep Learning Training, Alexander Sergeev, Mike Del Balso, 2018 arXiv preprint arXiv:1802.05799 DOI: 10.48550/arXiv.1802.05799 - Introduces Horovod, a distributed training framework that efficiently implements data parallelism using all-reduce, illustrating concepts for synchronous strategies like
MirroredStrategy. - Large Scale Distributed Deep Networks, Jeffrey Dean, Greg Corrado, Rajat Monga, Kai Chen, Matthieu Devin, Mark Mao, Marcaurelio Ranzato, Andrew Senior, Paul Tucker, Ke Yang, Quoc V. Le, Andrew Y. Ng, 2012 Advances in Neural Information Processing Systems (NIPS) 25 (Curran Associates, Inc.) - A paper presenting a large-scale distributed training system using a parameter server architecture, relevant for understanding the principles behind
ParameterServerStrategy. - A Domain-Specific Architecture for Deep Neural Networks, Norman P. Jouppi, Cliff Young, Nishant Patil, David Patterson, 2018 Communications of the ACM, Vol. 61 (Association for Computing Machinery (ACM)) DOI: 10.1145/3154484 - Describes the architecture of Google's Tensor Processing Unit (TPU) and its evolution, giving insight into the hardware optimized for deep learning, directly supporting the
TPUStrategy.