As machine learning models grow in size and datasets become larger, training on a single device (CPU or GPU) often becomes impractical due to long training times or memory limitations. Scaling training across multiple devices or machines is frequently necessary to handle these demanding workloads efficiently.
This chapter introduces methods for distributing TensorFlow training jobs. You will learn about the core ideas behind distributed machine learning and TensorFlow's tf.distribute.Strategy API, which simplifies the process. We will cover specific strategies for different hardware setups:
MirroredStrategy for training on multiple GPUs within a single machine.MultiWorkerMirroredStrategy for synchronous training across multiple machines.ParameterServerStrategy for asynchronous approaches.TPUStrategy for utilizing Google's Tensor Processing Units.Additionally, you will learn techniques for managing data parallelism and approaches for debugging distributed training setups. Completing this chapter will equip you with the skills to accelerate the training of large-scale models using TensorFlow's distributed capabilities.
3.1 Fundamentals of Distributed Machine Learning
3.2 Overview of tf.distribute.Strategy
3.3 MirroredStrategy for Single-Node, Multi-GPU Training
3.4 MultiWorkerMirroredStrategy for Multi-Node Training
3.5 ParameterServerStrategy Concepts
3.6 TPUStrategy for Training on TPUs
3.7 Handling Data Parallelism Effectively
3.8 Debugging Distributed Training Jobs
3.9 Practice: Implementing Distributed Training
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