Introduction

TensorFlow is an open-source machine learning framework developed by the Google Brain team. It provides a wide suite of functionalities aimed at building and training machine learning models, especially for deep learning applications. One of the striking features of TensorFlow is its ability to execute computations on various hardware configurations, including CPUs, GPUs, and even across multiple devices. This support gives rise to two prominent configurations: multi-GPU and distributed TensorFlow. Although they might sound somewhat similar, they cater to different needs and use-cases in handling large-scale machine learning models. This article delves into the differences and applications of these two configurations.

Multi-GPU TensorFlow

Overview

In a typical machine learning or deep learning task, training a neural network model is computationally intense. GPUs (Graphics Processing Units) are specifically designed for high-throughput computations and are particularly suitable for training large models. Multi-GPU support in TensorFlow allows one to utilize more than one GPU on a single machine to accelerate computational processes.

Technical Explanation

• Data Parallelism: In this setup, the same model is copied across multiple GPUs. Input data is divided into smaller batches, each processed by one of the GPUs. Upon completion of forward and backward propagation, the gradients from each GPU are averaged and the weights are updated. This process is known as Synchronous Training.
• Model Parallelism: This is less common and is used for very large models where a single GPU cannot fit all the parameters at once. Here, different parts of the model are assigned to different GPUs. Careful implementation is required to ensure efficient communication between GPUs.
• Types of Distribution Strategies: TensorFlow provides multiple strategies that can be applied:
  • Central Storage Strategy: This approach involves parameter servers to store parameters, while worker machines handle computation. Workers get updated parameters from the servers and then update them back after computation.
  • Parameter Server Strategy: A variation of central storage where specifically designed parameter servers maintain models' parameters.
  • Multi-Worker Mirrored Strategy: Uses all machines as a synchronized group of workers emulating a multi-GPU setup, effectively mimicking data parallelism at this level.
• Communication Mechanism: Data is partitioned and parallelized across machines, leveraging TensorFlow's gRPC protocol for efficient data exchange.