Distributed Training with TensorFlow

TensorFlow offers significant advantages by allowing the training phase to be split over multiple machines and devices. The main goal of distributed training is to parallelize computations, which drastically cuts down on the amount of time required to train a model. Furthermore, it enhances resource efficiency by dispersing the task among several devices, which optimizes resource utilization. Additionally, this method facilitates scalability because expanding data can be split between several devices for processing. TensorFlow uses a number of techniques to divide the computational load among distributed computing resources.

Distributed Strategy in TensorFlow

In TensorFlow, the idea of a Distributed Strategy acts as an interface between various machines or devices and the training data. The two most widely adopted distributed strategies are:

• MirroredStrategy: It uses data parallelism technique. Firstly, it allows model to replicate into each device and then the gradients are simultaneously calculated and synchronised during training.
• ParamterServerStrategy: It uses a paramter server architecture. Here, work is divided between parameter server and worker devices. Worker devices are responsible for computation whereas parameter servers store and udpate model parameters.

Though these strategies are offered by Tensorflow but it completely depends on us how we efficiently distribute the task between the multiple devices.

As the size of data sets and model complexity is increasing day by day, traditional training methods are often unable to stand up to the heavy requirements of various contemporary tasks. Therefore, this has given rise to the necessity for distributed training. In simple words, when we use distributed training the computational workload is split across a considerable number of devices or machines that would run the training of the machine learning models more quickly and efficiently.

In this article, we will discuss distributed training with Tensorflow and understand how you can incorporate it into your AI workflows. In order to maximize performance when addressing the AI challenges of today, we’ll uncover best practices and valuable tips for utilizing TensorFlow’s capabilities.