What is Distributed Training?
Distributed training is a state-of-the-art technique in machine learning where model training is obtained by combining the computational workloads split and arranged across different devices at a time, each of them contributing to the whole training in an active way.
As you know, in machine learning data is the key to successfully building a model. The more quality data you have, the better your model can train, However, as the size of your dataset increases, your model’s complexity and calculations will also increase. This would make training a time-consuming process. Thus, one of the major reasons distributed training is used is that it will make computation faster in the case of training of large-scale models.
There are two approaches to distributed training, they are:
- Data Parallelism: In Data Parallelism, the training data is split across different devices available for computation. Therefore, a copy of the model is trained on each device using different portions of data. All the models are synchronized periodically which makes sure that all of them have the same weights. This method is likely to work best when we have a large model and dataset.
- Model Parallelism: In the case of model parallelism, we split the model itself rather than splitting the data. The different parts of the model are trained on different machines. For example, if a model first calculates the product and then sums, then one of the devices computes the product while another device adds up the products. You can use model parallelism especially if the model is too large to fit in the memory of a single machine. It is comparatively complex and less common, however still used in some specialized applications.
Distributed Training with TensorFlow
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.
Table of Content
- What is Distributed Training?
- Distributed Training with TensorFlow
- How does Distributed Training work in Tensorflow?
- Optimizing Distributed Training: Best Practices & Fault Tolerance
- Optimizing Performance in Distributed Training
- Monitoring, Debugging, and Fault Tolerance
- Conclusion