Distributed Deep Learning Training Strategies: Data and Model Parallelism for High Performance

Are you looking to scale out your deep learning training beyond a single machine and GPU to achieve higher performance and efficiency? In this blog post, we will explore the different strategies for distributing training in deep learning models.

In many cases, deep learning training can be done on a single machine with a single GPU. However, when dealing with large datasets or when the hardware is not capable enough, scaling out becomes necessary. Scaling out involves adding more GPUs to the system or using multiple machines within a cluster. To distribute training efficiently in such scenarios, we need to employ strategies that suit our specific use case, data, and model.

Two major schools of distributed training strategies are data parallelism and model parallelism. Data parallelism involves scattering data across multiple GPUs or machines and performing training loops synchronously or asynchronously. Model parallelism, on the other hand, splits the model into different chunks and trains each chunk on a different machine. This is often used for very large models like those in natural language processing.

One common strategy for data parallelism is synchronous training, where all workers or accelerators train on different slices of data and aggregate gradients in each step. TensorFlow provides the `tf.distribute.MirroredStrategy` for this purpose. Similarly, `tf.distribute.experimental.MultiWorkerMirroredStrategy` is used for training on multiple workers.

Asynchronous training, on the other hand, allows workers to train at different rates without waiting for each other. The Parameter Server Strategy is a common technique for asynchronous training, where some devices act as parameter servers holding the model parameters, while others act as training workers.

Model parallelism involves splitting the model architecture instead of the data, which can be beneficial for very large models. A common use case for model parallelism is natural language processing models like Transformers.

In conclusion, understanding the various distributed training strategies in deep learning is essential for efficiently scaling your training process. By leveraging data and model parallelism, as well as synchronous and asynchronous training techniques, you can achieve higher performance and efficiency in training your deep learning models.