Keras is a high-level neural network API written in Python for building and training deep learning models. It provides an easy-to-use interface to design complex neural networks and its APIs are designed to be user-friendly and intuitivewhich makes the process of building and training models faster and more efficient.
However, deep learning models can be computationally intensive and require significant computational resources to train on large datasets. To speed up the training process, one can use a distributed training framework like Horovod or TensorFlow’s tf.distribute, which enables distributed computing across multiple GPUs or machines.
The purpose of using Keras with a distributed training framework is to achieve faster and more scalable training of deep learning models. By utilizing distributed training, one can parallelize the training process across multiple nodes and GPUs, which can reduce the training time and achieve higher throughput.
One of the most significant advantages of using Keras with a distributed training framework like Horovod or TensorFlow’s tf.distribute is that the user can continue to use Keras’s high-level APIs while leveraging the full power of distributed training. The user can write Keras code the same way they would for a single GPU/machine, and the distributed training framework will handle the distribution of the workload transparently.
For example, if we have a deep learning model built using Keras that takes a long time to train on a single GPU, we can distribute the training using Horovod or TensorFlow’s tf.distribute. This will enable us to train our model on multiple GPUs or machines in parallel, significantly reducing the training time and allowing us to scale up our training to larger, more complex models and datasets.
In summary, using Keras with a distributed training framework like Horovod or TensorFlow’s tf.distribute enables faster and more scalable training of deep learning models by parallelizing the training process across multiple GPUs or machines while still allowing the user to leverage the high-level APIs provided by Keras.