Setting up a deep-learning-based cloud environment

Before we begin setting up a cloud-based deep learning environment, we might wonder why would we need it or how a cloud-based deep learning environment would benefit us. Deep learning requires a massive amount of mathematical calculation. At every layer of the neural network, there is a mathematical matrix undergoing multiplication with another or several other such matrices. Furthermore, every data point itself can be a vector instead of a singular entity. Now, to train over several repetitions, such a deep learning model would require a lot of time just because of the number of mathematical operations involved.

A GPU-enabled machine would be much more efficient at executing these operations because a GPU is made specifically for high-speed mathematical calculations however, GPU-enabled machines are costly and may not be affordable to everyone. Furthermore, considering that multiple developers work on the same software in a work environment, it might be a very costly option to buy GPU-enabled machines for all the developers on the team. For these reasons, the idea of a GPU-enabled cloud computing environment has a strong appeal.

Companies nowadays are increasingly leaning towards the usage of GPU-enabled cloud environments for their development teams, which can lead to the creation of a common environment for all of the developers as well as the facilitation of high-speed computation.