Deep learning requires massive parallel computing capabilities provided by datacenter grade GPUs such as Tesla V100. The more the data you have, the more the processing power needed to train a deep learning model.
The truth about developing a deep learning model is that it’s hard to find the right set of hyperparameters with just one attempt. You need to experiment continually.
As a data science professional, you know underfitting and overfitting can occur when developing learning models. So, you need access to computation power that can help you train deep learning models quickly.
Buying a GPU based deep learning machine
Buying your own GPU incurs upfront costs, and you have to stick with the same GPU for a sizable period to get a return on the costs involved. Not to say that the best GPU for deep learning workloads, Tesla V100, can cost you upwards of USD 10k.
Having your GPU server on-prem means, you need to ensure that electricity and bandwidth requirements are sufficient to run your deep learning experiments.
What if you’re working on a larger dataset than you usually do?
You can tap into a GPU cloud services such as E2E GPU Cloud, where you can pay hourly and monthly basis as long as you use the GPU services.
A lot of innovations are happening in the deep learning world. The world’s famous GPU maker NVIDIA is quickly catching up and introducing newer and better hardware to tackle the processing needs of deep learning. If you buy your own GPU based deep learning machine, you will incur 3-year amortization periods. What if the GPU machine you bought becomes obsolete or can’t deliver quickly enough?
Rent a GPU based deep learning machine (aka Cloud GPU service)
Cloud GPU services like E2E GPU Cloud are agile to the latest innovation and developments in the deep learning space. If tomorrow NVIDIA introduces a GPU better than Tesla V100, GPU service providers can quickly bring that innovation to their services, letting you tap into the latest innovations without committing sizeable upfront costs.
If you need to cut down the time to deliver your deep learning models, you can run your workloads on multiple GPU instances at much lower costs than acquiring and running GPU machines on-premises.
When you deploy deep learning models for inferencing in production environments, you’d need continuous uptime and processing capacity to serve end-users adequately. On E2E Cloud, you can scale up & scale back on-demand as your situation demands; this ensures a smooth experience for your end-users.
If you conduct deep learning experiments with small datasets, buying a simple GPU based machine would suffice. Occasionally, you can tap into a Cloud GPU service when needed.
If you work with massive data sets and plan to deploy your models in production environments, services like E2E GPU Cloud would be the best option for you.
As a side note, E2E GPU Cloud offers Tesla V100 and NVIDIA T4 based GPU instances in the Indian region and saves up to 70% costs for you when compared to other GPU Cloud providers, without compromising quality.