Graphic cards deal with lots of challenging operations related to the number-crunching of image and video data. Since the computer's CPU, which traditionally manages this sort of task, has lots of stuff on its plate, it's usually the case that the graphics card has its own processor for handling all the data processing. This processor is referred to as GPUs (a CPU specializing in graphics data) and plays an essential role in our lives today, even when we don't care about the graphics on our computer. As we've seen in the corresponding book I've co-authored, it's crucial for many data science and AI-related tasks. In this article, we'll look at the latest information on this topic.
First thing's first: data science and A.I. needing GPUs is a modern trend, yet it's bound to stick around for the foreseeable future. The reason is simple: many modern data science models, especially those based on A.I. (such as large-scale Artificial Neural Networks, aka, Deep Networks), require lots of computing power to train. This additional computing requirement is particularly the case when there is lots of data involved. As CPUs come at a relatively higher cost, GPUs are the next best thing, so we use them instead. If you want to do all the model training and deployment on the cloud, you can opt for servers with extra GPUs for this particular task. These are referred to as GPU servers and are a decisive factor in data science and A.I. today.
What's the catch with GPUs, though? Well, first of all, most computers have a single graphics card, meaning limited GPU power on them. Even though they are cheaper than CPUs, they are still a high cost if you have large DNNs in your project. But the most critical impediment is that they require some low-level expertise to get them to work, even though it's simpler than building a computer cluster. That's why more often than not, it makes more sense to lease a GPU server on the cloud rather than build your own computer configuration utilizing GPUs. Besides, the GPU tech advances rapidly, so today's hot and trendy may be considered obsolete a couple of years down the road.
Beyond the stuff mentioned earlier, there are some useful considerations that are good to have in mind when dealing with GPUs in your data science work. First of all, GPUs are not a panacea. Sometimes, you can get by with conventional CPUs (e.g., standards cloud servers) for the more traditional machine learning models and the statistical ones. What's more, you need to make sure that your deep learning framework is configured correctly and leverages the GPUs as expected. Additionally, you can obtain extra performance from GPUs and CPUs if you overclock them, which is acceptable as a last resort if you need additional computing power.
For GPU servers that are state-of-the-art yet affordable, you can check out Hostkey. This company fills the GPU server niche while providing conventional server options for your data science projects. Its key advantage is that it optimizes the performance/cost metric, meaning you get a bigger bang for your buck in your data models. So, check it out when you have a moment. Cheers!
Zacharias Voulgaris, PhD
Passionate data scientist with a foxy approach to technology, particularly related to A.I.