Engineering isn't what it used to be a decade ago. Computers and other modern technology have entirely changed the game. With so much technological advancement, better and more equipment keeps entering the market for better and optimized engineering results.
To stay up with the latest innovations, learning methods have evolved as well. Earlier, computers were required for engineering students to learn and practice computer-related programs. But since the advent of AI/ML/DL, the requirement of courses offered within these domains by the universities has changed a lot. Recent years and the advent of such technologies have marked the importance of GPUs (Graphical Processing Units) for extra computing power.
Earlier a laptop or computer with a dedicated GPU could have worked but as more and more data is used to train AI and ML models, the need for extra computing power has surged recently. This need, if addressed by traditional methods of installing GPUs locally on the device's premise, may increase the cost and risk significantly. To support the numerous resource-hungry engineering programs or training models, the laptop or desktop engineering colleges choose should have robust and advanced connectivity to cloud GPUs. The method of using a dedicated graphics card is long gone.
This article investigates why engineering colleges offering courses related to AI/ML/DL need to update their systems and make a shift to cloud GPUs for better training of students and to increase college performance as a whole. To learn more, please read on…
Table of Content:
- What are GPUs?
- Overview: Cloud GPUs
- Cloud GPU in engineering colleges
- How will Cloud GPUs benefit Engineering Students?
- End Note
What are GPUs?
Graphical Processing Units (GPUs) are electronic integrated circuits that were mostly employed for gaming until around a decade ago. The performance of computers is accelerated to a greater extent after the GPU is used. Its use relieves the CPU instance of memory-intensive tasks, resulting in improved performance overall. The original objective of the GPU was to provide greater visual quality to the user, but as time went on, it grew more versatile and improved its performance. Developers, not only gamers, began to use GPUs for mathematical and computationally heavy procedures. The astounding results of this ushered in the GPU era, in which GPUs were used well beyond their typical applications.
Overview: Cloud GPUs?
GPUs have several drawbacks that Cloud GPUs solve. As in, to get a job in the field of Artificial Intelligence or Machine Learning you need to train yourself with the computers of huge computing capacity to learn and train models quickly and efficiently. The reason that the computing capacity of the systems is a concern is - with so much digitalization a huge amount of data gets generated in fractions of seconds and leveraging this data and feeding it to algorithms for training, requires both time and patience. Computers enabled with huge computing powers make these tasks much easier and less frustrating. Setting up such systems might not be feasible physically but putting these systems in the cloud i.e. cloud GPUs and then using them helps in covering a lot of issues in a way that GPUs on premise might not resolve.
Cloud GPUs in engineering colleges-
The traditional approach of training one model after the other is long gone since the advent of Cloud GPU. Cloud computing enables larger access to parallel computing through enormous, virtual computer clusters, allowing the ordinary engineering student to take advantage of parallel processing power and storage possibilities formerly reserved for major corporations. Engineering colleges now don’t have to deploy big staff for the maintenance of the systems. Rather, these institutions can pay a cloud service provider for computational power. For heavy workload, high computational, and huge mathematical operations, GPU’s computing power does matter a lot.
In colleges or universities, many AI or deep learning models can benefit from the use of cloud graphics processing units to speed up the training process. Image classification, video analysis, and natural language processing all need compute-intensive matrix multiplication and other operations that can benefit from a Cloud GPU's massively parallel design. On a single processor, training a machine learning or deep learning model that requires expensive computation activities on extremely big datasets might take days. However, if your application is designed to offload those activities to one or more GPUs (cost effective and more secure on Cloud GPUs), training time can be reduced to hours rather than days. This will promote students to learn quickly and implement the models even more quickly.
How will Cloud GPUs benefit Engineering Colleges?
Apart from the fact that having a Cloud GPU subscription would provide students an advantage in terms of training and learning, there are several additional advantages that Cloud GPUs may provide to universities or colleges. These advantages include:
#1 Low Ownership Cost-
The first advantage of the cloud GPU is that it is less expensive than setting up a physical instance of a GPU-intensive system. Cloud GPU not only outperforms traditional GPUs in terms of computational power and mathematical calculations, but it also does it at a lower cost. There are a number of cloud GPU service companies that charge on an hourly basis. Colleges can have the cloud GPU service only when the students are in the lab or are learning and not on the days or time when college is off, this could help colleges save a lot of money.
#2 Customizable and secure-
Cloud GPUs offer services that are adaptable to the demands of the user. Because the services are also based on an hourly basis, these customizable services do not bind users to a certain service for an extended period of time. Taking advantage of this, engineering colleges may change their service preferences on an hourly basis and precisely customize them to the needs of the students, which gives them greater freedom. These cloud GPU services are completely customized, private, and secure.
#3 No need for Physical Infrastructure-
If your institution promotes learning flexibility from any corner of the institute and not merely fixed to the labs or classrooms, cloud GPU gives you the option of merely bringing your computer, and with a simple login to the Cloud GPU service provider site, you'll have your entire system of high processing power at your fingertips. This eliminates the need to transport a large number of GPUs or build huge infrastructures like labs and classrooms to accommodate students while they learn.
#4 Parallel Environment-
Rather than executing algorithms and training models one at a time, engineering students may build many parallel instances on cloud GPU to run numerous training sets for machine learning or deep learning models. This guarantees that time is saved while also lowering operational costs.
End Note-
In this article, we looked at why engineering colleges should use Cloud GPUs for training their students. Many prominent Indian colleges have already established Artificial Intelligence research centers with the Cloud GPUs service integrated. Hopefully, in the coming days with more universities establishing AI labs and research centers, many universities across India will subscribe to this new normal mode of using Cloud GPUs
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