Do you Really Need A GPU For Deep Learning? | by Bharath K | Towards Data Science

I have always been a huge fan of gaming and a bit of a gaming nerd.
Since childhood, the only requirement I saw for graphics cards was for the purpose of gaming.
Luckily for me, after I started getting into artificial intelligence and data science, especially deep learning, I realized the true potential of graphics cards.
This was almost like a dream come true. It was like striking two bullseyes with one arrow because I could utilize the same single graphics card for gaming as well as study and research purposes.
Note: GPUs and graphics cards pretty much mean the same thing and will be used interchangeably throughout this article.
GPUs are optimized for training artificial intelligence and deep learning models as they can process multiple computations simultaneously. They have a large number of cores, which allows for the better computation of multiple parallel processes.
In this article, we will understand what exactly a GPU and CUDA is, then explore the benefits of graphics processing units as well as when you should consider buying it if you are on a budget constraint. Finally, we will discuss the alternatives.
Without further ado, let us get started with understanding these concepts.
A Graphics Processing Unit is a specialized, electronic circuit designed to rapidly manipulate and alter memory to accelerate the creation of images in a frame buffer intended for output to a display device.
GPUs are a key part of modern computing. GPU computing and high-performance networking are transforming computational science and AI. The advancements in GPUs contribute a tremendous factor to the growth of deep learning today.
NVIDIA provides something called the Compute Unified Device Architecture (CUDA), which is crucial for supporting the various deep learning applications.
CUDA is a parallel computing platform and application programming interface model created by Nvidia. It allows software developers and software engineers to use a CUDA-enabled graphics processing unit (GPU) for general-purpose processing — an approach termed GPGPU.
These CUDA cores are highly beneficial and evolutionary in the field of artificial intelligence. We will discuss this topic further in the next section.
As discussed earlier, the NVIDIA GPUs offer the support of CUDA cores.
The number of CUDA cores differs for each type of graphics card, but it is safe to assume that most usually have over at least 1000 of them.
When you are using a deep learning framework such as TensorFlow or Pytorch, you can utilize these CUDA cores for computing your deep learning algorithms significantly faster in comparison to the same performance with a CPU.
While CPUs can do only a handful of operations at once, GPUs can perform thousands of these operations at once. Just to mention a hypothetical example, a task requiring 2–3 hours to train on a CPU could potentially be completed in about 10 minutes with the help of a decent quality GPU.
GPUs are a dynamic resource for computer vision and supercomputing with deep learning and neural networks to perform complicated tasks, sometimes even beyond human imagination.
Also, there are lots of other applications for GPUs as well. GPUs find their uses in embedded systems, mobile phones, personal computers, workstations, and game consoles.
You could make the best possible use of these graphics cards for other tasks like gaming and playing AAA games (triple-A games are basically an informal classification used for video games produced and distributed by a mid-sized or major publisher, typically having higher development and marketing budgets.) or for other graphical software purposes that need the requirement of a GPU as well like animation and designs.
GPUs also have an enormous scope in the field of robotics enabling high-tech robots to perceive the environment with the integration of artificial intelligence into them.
They are also in high demand in the automotive industry for the applications of deep learning-based self-driving cars. Large investments are being made in the development of these cars to change the future.
Last but not least, these GPUs also find a variety of applications in the medical fields of healthcare and life sciences by utilizing the data for ideal image segmentation tasks and other medical applications.
Graphics cards are pretty expensive!
Especially when you are on a budget constraint, you probably will have a tough time deciding if you want to buy a graphics card or not. So, how can you come to a sensible conclusion regarding this question?
A personal suggestion that I would like to offer is to make sure you dig yourself deeper into the field of data science. Understand the concepts of machine learning and have a basic conceptual understanding of deep learning.
If you absolutely feel certain and confident that deep learning is something you are interested in and want to pursue this intriguing topic further, then feel free to go ahead and purchase one for yourself.
In case you are wondering if there are any alternative choices or free tools to test the subject before straight away diving in, then you are in luck because there are lots of these resources available.
Let us look into these alternatives and other options available.
If you want to test out deep learning or you have concluded that you have no use for a graphics card, then what are the possible alternatives that you could choose for deep learning?
For starters, if you want to work on your own personal computer, and you have a moderate CPU capable of average computations, then you can choose to install the CPU version for TensorFlow. This simple installation can be done by a simple pip command as follows:
pip install tensorflow
For simple deep learning computations like working with the MNIST dataset, it does not make a big difference if you want to utilize the CPU or GPU versions. The CPU version should work just fine for beginner-level deep learning projects.
However, if you want hands-on experience and the feel of using a GPU, then you can do so completely for free on the Google Colaboratory or Google Colab in short. It is a product from Google Research. Colab allows anybody to write and execute arbitrary python code through the browser and is especially well suited to machine learning, data analysis, and education.
I will be writing two articles shortly on the complete guides to Jupyter Notebooks as well as Google Colab shortly in the coming week. Stay tuned for those if want to understand a detailed guide on using them.
The other alternatives include creating an AWS cloud instance or using the IBM Watson Studio, and a few more approaches where you can pay a small amount for using a GPU on the cloud.
These are some of the alternative methods from my experience. If you have better alternative suggestions, then please let me know in the comments section.
But with these alternatives, you should easily be able to explore deep learning for yourself and gain a basic understanding of the subject.
In this article, we have covered the topics of what exactly a GPU is and what are its numerous benefits. I also suggested when would be the best time to consider owning a GPU for yourself. We also checked out the various alternatives that can help you thrive in learning deep learning basics even without a GPU.
So, to answer the question “Do you Really Need A GPU For Deep Learning?” in simple terms —
If you are a beginner and you are just getting started, then absolutely not. However, if you are more serious, gained a sharper understanding and knowledge, and want to move further with deep learning, then it is highly recommended.
Check out some of my other articles that you might enjoy reading!
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Thank you all for sticking on till the end. I hope all of you enjoyed reading the article. Wish you all a wonderful day!