Is having a GPU an important requirement for deep learning? This question could be going through your mind whether you must have a GPU for you to learn deep learning.
In this post lets Getting to understand GPU, its importance, and exploring alternatives.
Do I Need GPU For Deep Learning?
Though it’s not a must but Yes, you’ll need GPU for deep learning, it’s very important. GPUs are designed for training artificial intelligence and deep learning models since they can process multiple computations simultaneously.
This is only possible because of the large number of cores that they have. Though purchasing GPU is quite expensive especially as a student or your pocket is somehow loose.
Or you can use the alternatives on your computers if you have made up your mind that it is deep learning that you want to do.
Yes, you’ll need GPU for deep learning, it’s very important.
After doing a lot of research and being a lover of Artificial Intelligence especially deep learning, I have come to realize that they still use graphics cards as well. I now have a clear understanding of how powerful the graphics card is.
There is not much difference between GPUs and graphics cards as they mean the same thing expect to find them interchangeably in this post as I will be using them many times.
Let me help you get more understand as to what exactly a GPU and CUDA are, and after that, we will take a look at the benefits of graphics processing units as well as when you should consider purchasing them if it is good for you. We will also discuss other alternatives.
All my childhood I have been a huge fan of playing computer games and every time I saw a graphics card, I knew exactly that it was for gaming purposes.
What is the GPU about?
A GPU is a specialized, electronic circuit designed to rapidly manipulate and alter memory to accelerate the creation of images in a frame buffer intended for a display device.
These devices are very important in modern computing. GPU computing and high-performance networking are transforming deep learning.
Its advancement help contributes a tremendous factor to the general growth of deep learning.
CUDA(Compute Unified Device Architecture) is provided by NVIDIA and it is very important in supporting the various deep learning applications.
CUDA is a parallel computing platform and application interface model created by NVIDIA. It helps software Engineers to use GPU for general purpose processing through an approach known as GPGPU.
GPUs are designed for training artificial intelligence and deep learning models.
The benefits of a GPU!
As we have seen in our earlier discussion, the NVIDIA GPUs provide the support of CUDA cores.
The number of CUDA cores varies from each type of graphics card to the other but my research reveals that most have way over 1000 of them.
When using a deep learning framework such as TensorFlow or Pytorch, you can be sure to utilize these cores for computing your deep learning algorithms significantly faster in comparison to the same performance CPU.
Since CPUs can only perform a few operations at the same time, GPUs on the other hand can perform these thousands of such operations at once.
Let’s take an example whereby a task that needs at least 2 or 3 hours to train on a CPU could just be finished in 10 minutes with the assistance of a good GPU.
A GPU is a very important resource for computer vision and supercomputing with deep learning and neural networks to perform complicated tasks even beyond human imaginations.
GPUs are also supported in other applications. They are very useful in embedded systems, mobile phones, PCs, workstations, and game consoles.
When it comes to gaming, GPUs are also very important. You can easily play games such as AAA games.
GPUs are also used in the robotics industry to equip high-tech robots to perceive the environment with the integration of artificial intelligence into them.
In the automotive industry, GPUs also play a very important role in applications of deep learning-based self-driving cars. There are a lot of inventions going on and in the future, we might expect to find self-driven cars.
GPUs also play a very important role in the medical industry, you can get a good number of applications in the field of medicine for utilization of data for ideal image segmentation tasks and other medical applications.
Purchasing GPU is quite expensive especially as a student or your pocket is somehow loose.
When should you consider it then?
To be honest with you guys, purchasing these cards is quite expensive especially when your pocket is somehow loose.
But if you have made up your mind that it is deep learning that you want to do, then you will have to sacrifice to make sure you get one. Deep learning requires a lot of graphics work and there is no way you will avoid using a GPU.
And if you are not able to purchase these cards and you still are willing to do deep learning, then you should not worry as we are going to give you alternatives that you can use at the time before you can afford to buy a real GPU.
You can use the alternatives on your computers if you have made up your mind that it is deep learning that you want to do.
What are the alternatives?
If you want to use your personal computer as a tool in deep learning, you can still work on it as long as your computer has a moderate CPU that can do all the computations, then you can choose to install the CPU version for TensorFlow. The installation procedure can be done just using a command as follows;
Pip install TensorFlow
For simple learning computations such as working with the MNIST dataset, there is not much difference between whether you want to use the GPU or the GPU version.
The GPU version must work just fine for beginner-level deep learning projects.
If you want to use the real GPU and experience some hands-on experience, then you can still do it on Google Colaboratorty or Google Colab.
This is a product from Google research. It(Collab) allows you to write and execute arbitrary python code through the browser and is especially well suited to machine learning, data analysis, and education.
Final thought
Do I need a GPU to learn deep learning? To answer today, is question, it is not a must to have a GPU for you to learn deep learning, as we have seen, if you are a beginner, you can just do it on your personal computer through a simple process of installing the CPU version and then running a command pip install TensorFlow. You can also use Google Colaboratory to write and execute your computations freely.
However, having a GPU will help simplify this process as you will not have to follow a long process to achieve the same goal.
So my advice is that if you want to do deep learning, then try as much as you can to have this device, it will help you a lot.
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