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Best PC Builds For Deep Learning In Every Budget Ranges

Opinion

Exploring the best PC Building rigs for performing deep learning computations in every budget range

Photo by 6 9 on Unsplash
Photo by 6 9 on Unsplash

The popularity of Deep learning has risen to its peak with all the modern developments in Artificial Intelligence. The rise to prominence and the wide array of applications that deep learning can perform are magnificent. The revolution of deep learning is bound to continue for the upcoming years as intensive research is being done on many major topics of interest.

If you are aiming to become proficient at deep learning, you will eventually need a powerful system. A system with which you can tackle the wide variety of challenging tasks that are available to you on the internet for deep learning. While aiming to achieve this objective, you might have certain budget constraints. And you might not have the best idea on the type of build that you are looking forward to building and achieve.

Keep in mind that all the builds mentioned in this article are for machine learning, specifically for those who are interested in diving and exploring deep learning even further. But, before we dive straight into our PC builds, let us understand some of the core requirements that will be essential for speeding up some of the tasks that we will encounter in our deep learning journey.

Before you continue reading this article, there are mainly a couple of pre-requisites I would like to mention for all the interested viewers. Ensure that you are completely willing to spend the specified amount depending on whether it is a low, average, or high-end build. More significantly, make sure you want to pursue deep learning as an essential part of your future.

If you are not too sure that you want to invest your time in it or if you want to pursue your dream without currently having the best budget to purchase a PC, I would highly encourage the viewers to check out the next section of the article.

Before you dive into the article, I would recommend checking out if you really require GPU for deep learning. This article will help you to learn about graphics processing units, CUDA Cores, and other essential concepts.

Do you Really Need A GPU For Deep Learning?


Unsure What To Do?

Photo by Abdul Barie on Unsplash
Photo by Abdul Barie on Unsplash

What to do if you have one of the following issues? –

  1. Lower budget constraints.
  2. Still unsure about deep learning.
  3. You don’t like spending money on technology (or you find technology boring).
  4. Other similar reasons.

The above reasons are absolutely valid as per each individual’s perspective. For such scenarios as mentioned in the following four points stated above, I mainly have a couple of suggestions. Firstly, I would highly recommend checking out the Google Colab environment as it offers numerous fantastic features for beginners and experts alike.

If you are a beginner to Data Science, AI, and deep learning, Google Colab is a Jupyter Notebook-like environment that offers the users an incredible platform to perform any kind of complex tasks just by signing into your email ID. You can also mount the Google Drive with the datasets and perform computations alongside.

I would like to suggest the first-time viewers or even those who are completely unsure about their interest or positioning in deep learning start using Google Colab and spending their time doing their projects and exploring their interests. Feel free to try out as much new stuff as you want because the Cloud environment provides you with a way to utilize its GPU as well as share the contents of the Jupyter Notebook with others quite easily.

In one of my previous works, I had written a complete starter guide on Jupyter Notebooks. Check out the following article provided below to learn almost everything about Jupyter Notebooks.

Everything You Need To Know About Jupyter Notebooks!

Another option that you should consider is to make use of the numerous Cloud platforms offered to you for artificial intelligence by various companies. These include AWS by Amazon, Azure by Microsoft, Watson Studio by IBM, and many others. Using these Cloud environments, you can not only build effective models as you desire, but you also have the ability to deploy them on the Cloud for it to reach a wider range of audiences.

With these initial queries out of the way, let us start exploring what are the best PC builds for Deep Learning across various budget ranges.


Best PC Builds For Deep Learning Across Various Budget Ranges:

Photo by Nana Dua on Unsplash
Photo by Nana Dua on Unsplash

Before we dive straight into which GPU might suit your builds the best, let us analyze the other essential components for building your ultimate PC build. For starters, the datasets will require a huge amount of storage for performing complex applications. If you want to work on bigger projects, then a hard disk (HDD) with large storage is a must. I would also recommend a smaller solid-state drive (SSD) to speed up your operations faster. The SSD can storage your operating system, and a wide array of tasks will be faster and efficient.

The next essential component is the Random Access Memory (RAM). The RAM on your system is significant for handling how quickly some of the tasks on your are PC are performed. Having a decently high RAM is often essential for better Productivity. The other essential element to consider in your PC build is the type of processor you are utilizing. The CPU choices for you are Intel or AMD. Both have their own benefits, and I would highly recommend the viewers to research them.

Finally, let us explore the best options for the heart of deep learning, which is the GPU. The obvious choice for the best company to choose the GPU is NVIDIA, mainly because of one primary reason. NVIDIA provides something called the Compute Unified Device Architecture (CUDA), which is extremely beneficial for the computation of deep learning models.

CUDA is a parallel computing platform and application programming interface model created by Nvidia. These CUDA cores are highly beneficial and evolutionary in the field of artificial intelligence. The research and time invested in these CUDA modules are significantly higher compared to other companies like AMD.

Although some of the other graphics cards may be powerful, they don’t have the complete support of CUDA for certain crucial operations. Hence, NVIDIA GPUs, for the purpose of deep learning, prevail over their other counterparts. NVIDIA’s CUDA supports multiple deep learning frameworks such as TensorFlow, Pytorch, Keras, Darknet, and many others.

While choosing your processors, try to choose one which does not have an integrated GPU. Since we are already purchasing a GPU separately, you will not require a pre-built integrated GPU in your CPU. Following this step will help you to save a bit more money. I have more experience with Intel CPUs, and I will comment on this topic more than AMD.

The other essential components like the motherboard, power supply unit (PSU), and the CPU casing are not going to be covered in detail in this article. The appropriate motherboard for your branch of CPU, whether Intel or AMD, should be researched and purchased accordingly. The PSU builds with Graphics cards often work best with at least 450W supply for lower budget builds and go up to 750W or more for the higher budget builds. The CPU casing is a choice of comfort. Choose whatever suits you best.

Low Budget Builds:

The components that you should be looking for in a low-budget PC build are a processor that can handle some complexity of operations, such as a Jupyter Notebook, with ease. The intel suggestions would be an i3- I3–10100F or any other i3–9th generation with the F series. These processors don’t have an integrated GPU and can perform most tasks optimally at a lower budget. I would recommend researching AMD CPUs as I have heard that these perform much better in the lower price budgets with higher cores and threads.

You should be looking for at least 8GB RAM at the very least. However, if the other components don’t fall into place, 4GB RAM should suffice momentarily. However, please upgrade them as soon as possible. Try to get a 128 GB SSD if you can, along with a 1TB HDD for performing decent level deep learning projects. Finally, for the Graphics cards, my primary recommendations would be a GTX 1050 or anything below that range.

Average Budget Builds:

The components that you should be looking for in an average budget PC build are a processor such as i5–9400F, i.e., a decent processor that can support most tasks without an integrated GPU. Please research the respective AMD counterparts thoroughly as well. From my research, AMD Ryzen 5 (6 cores, 3.4 GHz) seems like a suitable choice.

You should be looking for a RAM range of 8GB to 16GB, more preferably 16 GM of RAM. Try to purchase an SSD of size 256 GB to 512 GB for installing the operating system and storing some crucial projects. And an HDD space of 1TB to 2TB for storing deep learning projects and their datasets. For the Graphics cards recommendations, please look into any similar GPUs such as GTX 1650, GTX 1660, GTX 1660 Ti, RTX 2060, GTX 1070, and other similar GPUs in this range.

I would say that my first custom PC build is an average budget one as well, which is composed of the following components, namely i5–9400K, 2 TB HDD, 256 MB SSD, and the GPU GTX 1660. These builds constitute a remarkable performance for most smaller deep learning tasks. However, certain higher complexity problems like projects on GANs could be slightly problematic.

High Budget Builds:

The components that you should be looking for in a high-budget build are an AMD or an intel processor with or without an integrated GPU. At these price ranges, these CPUs often almost come with an integrated GPU. My intel recommendations would be something along the lines of an i7–10700k, i9–9900k, or an i9–10900X. As for the disk space, I would recommend 512 GB to 1 GB or more of SSD and at least a 4 TB HDD. Try to aim for a RAM of 16–32 GB DDR4 with a high frequency.

The optimal graphics cards for these ranges often extend to a wide variety of choices, including the purchasing of multiple graphics cards as well. Some of the best options are GTX 1080 Ti, RTX 2080, RTX 2080 Ti, RTX 3080, and RTX 3090, among many other fabulous options.

If you have a higher budget, then feel free to spend your resources in building up the best device possible. You can also look at other elements like RGB fans, or cooler CPU cases. You can also include the elements of liquid cooling to keep your high intensity engines from performing well.


Conclusion:

Photo by Artiom Vallat on Unsplash
Photo by Artiom Vallat on Unsplash

"AI winters were not due to imagination traps, but due to lack of imaginations. Imaginations bring order out of chaos. Deep learning with deep imagination is the road map to AI springs and AI autumns."

  • Amit Ray

As an additional tip, I would recommend the viewers to concentrate on the prices ranges of these GPUs in your respective countries. Whenever the prices drop substantially or slightly, would be the best time for you to purchase these devices. Keep researching on your own as well to look for better options in your price ranges.

The article covers most of the essential aspects to look into your PC builds for obtaining the best ever deep learning setup you can achieve in your desired price ranges. Feel free to research and explore more until you ultimately decide what would be the best option for you. As a side note, my personal suggestion would be a big YES and a go for it if you have the required budget.

Worst case scenario, if you buy a PC and then decide that deep learning is probably not the best choice for you as it does not suit your current interests, you can still use it as a device for gaming, editing, streaming, and so much more. So, if you can afford it, then go for it! The regrets made with this choice will be few.

If you have any queries related to the various points stated in this article, then feel free to let me know in the comments below. I will try to get back to you with a response as soon as possible.

Check out some of my other articles that you might enjoy reading!

7 Best Free Tools For Data Science And Machine Learning

Best Library To Simplify Math For Machine Learning!

6 Best Programming Practices!

5 Essential Skills To Develop As A Data Scientist!

5 NLP Topics And Projects You Should Know About!

Thank you all for sticking on till the end. I hope all of you enjoyed reading the article. Wish you all a wonderful day!


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