How to Build a Deep Learning PC in 2020

How to Build a Deep Learning PC in 2020

With the right deep learning PC, you can train sophisticated models and achieve state-of-the-art results. But what exactly do you need to build one? In this post, we’ll guide you through the process of choosing the right components and putting everything together. By the end, you’ll have a powerful deep learning machine that can handle the most complex tasks.

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Introduction

As deep learning becomes more popular, so does the demand for hardware that can handle the computational demands of training and deploying neural networks. While it’s possible to get by with a modestly powerful CPU and graphics card for some tasks, such as running inference on pre-trained models, building a deep learning PC capable of handling more complex workflows is becoming increasingly necessary.

In this guide, we’ll show you how to build a deep learning PC that can handle the most demanding tasks while staying within a reasonable budget. We’ll also provide some recommendations on what components to choose and where to find the best deals.

With that said, let’s get started!

What You’ll Need

If you’re interested in building a deep learning PC, there are a few things you’ll need to consider. Firstly, you’ll need to choose a CPU that is powerful enough to handle the demands of training large neural networks. You’ll also need to select a suitable GPU (graphics processing unit) which can accelerate the training process. Finally, you should choose an appropriate deep learning framework, such as TensorFlow or PyTorch, and install it on your PC.

In terms of hardware, you’ll need a CPU with at least 4 cores and a GPU with at least 4GB of memory. However, for more demanding applications, such as training large neural networks, you may need a CPU with 8 or even 16 cores, and a GPU with 8GB or more of memory. In terms of software, you should install the latest version of your chosen deep learning framework.

Once you have all the necessary hardware and software installed on your PC, you’re ready to start building deep learning models!

Step 1: Choose Your Graphics Card

If you want to build a deep learning PC that can handle the most demanding tasks, you’ll need a top-of-the-line graphics card. The best deep learning graphics cards are:

-Nvidia Titan RTX
-Nvidia GeForce RTX 2080 Ti
-Nvidia GeForce RTX 2080
-AMD Radeon VII

These cards are the most expensive, but they’re also the most powerful. If you’re on a budget, the Nvidia GeForce GTX 1080 Ti is a good option. It’s not as powerful as the other cards on this list, but it’s still a very good card for deep learning.

Step 2: Choose Your Processor

Deep learning is a branch of machine learning that uses algorithms to model high-level abstractions in data. By doing so, deep learning can enable computers to predict complex outcomes without being explicitly programmed to do so.

The most important factor in building a deep learning PC is the processor. A good processor will allow you to train your models faster and achieve better results. There are two main types of processors that you can choose from: CPUs and GPUs.

CPUs (central processing units) are the traditional type of processors that are found in most computers. They are good at performing a wide variety of tasks, but they are not as efficient at deep learning as GPUs (graphics processing units). GPUs are designed for processing large amounts of data in parallel, which makes them much faster at deep learning.

If you want to build a deep learning PC, you should choose a processor with as many cores as possible. A CPU with more cores will be able to train your models faster and achieve better results. You should also choose a processor with a high clock speed (measured in GHz). A higher clock speed means that your processor can perform more calculations per second, which will again lead to better results.

When choosing a processor for your deep learning PC, you should also consider the price. Deep learning requires a lot of computations, so you will need a powerful processor that can handle the load. However, powerful processors often come with a high price tag. You will need to balance your budget with your needs when choosing a processor for your deep learning PC.

Step 3: Choose Your Motherboard

Now that you’ve decided on a case and a CPU, it’s time to pick a motherboard. The motherboard is the backbone of your PC, providing the connections and basic features that all other components rely on.

When choosing a motherboard for a deep learning PC, there are three main things to consider: size, form factor, and chipset.

Size: The first thing to consider is the size of your motherboard. The most common size for desktop motherboards is ATX, which offers the greatest compatibility with cases and other components. However, if you’re looking to build a smaller deep learning PC, you may want to consider a microATX or mini-ITX motherboard.

Form Factor: The next thing to consider is the form factor of your motherboard. The most common form factor for desktop motherboards is ATX, but there are also smaller form factors such as microATX and mini-ITX. If you’re looking to build a small deep learning PC, you may want to consider a mini-ITX motherboard.

Chipset: The chipset is the heart of your motherboard, providing the connections and basic features that all other components rely on. When choosing a chipset for your deep learning PC, you’ll want to look for one that supports PCIe 4.0 (if you’re using an AMD Ryzen 3000 series CPU) or Thunderbolt 3 (if you’re using an Intel Core X-series CPU).

Step 4: Choose Your Memory

Memory, or RAM, is one of the most important parts of any computer, but it’s especially important for deep learning. That’s because deep learning algorithms can require a lot of memory to run effectively.

When you’re choosing memory for your deep learning PC, you should look for two things: capacity and speed. For capacity, you’ll want to choose a memory option that gives you at least 16GB of RAM. For speed, you’ll want to choose a memory option that runs at 3200MHz or faster.

There are a few different ways to get the right mix of capacity and speed for your deep learning PC. One option is to buy two 8GB RAM sticks and overclocking them to 3200MHz. Another option is to buy one 16GB RAM stick and overclocking it to 3200MHz. And finally, you could also just buy two 16GB RAM sticks and leave them at their default speeds.

Whichever route you choose, just make sure that you have at least 16GB of fast RAM in your deep learning PC.

Step 5: Choose Your Storage

The final component you need to consider when building your deep learning PC is storage. You have two main options when it comes to storage: HDD (hard disk drive) and SSD (solid-state drive). Both have their pros and cons, but for deep learning PCs, we recommend using SSDs.

HDDs are cheaper and have more storage capacity than SSDs, but they’re also slower. For deep learning tasks that require a lot of data processing, the speed of an SSD will be a big advantage. SSDs are also more reliable than HDDs because they have no moving parts, which means there’s less that can go wrong.

If you’re on a budget, you can use a combination of both HDD and SSD storage. For example, you could use an HDD for storing your deep learning data sets and models, and an SSD for your operating system and other software applications.

Step 6: Choose Your Power Supply

Now that you’ve decided on the hardware for your deep learning PC, it’s time to pick a power supply. A good quality power supply is important for any PC, but it’s especially important for a deep learning PC since you’ll be running resource-intensive training tasks on your GPU(s).

The first thing to consider when choosing a power supply is its wattage rating. The wattage rating tells you how much power the power supply can deliver. A higher wattage rating means that the power supply can deliver more power, which is important if you’re planning on adding additional GPUs or other high-power components to your system in the future.

In general, a 750W power supply should be enough for most deep learning PCs. If you’re planning on using multiple GPUs or other high-power components, you may want to consider a higher wattage power supply.

Once you’ve decided on the wattage rating of your power supply, the next thing to consider is its efficiency. Power supplies come in different efficiency ratings, with higher efficiency ratings meaning that more of the power drawn from the outlet is converted into usable power for your PC.

Efficiency is important because it determines how much heat your power supply will generate. A more efficient power supply will generate less heat and thus be cooler and quieter than a less efficient one. For deep learning PCs, where heat generation is already a concern, it’s best to choose a highly efficient power supply.

Power supplies are typically rated using one of two efficiency standards: 80 PLUS or 90 PLUS. Power supplies that meet either of these standards are considered highly efficient and are a good choice for deep learning PCs.

Step 7: Choose Your Case

Now that you’ve made all your decisions on which parts to buy for your new deep learning PC, it’s time to pick a case. There are many different factors to consider when choosing a case including size, airflow, and expandability. In this post, we’ll help you choose the best case for your needs.

Size
The first factor to consider is size. Depending on the number of GPUs and other components you’re planning on using, you’ll need a different size case. If you only plan on using one or two GPUs, a smaller case will be sufficient. If you’re planning on using four or more GPUs, you’ll need a larger case.

Airflow
Another important factor is airflow. You want to make sure your case has good airflow so that your components don’t overheat. Look for a case with plenty of fans and good ventilation.

Expandability
If you think you might want to add more components in the future, it’s important to choose a case that is expandable. Look for a case that has room for additional drives and expansion cards.

Once you’ve considered all of these factors, it’s time to start shopping! We’ve compiled a list of our favorite cases for deep learning PCs below.

Step 8: Assemble Your PC

Now that you have all the components you need, it’s time to assemble your PC. If you’ve never done this before, don’t worry – it’s not as difficult as it may seem. Just follow the instructions below, and you’ll have your machine up and running in no time.

1. Start by putting the motherboard on top of the case. If your case has a CPU cutout, line it up with the socket on the motherboard.

2. Next, install the CPU into the socket. Make sure that it is properly seated and locked in place.

3. Install the RAM modules into their slots on the motherboard. Make sure that they are properly seated and locked in place.

4. Install the hard drive(s) into their slots in the case. Make sure that they are properly secured in place.

5. Install the optical drive(s) into their slot(s) in the case (if applicable). Make sure that they are properly secured in place.

6. Connect all of the cables to their appropriate ports on the motherboard and/or devices (CPU cooler, etc.). Refer to your motherboard manual for specific instructions on where to connect each cable.
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7、将电源连接到主板上的相应端口,并将电源线插入电源插座。

8、安装显示器并将显示器连接到主板的显示端口(通常是 HDMI 或 DisplayPort)。

9、现在,你可以启动电脑并开始安装你选择的作业系统。

Keyword: How to Build a Deep Learning PC in 2020

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