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3 Recommended PCs for machine learning / deep learning | Explain the necessary parts!

Table of contents

  • What is machine learning
  • What is deep learning
  • Parts required for deep learning PC
    • OS
    • CPU
    • GPUs
    • Memory
    • Storage
  • Differences between deep learning PCs and ordinary PCs
    • Normal PC
    • Homemade PC
    • Workstation/PC for deep learning
    • Supplement: Server
  • Should I make my own PC for deep learning?
    • Advantages of using your own PC
    • Points to note when making your own
  • 3 Recommended PCs for Deep Learning
    • ①DEEP-17FG102-i7K-VOXVI
    • ②THIRDWAVE Pro WORKSTATION X4612 standard model
    • ③HP ZBook Studio 15.6inch G8 Mobile Workstation New standard model
  • Summary

What is machine learning

Machine learning is one of the most important aspects of AI development, and is an effective method for predicting numerical values ​​and identifying and classifying images.

One of the machine learning methods is ” neural network . A neural network is a technique inspired by the structure of the human brain and mimics the way neurons work.

Some of the neural networks include multi-layer perceptrons and deep learning.

What is deep learning?

Deep learning was developed to enhance the capabilities of the neural network mentioned in the previous section.

Deep learning is a neural network with a multi-layered structure, and is currently the mainstream of AI development.

The difference between machine learning and deep learning is that machine learning learns rules from data by itself. In deep learning, the computer itself learns the feature values ​​that must be specified in machine learning.

To put it simply, machine learning involves specifying feature values ​​by humans, and deep learning involves learning feature values ​​as well.

Currently, deep learning is used for image recognition , speech processing, natural language processing , etc. a variety of situations around the world, including

From here, let’s see what kind of PC should be used when actually performing deep learning.

Parts required for deep learning PC

The following five parts are required for a PC for deep learning.

  1. OS
  2. CPU
  3. GPUs
  4. Memory
  5. Storage

I will explain each.

OS

OS is an abbreviation of “Operating system”, and refers to the system software that controls the operation (operation, operation, and operation) of a computer. In terms of a PC, it is a system that connects the device and software of the PC and controls the device and software.

Major PC OSs include Microsoft’s Windows, Apple’s mac OS, and open source Linux.

In AI development, Windows is recommended because it is easy to expand functions, and Linux is also used for servers. For Windows, the pro series is better from the point of view of functions

CPU

CPU is an abbreviation for “Central Processing Unit” and is the central processing unit in a computer (the brain of the computer, so to speak). CPUs are versatile in their processing and can handle a variety of things.

When choosing a PC for deep learning, it is a good idea to choose a higher model from Intel’s CPU “core i5”.

GPUs

GPU is an abbreviation for “Graphics Processing Unit”, and is a computing device specialized for screen display and image processing such as 3D graphics.

GPUs are good at simple calculations and good at parallel processing, so they are a very important part in AI development.

GPU processing speed is several to 100 times faster than CPU processing speed, and GPU is essential for deep learning.

Memory

Memory is the temporary storage of your computer’s work. Since it is temporary, it is characterized by fast access so that the current contents can be retrieved immediately.

When choosing a PC for deep learning, it is a good idea to choose a memory of 16GB or more.

Storage

Storage, also known as “auxiliary storage”, stores data for a long period of time. What is called a hard disk or SSD is one of this storage.

There is no problem with the storage that is installed in a normal PC, but if it is 512 GB or more, it can handle large amounts of data, so you can rest assured.

Differences between deep learning PCs and ordinary PCs

There are three differences between deep learning PCs and ordinary PCs: differences in specifications for each part,''using Linux as the OS,” and “requiring a GPU.”

In addition, PCs for deep learning are a type of workstation, and feature higher performance than regular PCs.

Also, some people who are serious about deep learning development use a PC that they have assembled with the necessary parts themselves.

In the following, I will briefly introduce “ordinary PC”, “self-made PC”, and “workstation/deep learning PC” as a supplement.

Normal PC

Deep learning can be performed even on a PC that is normally sold if it is equipped with a GPU.

For those who are studying deep learning for the first time or who want to try deep learning, a normal PC may be fine.

Homemade PC

If you want to do full-scale AI development, you should use your own PC. We also recommend the BTO PC, which allows you to select parts to some extent.

BTO: An abbreviation for “Build To Order”, which means build-to-order manufacturing. Compared to commercially available finished PCs, you can freely customize the processor, memory, hard disk, mouse, storage, etc.

Workstation/PC for deep learning

Workstations are used by individuals for work such as CAD. If you find it difficult, remember that it is a version with good performance on a normal PC.

* CAD: Design support software for automobiles, architecture, and clothing.

Supplement: Server

In addition to the above three methods, there are other ways to develop on the server. A server is used by many users. For personal use, you should choose one of the above three options.

Should I make my own PC for deep learning?

Earlier, I mentioned that “Some people who are serious about deep learning development use their own PCs.”

Below, we will introduce the advantages and cautions of using a self-made PC for those who are wondering whether they should build their own PC for deep learning.

Advantages of using your own PC

The advantage of using a self-made PC is that it can be specialized for deep learning and machine learning.

Homemade PCs can be assembled to have higher specs than those sold at regular stores, so it is recommended when a server cannot be used.

Points to note when making your own

One thing to keep in mind when building your own PC for deep learning is that you cannot request guarantees or repairs from the sales company.

It goes without saying that you build your own PC, but basically if something goes wrong, you have to investigate and deal with it yourself, or pay a certain amount of money and ask for a PC repair.

Therefore, if you are not very familiar with PCs and machines, you need to be careful when building your own PC.

3 Recommended PCs for Deep Learning

From here, we will introduce recommended PCs for deep learning. The following three PCs are introduced this time.

  1. DEEP-17FG102-i7K-VOXVI
  2. THIRDWAVE Pro WORKSTATION X4612 standard model
  3. HP ZBook Studio 15.6inch G8 Mobile Workstation new standard model

①DEEP-17FG102-i7K-VOXVI

machine learning

Quote: PC Kobo

The first recommended PC is “DEEP-17FG102-i7K-VOXVI”.

OS Ubuntu 18.04 LTS
CPU Core i7-9700K Intel Z370
memory DDR4-2400 SODIMM (PC4-19200) 16GB (8GB x 2)
Storage ① 250GB NVMe M.2 SSD
Storage② 1TB Serial-ATA HDD
drive No optical drive
GPUs GeForce RTX 2080 8GB GDDR6
display 17.3 type (matte color liquid crystal) full HD (1920 x 1080 dots)
price 32,3980 yen ~ (as of 2022/02/08)

It supports 8GB of high-speed GDDR6 memory similar to desktop and GPU Boost 4.0 that brings out GPU performance, so code created at the development site can be executed at a speed comparable to mobile environments.

Although this PC is a notebook PC, it boasts performance comparable to that of a desktop PC. It is one that can be used at the forefront of AI development, such as creating sample code, demonstrating, and giving presentations.

In addition, the same PC comes with ax Co., Ltd.’s demo software “ailia AI showcase”, so you can use various AI functions using trained models such as object detection, image classification, feature extraction, skeleton extraction, and personal identification. You can easily try it.

In addition, it supports the GPU Cloud platform “NGC (NVIDIA GPU Cloud)” that facilitates AI development, and the latest development environment can be used without complicated settings.

Just by downloading the deep learning framework, you can use it without worrying about complicated environment settings and consistency, so it is the best laptop for those who are just starting deep learning.

A framework is a piece of software that serves as the foundation upon which an application is developed.

②THIRDWAVE Pro WORKSTATION X4612 standard model

Quote: dospara.co.jp

The second recommended PC is “THIRDWAVE Pro WORKSTATION X4612 standard model”.

OS none
CPU Intel Xeon Silver 4210R (rated 2.40GHz/3.20GHz/13.75MB/10Core/20Thread at TB) x2
memory 32GB (DDR4-3200 ECC RDIMM/16GB×2)
storage No disc (2.5″ rear bay 1)
GPUs NVIDIA T600 4GB (MiniDisplayPort x4) x 1 [Order]
price Price starts at 72,8860 yen (as of 02/08/2022)

The THIRDWAVE Pro WORKSTATION X4612 standard model is a high-end model that achieves expandability and powerful performance. *Since there is no OS, you will have to choose by yourself.

Up to two NVIDIA® Quadro® and NVIDIA® GeForce® series ultra-high-end graphics cards can be installed.

In addition, assuming use on the desk side, it can be operated with a commercial 100V power supply, and can be used for various purposes such as high-resolution video/audio editing, deep learning , CAE/CAD, and 3D animation. .

③HP ZBook Studio 15.6inch G8 Mobile Workstation New standard model

Quote: Japan HP

The third recommended PC is “HP ZBook Studio 15.6inch G8 Mobile Workstation New Standard Model”.

OS Windows 10 Pro (Japanese) (Downgrade from Windows 11 Pro)
CPU Intel® Core™ i7-11800H processor (max frequency 4.6GHz, 8 cores/16 threads, 24MB cache)
memory 16GB DDR4-3200 (onboard)
storage 512GB M.2 SSD (PCIe, NVMe, SED OPAL2, TLC)
GPUs Intel® UHD Graphics and NVIDIA T1200 (4 GB GDDR6)
display 15.6 inch wide full HD liquid crystal display (matte panel, maximum resolution 1920 x 1080, maximum brightness 400cd/m², maximum 16.77 million colors, IPS method, LED backlight, ambient light sensor)
price Price starts from 35,2000 yen (as of 02/08/2022)

The next-generation Intel® Core™ i9 vPro® processor in the PC is designed to handle complex multi-threaded applications, making multitasking easy.

The HP ZBook Studio 15.6inch G8 Mobile Workstation New Standard Model is a laptop designed for performance workflows in every aspect, from keyboard to screen.

Also, up to NVIDIA RTX™ A5000 or GeForce RTX™ 3080 GPUs can be installed. So you can seamlessly render, design and multitask even with heavy files.

And with NVIDIA RTX™ professional graphics, the PC can query millions of rows of data sets and analyze them in real time, making it the perfect PC for data scientists and business intelligence professionals.

Summary

How was it?

This time, I explained what deep learning is and the difference between a PC for deep learning and a normal PC.

A PC has various parts and I think it is difficult, but I would like you to acquire knowledge by all means.

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