深度學習數據集制作工作

My Home Setup我的家庭設置

Creating my workstation has been a dream for me, if nothing else.

創建工作站對我來說是一個夢想，即使沒有其他選擇。

I knew the process involved, yet I somehow never got to it. It might have been time or money. Mostly Money.

我知道所涉及的過程，但是我莫名其妙地從不進行。 可能是時間或金錢。 主要是金錢。

But this time I just had to do it. I was just fed up with setting up a server on AWS for any small personal project and fiddling with all the installations. Or I had to work on Google Collab notebooks, which have a lot of limitations on running times and network connections. So, I found out some time to create a Deep Learning Rig with some assistance from NVIDIA folks.

但是這次我只需要這樣做。 我剛受夠在AWS上為任何小型個人項目設置服務器并擺弄所有安裝。 或者，我必須使用Google Collab筆記本，因為筆記本在運行時間和網絡連接方面有很多限制。 因此，我花了一些時間在NVIDIA員工的幫助下創建深度學習裝備。

The whole process involved a lot of reading up and watching a lot of Youtube videos from Linus Tech Tips. And as it was the first time I was assembling a computer from scratch, it was sort of special too.

整個過程涉及大量閱讀和觀看Linus Tech Tips的許多Youtube視頻。 這是我第一次從頭開始組裝計算機，這也很特別。

Building the DL rig as per your requirements takes up a lot of research. I researched on individual parts, their performance, reviews, and even the aesthetics.

根據您的要求構建DL裝備需要大量的研究。 我研究了各個部分，它們的性能，評論，甚至美學。

Now, most of the workstation builds I researched were focussed on gaming, so I thought of putting down a Deep Learning Rig Spec as well.

現在，我研究的大多數工作站版本都集中在游戲上，因此我也考慮制定《深度學習裝備規范》。

I will try to put all the components I used along with the reasons why I went with those particular parts as well.

我將嘗試列出我使用的所有組件以及為什么要使用這些特定部分。

If building your own seems too difficult or expensive, Exxact has a great line of deep learning workstations and servers starting at $5,899, with a couple of NVIDIA RTX 2080 Ti GPUs, Intel Core i9, 3-year warranty, and a deep learning software stack.

如果構建自己的產品似乎太困難或太昂貴，Exxact擁有大量的深度學習工作站和服務器，起價為$ 5,899，配備了兩個NVIDIA RTX 2080 Ti GPU，Intel Core i9、3年保修和深度學習軟件堆棧。

Also, if you want to see how I set up the Deep Learning libraries after setting up the system to use Ubuntu 18.04, you can view this definitive guide for Setting up a Deep Learning Workstation.

此外，如果要在設置系統以使用Ubuntu 18.04之后查看我如何設置深度學習庫，則可以查看此權威指南，以設置深度學習工作站 。

那么為什么需要工作站呢？ (So why the need for a workstation?)

The very first answer that comes to my mind is, why not?

我想到的第一個答案是，為什么不呢？

I work a lot on deep learning and machine learning applications, and it always has been such a massive headache to churn up a new server and installing all the dependencies every time I start to work on a new project.

我在深度學習和機器學習應用程序上做了大量工作，每次我開始從事新項目時，總是要組裝一個新服務器并安裝所有依賴項，這一直是一個巨大的難題。

Also, it looks great, sits on your desk, is available all the time, and is open to significant customization as per your requirements.

此外，它看起來很棒，可以坐在您的辦公桌上，隨時可用，并且可以根據您的要求進行重大定制。

Adding to this the financial aspects of using the GCP or AWS, and I was pretty much sold on the idea of building my rig.

再加上使用GCP或AWS的財務方面，就建造鉆機的想法我幾乎被甩了。

我的構建 (My Build)

It took me a couple of weeks to come up with the final build.

我花了幾個星期才得出最終版本。

I knew from the start that I want to have a lot of computing power and also something that would be upgradable in the coming years. Currently, my main priorities were to get a system that could support two NVIDIA RTX Titan cards with NVLink. That would allow me to have 48GB GPU memory at my disposal. Simply awesome.

從一開始，我就知道我希望擁有強大的計算能力，并且希望在未來幾年內能夠對其進行升級。 目前，我的主要任務是獲得一個系統，該系統可以支持兩塊帶有NVLink的NVIDIA RTX Titan卡。 這樣一來，我就可以擁有48GB的GPU內存。 就是棒。

PS: The below build might not be the best build, and there may be cheaper alternatives present, but I know for sure that it is the build with the minimal future headache. So I went with it. I also contacted Nvidia to get a lot of suggestions about this particular build and only went forward after they approved of it.

PS： 以下版本可能不是最佳版本，并且可能存在更便宜的替代方案，但我可以肯定，這是將來頭痛最小的版本。 所以我同意了。 我還與Nvidia聯系，以獲取有關此特定版本的很多建議，直到他們批準后才繼續。

1. 英特爾i9 9920x 3.5 GHz 12核處理器 (1. Intel i9 9920x 3.5 GHz 12 core Processor)

Intel or AMD?英特爾還是AMD？

Yes, I went with an Intel processor and not an AMD one. My reason for this (though people may differ with me on this) is because Intel has more compatible and related software like Intel’s MKL, which benefits most of the Python libraries I use.

是的，我選擇了Intel處理器而不是AMD處理器。 我這樣做的原因(盡管人們對此可能有所不同)是因為英特爾擁有更多兼容和相關的軟件，例如英特爾的MKL，它使我使用的大多數Python庫受益。

Another and maybe a more important reason, at least for me, was that it was suggested by the people at NVIDIA to go for i9 if I wanted to have a dual RTX Titan configuration. Again zero headaches in the future.

至少對我來說，另一個也許是更重要的原因是，如果我想擁有雙RTX Titan配置，NVIDIA的人建議選擇i9。 將來再次零頭痛。

So why this particular one from the Intel range?

那么，為什么要選擇這種來自英特爾的產品呢？

I started with 9820X with its ten cores and 9980XE with 18 cores, but the latter stretched my budget a lot. I found that i9–9920X, with its 12 cores and 3.5 GHz processor, fit my budget just fine, and as it is always better to go for the mid-range solution, I went with it.

我開始與9820X具有其10核心和9980XE 18個內核，但后者伸出我的預算了不少。 我找到 i9-9920X ，其12個內核和3.5 GHz處理器，適合我的預算就好了，和它始終是更好地去為中檔的解決方案，我用它去。

Now a CPU is the component that decides a lot of other components you are going to end up using.

現在，CPU是決定您將要使用的許多其他組件的組件。

For example, if you choose an i9 9900X range of CPU, you will have to select an X299 motherboard, or if you are going to use an AMD Threadripper CPU, you will need an X399 Motherboard. So be mindful of choosing the right CPU and motherboard.

例如，如果您選擇i9 9900X系列的CPU，則必須選擇X299主板，或者如果您要使用AMD Threadripper CPU ，則需要X399主板。 因此，請注意選擇正確的CPU和主板。

2.微星X299 SLI PLUS ATX LGA2066主板 (2. MSI X299 SLI PLUS ATX LGA2066 Motherboard)

This one Fits the bill這符合要求

This was a particularly difficult choice. There are just too many options here. I wanted a Motherboard that could support at least 96GB RAM (again as per the specifications by the NVIDIA Folks for supporting 2 TITAN RTX GPUs). That meant that I had to have at least six slots if I were to use 16GB RAM Modules as 16x6=96. I got 8 in this one, so it is expandable till 128 GB RAM.

這是一個特別困難的選擇。 這里有太多選擇。 我想要一個能夠支持至少96GB RAM的主板(同樣按照NVIDIA Folks的規范，支持2個TITAN RTX GPU)。 這意味著如果要使用16GB RAM模塊作為16x6 = 96，則必須至少有六個插槽。 我有8個，因此它可以擴展到128 GB RAM。

I also wanted to be able to have 2 TB NVMe SSD in my system(in the future), and that meant I needed 2 M.2 ports, which this board has. Or else I would have to go for a much expensive 2TB Single NVMe SSD.

我還希望能夠在我的系統中擁有2 TB NVMe SSD(將來)，這意味著我需要2個M.2端口，該主板具有此端口。 否則，我將不得不購買價格昂貴的2TB Single NVMe SSD。

I looked into a lot of options, and based on the ATX Form factor, 4 PCI-E x16 slots, and the reasonable pricing of the board, I ended up choosing this one.

我看著很多的選擇，以及基于ATX板型，4條PCI-E x16插槽，和董事會的合理定價，我最終選擇了這一個 。

3. Noctua NH-D15 chromax.BLACK 82.52 CFM CPU散熱器 (3. Noctua NH-D15 chromax.BLACK 82.52 CFM CPU Cooler)

Airflow Monster氣流怪獸

Liquid cooling is in rage right now. And initially, I also wanted to go for an AIO cooler, i.e., liquid cooling.

液體冷卻現在很流行。 最初，我還想購買AIO冷卻器，即液體冷卻器。

But after talking to a couple of people at NVIDIA as well as scrouging through the internet forums on the pro and cons of both options, I realized that Air cooling is better suited to my needs. So I went for the Noctua NH-D15, which is one of the best Air coolers in the market. So, I went with the best air cooling instead of a mediocre water cooling. And this cooler is SILENT. More on this later.

但是，在與NVIDIA的幾個人進行了交談，并在互聯網論壇上討論了這兩種方案的利弊之后，我意識到風冷技術更適合我的需求。 因此，我購買了Noctua NH-D15 ，它是市場上最好的空氣冷卻器之一。 因此，我選擇了最佳的空氣冷卻系統，而不是普通的水冷系統。 而且這個散熱器很安靜。 稍后再詳細介紹。

4. Phanteks Enthoo Pro鋼化玻璃盒 (4. Phanteks Enthoo Pro Tempered Glass Case)

An excellent Big house for all the components所有組件的絕佳大房子

The next thing to think was a case that is going to be big enough to handle all these components and also be able to provide the required cooling. It was where I spent most of my time while researching.

接下來要考慮的情況是，這種情況的大小將足以容納所有這些組件，并能夠提供所需的冷卻。 在這里，我大部分時間都在研究上。

I mean, we are going to keep 2 TITAN RTX, 9920x CPU, 128 GB RAM. It’s going to be a hellish lot of heat in there.

我的意思是，我們將保留2個TITAN RTX，9920x CPU和128 GB RAM。 那里將會有很多地獄般的熱量。

Add to that the space requirements for the Noctua air cooler and the capability to add a lot of fans, and I was left with two options based on my poor aesthetic sense as well as the availability in my country. The options were — Corsair Air 540 ATX and the Phanteks Enthoo Pro Tempered Glass PH-ES614PTG_SWT.

再加上Noctua空氣冷卻器的空間要求以及增加很多風扇的能力，基于我的審美觀念不佳以及我所在的國家的可用性，我剩下了兩個選擇。 選項為— Corsair Air 540 ATX 和Phanteks Enthoo Pro鋼化玻璃PH-ES614PTG_SWT 。

Both of them are exceptional cases, but I went through with the Enthoo Pro as it is a more recently launched case and has a bigger form factor(Full Tower) offers options for more customizable build in the future too.

兩者都是例外情況，但是我使用了Enthoo Pro，因為這是一個較新推出的案例，并且具有更大的尺寸(“ Full Tower”)也為將來提供更多可自定義的版本提供了選擇。

5.具有3插槽NVLink的雙TITAN RTX (5. Dual TITAN RTX with 3 Slot NVLink)

The Main Ingredient for the recipe配方的主要成分

These 2 TITAN RTX are by far the most important and expensive part of the whole build. These alone take up 80% of the cost, but aren’t they awesome?

這兩個TITAN RTX 到目前為止，它是整個構建中最重要和最昂貴的部分。 僅這些一項就占了成本的80％，但是它們不是很棒嗎？

I wanted to have a high-performance GPU in my build, and the good folks at NVIDIA were generous enough to send me two of these to test out.

我想在自己的構建中使用高性能的GPU，而NVIDIA的好伙伴足夠慷慨地向我發送其中兩個進行測試。

I just love them. The design. The way they look in the build and the fact that they can be combined using a 3 Slot NVLink to provide 48 GB of GPU RAM effectively. Just awesome. If money is an issue, 2 x NVIDIA GeForce RTX 2080 Ti would also work fine as well. Only a problem will be that you might need smaller batch sizes training on RTX 2080 Ti, and in some cases, you might not be able to train large models as RTX2080Ti has 11GB RAM only. Also, you won’t be able to use NVLink, which combines the VRAM of multiple GPUs in TITANs.

我就是愛他們 該設計。 它們在構建中的外觀以及可以使用3插槽NVLink進行組合以有效提供48 GB GPU RAM的事實。 太棒了 如果有錢問題，那么2 x NVIDIA GeForce RTX 2080 Ti也可以正常工作。 唯一的問題是，您可能需要在RTX 2080 Ti上進行較小批量的培訓，并且在某些情況下，由于RTX2080Ti僅具有11GB RAM，因此您可能無法培訓大型模型。 而且，您將無法使用NVLink，它會在TITAN中組合多個GPU的VRAM。

6. Samsung 970 Evo Plus 1 TB NVME固態驅動器 (6. Samsung 970 Evo Plus 1 TB NVME Solid State Drive)

The Fastest? Storage Option最快的？ 儲存選項

What about storage? NVMe SSD, of course, and the Samsung Evo Plus is the unanimous and most popular winner in this SSD race.

那存儲呢？ 當然，NVMe SSD和Samsung Evo Plus在這場SSD競賽中是一致且最受歡迎的贏家。

I bought 1 of them till now, but as I have 2 M.2 ports in my motherboard, I will get total storage of 2TB SSD in the future.

到目前為止，我已經購買了其中的1個，但是由于我的主板上有2個M.2端口，將來我將獲得2TB SSD的總存儲量。

You can also get a couple of 2.5" SSD for more storage space.

您還可以購買幾個2.5英寸SSD，以獲取更多存儲空間。

7. Corsair Vengeance LPX 128GB(8x16GB)DDR4 3200 MHz (7. Corsair Vengeance LPX 128GB (8x16GB) DDR4 3200 MHz)

My first computer had 4 MB of RAM. Never Thought I would build a computer with 128 GB RAM.我的第一臺計算機有4 MB的RAM。 沒想到我會用128 GB RAM建立一臺計算機。

I wanted to have a minimum of 96GB RAM, as suggested by the NVIDIA team. So I said what the heck and went with the full 128 GB RAM without cheaping out.

根據NVIDIA團隊的建議，我希望至少有96GB的RAM。 因此，我說了什么，不帶便宜就配備了完整的128 GB RAM。

As you can see, these RAM sticks are not RGB lit, and that is a conscious decision as the Noctua Air Cooler doesn’t provide a lot of clearance for RAM Slots and the RGB ones had a slightly higher height. So keep that in mind. Also, I was never trying to go for an RGB Build anyway as I want to focus on those lit up TITANs in my build.

如您所見，這些RAM棒沒有RGB點亮，這是一個有意識的決定，因為Noctua空氣冷卻器不能為RAM插槽提供很多間隙，而RGB插槽的高度略高。 所以記住這一點。 另外，我從來沒有嘗試過進行RGB構建，因為我想專注于構建中那些照亮的TITAN。

8. 海盜船1200W電源 (8. Corsair 1200W Power Supply)

The Powerhouse強國

A 1200W power supply is a pretty big one, but that is needed realizing that the estimated wattage of our components at full wattage is going to be ~965W.

1200W電源是一個很大的電源，但要意識到我們的組件在全瓦時的估計瓦數將約為965W，這是必需的。

I had a couple of options for the power supply from other manufacturers also but went with this one because of Corsair’s name. I would have gone with HX1200i, but it was not available, and AX1200i was much more expensive than this one at my location. But both of them are excellent options apart from this one.

我也有其他制造商提供的幾種電源選擇，但由于海盜船的名字，我選擇了這種電源。 我本來會選擇HX1200i的 ，但它不可用，而AX1200i在我的位置比這本要貴得多。 但是，除了這一項以外，它們都是極好的選擇。

9.甚至更多的粉絲 (9. Even More Fans)

Silent Heat Sinks靜音散熱器

The Phanteks case comes up with three fans, but I was recommended to upgrade the intake, and exhaust fans of the case to BeQuiet BL071 PWM Fans as Dual Titans can put out a lot of heat. I have noticed that the temperature of my room is almost 2–3 degrees higher than the outside temperature, as I generally keep the machine on.

Phanteks機箱帶有三個風扇，但建議我升級進氣口，并將機箱的排氣風扇升級為BeQuiet BL071 PWM風扇，因為Dual Titans可以散發大量熱量。 我注意到我的房間溫度比室外溫度高出近2-3度，因為我通常會保持機器開機。

To get the best possible airflow, I bought 5 of these. I have put two at the top of the case along with a Phanteks case fan, 2 of them in the front, and one fan at the back of the case.

為了獲得最佳的氣流，我購買了其中的5個。 我在機箱頂部放置了兩個風扇，并在前面放置了一個Phanteks機箱風扇，在機箱前面放置了2個風扇，在機箱背面放置了一個風扇。

10.外圍設備 (10. Peripherals)

The Essentials — A cup of tea and those speakers要點-喝杯茶和那些演講者

This section is not necessary but wanted to put it in for completion.

本部分不是必需的，但希望將其放入完成。

Given all the power we have got, I didn’t want to cheap out on the peripherals. So I got myself an LG 27UK650 4k monitor for content creation, BenQ EX2780Q 1440p 144hz Gaming Monitor for a little bit of gaming, a Mechanical Cherry MX Red Corsair K68 Keyboard and a Corsair M65 Pro Mouse.

鑒于我們擁有的所有功能，我不想廉價購買外圍設備。 因此，我得到了一個用于內容創建的LG 27UK650 4k顯示器，一個用于游戲的BenQ EX2780Q 1440p 144hz游戲顯示器，一個機械Cherry MX Red Corsair K68鍵盤和一個Corsair M65 Pro鼠標。

And my build is complete.

我的構建完成了。

定價💰💰💰 (Pricing 💰💰💰)

I will put the price as per the PCPartPicker site as I have gotten my components from different countries and sources. You can also check the part list at the PCPartPicker site: https://pcpartpicker.com/list/zLVjZf

我將按照PCPartPicker站點的價格進行定價，因為我從不同的國家和來源獲得了組件。 您也可以在PCPartPicker站點上檢查零件列表： https ://pcpartpicker.com/list/zLVjZf

It’s fricking expensive太昂貴了

As you can see, this is pretty expensive by any means (even after getting the GPUs from NVIDIA), but that is the price you pay for certain afflictions, I guess.

如您所見，無論如何，這都是相當昂貴的(即使是從NVIDIA獲得GPU之后)，但是我猜這就是您為某些痛苦所付出的代價。

最后 (Finally)

The End Result justifies the effort最終結果證明了努力的合理性

In this post, I talked about all the parts you are going to need to assemble your deep learning rig and my reasons for getting these in particular.

在這篇文章中，我討論了組裝深度學習裝備所需的所有部分，以及我特別希望獲得這些部分的原因。

You might try to look out for better components or a different design, but this one has been working pretty well for me for quite some time now, and is it fast.

您可能會嘗試尋找更好的組件或其他設計，但是這個組件對我來說已經運行了相當長的一段時間了，而且速度很快。

If you want to see how I set up the Deep Learning libraries after setting up the system with these components, you can view this definitive guide for Setting up a Deep Learning Workstation with Ubuntu 18.04

如果要在使用這些組件設置系統后查看我如何設置深度學習庫，可以查看此權威指南， 以了解如何使用Ubuntu 18.04 設置深度學習工作站 。

Let me know what you think in the comments.

讓我知道您在評論中的想法。

Thanks for the read. I am going to be writing more beginner-friendly posts in the future too. Follow me up at Medium or Subscribe to my blog to be informed about them. As always, I welcome feedback and constructive criticism and can be reached on Twitter @mlwhiz

感謝您的閱讀。 我將來也會寫更多對初學者友好的文章。 在Medium上關注我，或訂閱我的博客以了解有關它們的信息。 與往常一樣，我歡迎您提供反饋和建設性的批評，可以在Twitter @mlwhiz上與他們聯系。

翻譯自: https://towardsdatascience.com/creating-my-first-deep-learning-data-science-workstation-bd39c2f687e2

深度學習數據集制作工作

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