My $1000 Deep Learning Box

For machine learning enthusiastic, it is always good to have their own box to do deep learning number crunching. The easiest way is probably to purchase a desktop from Dell, HP or other vendors, and then add a GPU on top. I have considered this option but eventually gave it up. One reason for abandoning this idea is that it is hard to customize a commercially available desktop. For instance, if you want a 750W power supply to ensure GPU won't be short of power or need a motherboard which can support two GPUs, it is not easy to find such a desktop in the market with acceptable price. There are other reasons as well. I want to use a Linux computer dedicated for machine learning purpose but most of the computers available in the market come with Window OS. Therefore, after some contemplating, I decide to assemble my own computer, for which I have not done before.

Hardware

After searching in Internet, I found lots of useful information. In particular, I took good reference from the computer component list provided in this blog. But I also made some modifications on top of Yanda's list. Here is the list of components I am using:

1. EVGA GeForce GTX 1060 SC GAMING, ACX 2.0 (Single Fan), 6GB GDDR5, DX12 OSD Support (PXOC), 06G-P4-6163-KR   (A GPU not as fancy as GTX1080. But it is a fair choice for personal usage and can be upgraded later. $278)

2. ASUS ROG Strix Z370-G Gaming LGA1151 (Intel 8th Gen) DDR4 DP HDMI M.2 Z370 Micro ATX Motherboard with onboard 802.11ac WiFi, Gigabit LAN and USB 3.1 (Motherboard recommended by Yanda. This board can support up to 2 GPUs, which means there is room to expand. $189)

3. WD Blue 3D NAND 500GB PC SSD - SATA III 6 Gb/s M.2 2280 Solid State Drive - WDS500G2B0B (this 500GB SDD drive is also recommended by Yanda. I like it since this SDD drive can be embedded into the Z370-G motherboard. $95)

4. Corsair Vengeance LPX 16GB (2x8GB) DDR4 DRAM 3200MHz C16 Desktop Memory Kit - Black (CMK16GX4M2B3200C16) (16GM DRAM, 170$)

5. Intel 8th Gen Core i5-8400 Processor (For budget purpose, I did not purchase i7 but instead settled with an 8th gen i5 processor, $180)

6. EVGA Supernova 750 G3, 80 Plus Gold 750W, Fully Modular, Eco Mode with New HDB Fan, 10 Year Warranty, Includes Power ON Self Tester, Compact 150mm Size, Power Supply 220-G3-0750-X1 (this 750W power supply is recommended by Amazon. It seems to be a quite popular choice and is so far so good for me. $97)

7. Thermaltake Versa H15 SPCC Micro ATX Mini Tower Computer Chassis CA-1D4-00S1NN-00 (This case is also recommended by Amazon. I think any case with good reviews and supports microATX standard should do the job. $41)

All seven components add to $1050. If one chooses better CPU (like i7), better GPU (like GTX1080), or more than one GPU, the budget for this box will increase. But it also shows the benefit of DIY since it is fairly easy to tune the computer setting according to your own budget and needs.

I assembled everything myself. Even though it is the first time I did that, the whole process seems not to be that difficult. And my computer already starts running and it seems that I have not blew up anything. However, you want to read the manual especially the motherboard manual carefully before starting. Youtube education videos can also be quite benefitial.

Software

I installed Ubuntu Linux in my computer. Ubuntu website provides a good tutorial of how to create bootable USB stick for Ubuntu. I follow up that direction to install a Ubuntu 18.04 and everything works fine. During my installation process, I did not allow installing 3rd software out of the concern that it can mess up with Nvidia GPU installation done later.

The trickiest thing during the whole procedure of box assembly is installing CUDA. CUDA support for Linux is far from perfect. As of the time I wrote this blog, CUDA website only support Ubuntu 17 and 16, but not Ubuntu 18. Internet search returns lots of results of how to install CUDA in Ubuntu yet many of them are confusing especially for a Ubuntu newbie like me. For example, I had followed some recommendation to turn off X-server and it ends up with endless black screen. What eventually saves me is this blog.  The author lays out a relatively straightforward path to install CUDA in Ubuntu 18.04 and most importantly, it works! The only thing missing in that blog is how to install Nvidia driver version 390. For Nvidia driver installation, this discussion tells how to do it. It shows two ways and below is what I did. After driver installation done, remember to use nvidia-smi to verify the installation.

$ sudo add-apt-repository ppa:graphics-drivers/ppa
$ sudo apt update

$ sudo apt install nvidia-390

After installing CUDA, CUDNN, Tensorflow, you are still a few steps away from running your first deep learning code in the box. For example, you may want to install keras and other Python packages. However, these steps are straightforward. Finally, you should observe a keras or tensorflow sample code running on GPU and it calls a good end to your day.