Cheapest Deep Learning PC in 2020

Key Takeaways

mr. president the corona virus is spreading rapidly do we have a cure yet no sir Johnson send in the deep learners hello world it's Suraj in every single week there's some new state-of-the-art advanced in the capabilities of computers due to deep learning technology which can be summed up as neural networks trained on big datasets using GPUs to make predictions but not everyone can afford the expensive GPU stacks that the biggest companies like Google and Amazon used to train their own models so I wondered what's the cheapest deep learning PC that could possibly be built in 2020 after doing some research and weighing various component options I managed to compile a brand new deep learning PC build that comes out to an unbelievable 444 dollars and ninety one cents in this episode I'm gonna show you step-by-step how to build your own affordable deep learning computer both the hardware and the software and test out its GPU acceleration capability by training pi torches standard image classifier example on a data set which contains 60,000 images of everyday objects getting both the hardware and software set up isn't as complicated as you might think in fact it can all be done in a few hours but before we get to that let me describe to you what the building blocks of deep learning actually are at the core of it all we need energy in the form of electricity to power computations these computations run on Nvidia's GPUs which have dozens more cores than CPUs and were originally designed to speed up gaming algorithms like ray tracing but it turns out this also allows them to run many neural network operations simultaneously specifically matrix math operations resulting in massive speed ups this GPU enabled computer needs to have an operating system to support basic system functions Windows Linux OS X and Skynet are all compatible and the operating system needs to have a C language compiler AC compiler is a program that converts human readable C code into a machine readable language so that a computer can execute it the popular deep learning libraries all have Python api's but under the hood they pretty much all leverage C to handle more complex tasks like efficient memory allocation and threading C compilers are native to Mac and Linux called clang and GCC respectively for Windows it needs to be downloaded in the form of Visual Studio but while C is a wonderful language it wasn't specifically designed to leverage the full parallel processing capabilities of GPU architecture and that's why CUDA is needed CUDA is a GPU specific platform it's a collection of compilation tools a programming model and architecture all built on top of the C language CUDA optimized C code is what truly gives deep neural networks their state of the art results hey do you want to go out sometime I'm a Crudup professional what we do that was easy and NVIDIA has already built CUDA libraries for a variety of tasks for neural network training there's si UD n n for inference there's tensor RT for computer vision there's vision works for distributed GPUs there's qu + built on top of these CUDA libraries are our beloved Python frameworks designed to make building and training neural networks simple that includes pi torch tensorflow MX net and l TK and others using the stack we can download a data set then train a model on it now let's get to talking hardware first of all there are some great DIY cheap gaming computer tutorial videos right here on youtube and some are even cheaper than the one that i've compiled but a gaming computer is not the same as a deep learning computer and that's for two reasons one we specifically need an NVIDIA GPU that's CUDA enabled the cheaper AMD GPUs they list won't work for deep learning and a lot of those builds either pull from refurbished part lists or the parts only shipped to the United States which keep costs low but I chose Newegg for parts because of their inclusive global shipping policy I've had my own deep learning computer for about two years now and it cost me sixteen hundred dollars worth of parts at the time the case is a corsair 750d ATX the processor is an Intel Core i7 it's got 16 gigs of RAM one terabyte of hard disk space to store all my pron just kidding an asus prime z 370 motherboard and in 1080 TI g-force GPU but not gonna recommend you build that because it's been 2 years since then and components are now cheaper I built the list of parts around the most affordable CUDA enabled GPU I could find the gtx 1650 just last year nvidia introduced it as their most affordable GPU yet designed to play 1080p games its predecessor was the gtx 1050 and nvidia said the 1650 was 70% faster its architecture named turing after the og himself who's probably off solving p equals in the afterlife right now uses a technology called tensor cores to accelerate deep learning inference tensor cores each perform a fused matrix multiply add operation one thing to note about the 1650 though is that it's only got four gigs of RAM what that means is that it will be easy to run into memory issues while training large models on big datasets if the predefined batch size is too large thus it will be necessary to train in smaller batches to avoid that the trade-off is that the smaller the batch size the longer the training time but that's the benefit of having your own machine besides it being cheaper you can also leave it training as long as you want Google colab a cloud GPU training tool is amazing but it has an absolute timeout of 12 hours for the RAM I chose this south-korean memory supplier company called hynek's because because is amazing oh my god no but also because it offers eight gigs of memory for only $27 the hard drive is a one terabyte Seagate for $70 the motherboard is an MSI pro a 320 M for about $60 MSI is a reputable brand which supports AMD CPUs which is what I chose the the AMD Rison 3 Series 4 core 3.1 gig CPU for $62 and to power it all I found a 450 watt corsair power supply which will provide more than enough power for this GPU priced at $50 lastly i chose a pc case with two fans built-in for cooling at $26 the whole thing comes out to just 444 dollars in 91 cents cheaper than a gold toilet seat and i really don't think we could get this price much lower the only option is that we could shave off the price of storage by using 512 gigs instead of a terabyte but then what's the point of deep learning without big data who are gonna want at least one terabyte to store any big data sets we come across Linus tech tips just released this amazing first-person POV PC build video which takes you through the process from start to finish and if you prefer a blog post tutorial Instructables has a 23 step tutorial on putting a PC together I'll link you to both and everything else I talked about in the video description and if you're new here hit subscribe I found a pre-built desktop PC by ABS for $600 that uses the same GPU the only downside besides the $150 price increase is the fact that it's storage space is only 512 gigs instead of a terabyte but it also comes with a keyboard and a mouse now that the hardware is out of the way let's move on to the funnest part the software setup process there are five steps here we have to first install the driver for our specific GPU the CUDA toolkit the CUDA deep neural network library an app called docker and a docker image that contains all the relevant deep learning libraries we need let's go through each step in order on my Windows machine we're going to first install our driver from the nvidia website by selecting the proper drop down options once installed we'll need to restart our system then we can install the CUDA toolkit on the Nvidia developer website we can see a link to download cuda for our specific operating system make sure to get the latest version it's going to be a local installer file an exe for Windows and a dmg for mac and run file for Linux of course so no need for terminal just yet it's a GUI also it'll ask us to install Visual Studio on Windows in order to use its C compiler after CUDA has been installed we'll move on to the CUDA deep neural network library yes I do this is Nvidia's library specifically made to accelerate neural network operations and it's used heavily by all the major deep learning frameworks under the hood to tune heavily use routines like forward and backward propagation we'll need to join the Nvidia Developer Program to download it but at least it's free to join once downloaded we'll need to extract it and copy it to the CUDA toolkit rectory along with the other libraries in the kukuda toolkit now that we have our GPU environment set up we're going to install docker from the docker website using the downloadable installer docker is a container platform and containers are a software construct that packages up code and its dependencies into a single environment so that it can run quickly in any computing environment without you needing to reinstall any dependencies a huge lifesaver for us the reason we want to download docker is because I found this wonderful docker container by Petra Neto that has it all the latest version of Python PI torch jupiter notebooks and more well first download the repository then build the included docker file to install all of its associated dependencies from docker hub then once installed we'll run it using docker run lastly in a jupiter notebook we can choose a library let's say PI torch by importing it and test out our GPU integration by calling CUDA get device name if it returns the name of our GPU congratulations we successfully set up our environment now that we've done that let's run through the standard image classifier example by the PI torch team on the CF our dataset to train our model on our local machine using torch vision we can load up the c-4 dataset downloading it from the PI torch server we'll define a few labels then visualize some of the training images now we can define a convolutional Network these are the ones best suited for image classification problems once trained we can save our trained model for later use say in our own mobile app then test the network on the testing set to see if it classifies accurately the winner of last week's coding challenge is an SH David I'm just using alpha poll to try and visualize the coronavirus he's still working on it but at least he's working on it so good job hunch and that's it I hope to see you start building your own deep learning app soon and until next time happy learning