- #Deep learning workstation install
- #Deep learning workstation full
- #Deep learning workstation software
- #Deep learning workstation series
When it comes to a workstation, there are always chances of up-gradation.
#Deep learning workstation software
When going with a custom built one, you would have to give in to some software and hardware restrictions, whereas when you are putting together one by yourself, you are completely free to build it as you feel like. Buying a custom built workstation by a service provider would cost anywhere between 2 to 3 times higher what it would to putting together one yourself. It would cost you comparatively less if you buy the parts separately and assemble them yourself. There are always two sides of the same coin and building your own workstation to work on AI projects has its own ups and downs too. The case has been chosen as it is large enough to hold so many components and you can go with fancier cases as long as it is large enough for the components and the 4GPU SLI set. In case you see temperature hikes, you can get even better cooling units.
#Deep learning workstation install
Our unit supports easy expansion so you could add more memory modules as you need.Ĭooling units and cabinet- Although seemingly unimportant, running a 1500W machine has its own problems, and it is a must that you install cooling units separately for both the GPU and the CPU so that they are always in their optimum temperature. Hence we decided to go with the best in line Intel SSD with 4Gb of storage. Memory- Gone are the days of hard disks, and SSD is the new form of memory. Motherboard The motherboard has been decided after keeping in mind its support for.
Power Supply While I recommend the Corsair Ax1600i, you could actually go with any power supply unit that generates at least 1500W power since this beast of a workstation is power hungry and needs 1500W at its peak. You could leave a few memory slots empty as well since RAM up-gradation is simple and cost-effective. Depending on your needs and the type of datasets that you’d be handling, you could go for a 128GB, or a 256GB configuration too. That is the reason we went with the highest possible configuration of 128GB X 4. RAM Since many ML/DL based tasks are on images or videos, it is important to have enough memory to load such huge datasets. Workloads don’t always scale in the way one might expect with dual CPUs, and it is always better to use a single one with higher cores instead. In case you do need a dual CPU setup, it is recommended that you make two workstations instead. A dual CPU model does not boost performance but only takes care of tasks which need even more cores at the same time. It is also to be noted that our recommended GPU configuration of 4 V100s in SLI is also used by NVIDIA’s own custom workstation called the DGX STATION.ĬPU We chose a single CPU based model for our system since our computations will mainly run on the GPU itself, and a 20 core Intel Xeon processor with 40 threads are enough for any computation that might be CPU intensive. In case you are crazy about the specs sheet, let me tell you, this one comes with 640 tensor cores that deliver up to a humongous 125 teraflops of deep learning performance.
Its 32GB stick helps data scientists and ML engineers spend less time on each iteration of model changes so that they can focus more time on changing the model and running it again so as to make better breakthroughs in AI. NVIDIA Tesla V100 is the latest and most advanced data-centre GPU ever to be built by NVIDIA. GPU Let’s talk about the most important unit of the system and why we chose it.
Several things were taken into account while choosing the hardware configuration of this system.
#Deep learning workstation series
Memory- Intel SSD DC P4510 SERIES (4.0TB, 2.5in PCIe 3.1 x4, 3D2, TLC) Decisions while choosing the hardware
#Deep learning workstation full
Motherboard- Supermicro – X10SRA ATX LGA2011-3 MotherboardĬPU cooler- ASUS ROG Ryujin 360 RGB AIO Liquid CPU Cooler 360mm Radiator (Three 120mm 4-pin Noctua iPPC PWM Fans)Ĭabinet- Thermaltake Level 20 ATX Full Tower Case Power Supply- CORSAIR AX1600i, 1600 Watt, 80+ Titanium Certified, Fully Modular – Digital Power Supply GPU Cooling unit- ARCTIC Accelero Xtreme+ II VGA Cooler Processor- Intel Xeon E5-2698 v4 2.2 GHz with turbo-boost 3.60 GHz (20-Cores and 50 Mb Smart Cache) RAM- 4 X Supermicro – 128 GB Registered DDR4-2666 Memory GPU- 4 X NVIDIA Tesla V100 Volta GPU Accelerator 32GB Graphics Card We are calling our workstation “the beast” because of its immense computation capabilities. Getting it built for you by a service provider might end up being considerably costlier than assembling one yourself, and that is why we decided to deep dive into the modus operandi for building an ML/DL workstation in 2019.
While most “software engineers” get away with using a laptop, in case you want to build your in-house AI capabilities, it is a must for you to have a dedicated workstation. In a world that is being taken over by machine learning and deep learning algorithms, you do need faster machines to crunch the humongous data as well.
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